ORIGINAL_ARTICLE
Identifying and Prioritizing Humanitarian Supply Chain Practices to Supply Food before an Earthquake
Objective: The general concern about supplying food is one of the important issues before an earthquake happens. This research aims to present a conceptual model for “humanitarian supply chain practices to supply food before an earthquake” and prioritizing its practices.
Methods: This research is applied in nature and it is descriptive regarding the tools and instruments of data collection. After reviewing the related literature and interviewing some experts, 7 criteria and 19 sub-criteria were identified. Then the questionnaire was distributed among the related managers, experts, and academics and 281 questionnaires were completed and gathered. This measuring model was tested according to Structuring Equation Modeling. Furthermore, Fuzzy AHP was used to weight the importance of each practice and its dimensions.
Results: Our findings showed that “Monitoring”, “Education”, and “Readiness for logistics and distribution” are the most important dimensions of our conceptual model. So, we used one sample t-test to measure the performance of each practice.
Conclusion: Finally, importance-performance matrix was used for prioritizing the practices. We hope that our results will be a good guidance for managers and decision makers of humanitarian supply chain for better understanding of food supplying practices before an earthquake.
https://imj.ut.ac.ir/article_67515_79d386da546bcd9c4ad820e2bbe79331.pdf
2018-03-21
1
16
10.22059/imj.2018.234645.1007246
Humanitarian supply chain
Supplying food before earthquake
Confirmatory Factor Analysis
Fuzzy AHP
Importance-performance matrix
Rohollah
Ghasemi
ghasemir@ut.ac.ir
1
Ph.D. in Production and Operations Management, Faculty of Management, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Ali
Alidoosti
a_alidoost@yahoo.com
2
M.Sc. in Mechanical Engineering, Malek Ashtar University of Technology, Tehran, Iran
AUTHOR
Reza
Hosnavi
hosnavi@mut.ac.ir
3
Prof. in Systems and Productivity Department, Malek Ashtar University of Technology, Tehran, Iran
AUTHOR
Jaber
Norouzian Reykandeh
jaber.norouzian@ut.ac.ir
4
M.Sc. in Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
AUTHOR
سرمد، زهره؛ بازرگان، عباس؛ حجازی، الهه (1393). روشهای تحقیق در علوم رفتاری، تهران: نشر آگه.
1
سعدآبادی، علی اصغر؛ عظیمی، محدثه (1393). شناسایی اقدامات اساسی در مراحل مدیریت بحران به کمک روش فازی (مورد مطالعه: شناسایی اقدامات اساسی در مراحل مدیریت بحران زلزله)، فصلنامه ساختار و کارکرد شهری، 2 (6)، 31-54.
2
قاسمیان صاحبی، ایمن؛ نوروزیان ریکنده، جابر (1394). شناسایی و اولویتبندی عوامل کلیدی موفقیت در زنجیره تأمین بشردوستانه با استفاده از رویکرد مدلسازی ساختاری تفسیری. اولین کنفرانس بینالمللی مدیریت، اقتصاد، حسابداری و علوم تربیتی، ایران، 30 خرداد، ساری.
3
موسویان، سید ابوالحسن (1392) نقش حمل و نقل در مدیریت بحران و سوانح طبیعی. ششمین همایش فرامنطقهای پیشرفتهای نوین در علوم مهندسی، 24 اردیبهشت.
4
مؤمنی، منصور؛ فعال قیومی، علی (1391). تحلیلهای آماری با استفاده از SPSS. تهران: انتشارات کتاب نو.
5
نهفتی کهنه، جمال؛ تیموری، ابراهیم (1395). ارائه مدلی برای طراحی زنجیره تأمین فراوردههای خونی در زمان وقوع بحران زلزله با در نظر گرفتن انتقال از سایر استانها (مطالعه موردی: شبکه انتقال خون تهران). نشریه مدیریت صنعتی، 8(3)، 487-513.
6
References
7
Abidi, H., de Leeuw, S., & Klumpp, M. (2013). Measuring success in humanitarian supply chains. International Journal of Business and Management Invention, 2(8), 31-39.
8
Azzopardi, E., & Nash, R. (2013). A critical evaluation of importance–performance analysis. Tourism Management, 35, 222-233.
9
Balcik, B., Beamon, B. M., Krejci, C. C., Muramatsu, K. M., & Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126(1), 22-34.
10
Chandraprakaikul, W. (2010). Humanitarian supply chain management: literature review and future research. In The 2nd international conference on logistics and transport, Queenstown (Vol. 18).
11
Cozzolino, A. (2012). Humanitarian logistics and supply chain management. In Humanitarian Logistics (pp. 5-16). Springer Berlin Heidelberg.
12
Cozzolino, A., Rossi, S., & Conforti, A. (2012). Agile and lean principles in the humanitarian supply chain: the case of the United Nations world food programme. Journal of Humanitarian Logistics and Supply Chain Management, 2(1), 16-33.
13
Da Costa, S. R. A., Campos, V. B. G., & de Mello Bandeira, R. A. (2012). Supply chains in humanitarian operations: cases and analysis. Procedia-Social and Behavioral Sciences, 54, 598-607.
14
Ghasemian Sahebi, I., & Nourozian Reikandeh, J. (2015).Identifying and ranking of the success factors humanitarian supply chains through interpretive structural modeling (ISM). The First International Conference on Management, Economics and Education,Iran, Sari, 20, June, 2015. (in Persian)
15
Holguín-Veras, J., Jaller, M., Van Wassenhove, L. N., Pérez, N., & Wachtendorf, T. (2012). On the unique features of post-disaster humanitarian logistics. Journal of Operations Management, 30(7), 494-506.
16
Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management, 37(2), 99-114.
17
Kunz, N., & Reiner, G. (2012). A meta-analysis of humanitarian logistics research. Journal of Humanitarian Logistics and Supply Chain Management, 2(2), 116-147.
18
Momeni, M., & Ghayoumi, A. F. (2012). Statistical data analysis using SPSS (7th ed.). Tehran: Simaye Danesh. (in Persian)
19
Mousavian, S. A. (2013). Transportation role in crisis management and disaster. 6th Trans-Regional Conference On Advances In Engineering Sciences, 14, May, 2013. (in Persian)
20
Nahofti Kohneh, J. & Teimoury, E. (2016). A model for the design of blood products supply chain at the time of the earthquake disaster considering the transfer from the other provinces (Case Study: Tehran blood transfusion network). Industrial Management Journal, 8(3), 457-513.
21
Oloruntoba, R., & Gray, R. (2006). Humanitarian aid: an agile supply chain? Supply Chain Management: an international journal, 11(2), 115-120.
22
Saad Abadi, A. A., & Azimi, M. (2014). Identifying the Basic Actions in Phases of Disaster Management Using Fuzzy Technique. Journal of Shahr-ha, 2(6), 31-54. (in Persian)
23
Sarmad, Z., Bazargan, A., & Hejazi, E. (2014). Research Methods in the Behavioral Sciences (26th ed.). Tehran: Agah. (in Persian)
24
Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling. Psychology Press.
25
Sipahi, S., & Timor, M. (2010). The analytic hierarchy process and analytic network process: an overview of applications. Management Decision, 48(5), 775-808.
26
Thomas, A. S., & Kopczak, L. R. (2005). From logistics to supply chain management: the path forward in the humanitarian sector. Fritz Institute, 15, 1-15.
27
Tuncel, G., & Alpan, G. (2010). Risk assessment and management for supply chain networks: A case study. Computers in industry, 61(3), 250-259.
28
Van Heeringen, B. B. (2010). Risk management in regional humanitarian relief operations. Available in: https://dspace.ou.nl/bitstream/1820/3032/1/MWBBvHeeringenjan10.pdf.
29
Van Wassenhove, L. N. (2006). Humanitarian aid logistics: supply chain management in high geart. Journal of the Operational research Society, 57(5), 475-489.
30
ORIGINAL_ARTICLE
Efficiency Estimation using Nonlinear Influences of Time Lags in DEA Using Artificial Neural Networks
Objective: One of the common methods for the assessment of an organization's efficiency is comparison with other competitors. However, some researchers have studied the efficiency of a unit within itself during different periods of time and it is used to investigate the performance trend of the unit during previous times. The purpose of this research is to forecast the performance of a unit using the previous time series of its performance. Methods: This research conducts comparison and efficiency analysis of a unit during different time periods using SBM and DEA models. And then, the outcome is considered as the training elements of an ANN, so efficiency of future time steps can be estimated for that unit. Results: An industrial unit in Steel industry was studied in this research and its decreasing performance trend during ten years has been presented after efficiency measurements. Implementing different structures of ANNs, finally, we found out that a recurrent neural network with 10 neurons in a hidden layer and Bayesian Regularization algorithm had the best performance for future forecasting of efficiency. Conclusion: The most important achievement of this study is efficiency forecasting for organizations' future using the existing data with regards to the influences of previous time steps on current efficiency by a nonlinear approach. It would lead to providing a clear image of the organization's future as represented for the case of this paper.
https://imj.ut.ac.ir/article_67509_2680c7f27b770880ac4bb41fd79aa710.pdf
2018-03-21
17
34
10.22059/imj.2018.129192.1006898
Data Envelopment Analysis (DEA)
Artificial Neural Networks (ANN)
Performance analysis
Time series forecasting
SBM
Mostafa
Kazemi
kazemi@um.ac.ir
1
Prof. of Management, Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
Mohammadali
Faezirad
faezirad@gmail.com
2
Ph.D. Candidate in Management-Operational Research, Faculty of Economic and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
اجلی، مهدی؛ صفری، حسین (۱۳۹۰). ارزیابی عملکرد واحدهای تصمیمگیری با استفاده از مدل ترکیبی شبکههای عصبی پیشبینیکننده عملکرد و تحلیل پوششی دادهها (مورد مطالعه: شرکت ملی گاز ایران). نشریه مهندسی صنایع، ۴۵(۱)، 13-29. حجازی، رضوان؛ انواری رستمی، علی اصغر؛ مقدسی، مینا (۱۳۸۷). تحلیل بهرهوری کل بانک توسعه صادرات ایران و رشد بهرهوری شعب آن با استفاده از تحلیل پوششی دادهها (DEA). فصلنامه مدیریت صنعتی، ۱(۱)، 39-50. علی نژاد، علیرضا (۱۳۹۷). ارائه یک روش ترکیبی از مدل سروکوال و تحلیل پوششی داده در رتبهبندی کیفیت خدمات. فصلنامه مطالعات مدیریت صنعتی، ۱۶(۴۸)، 153- ۱۸۱. علیرضائی، محمدرضا؛ افشاریان، محسن؛ تسلیمی، وحید (۱۳۸۶). ارائه راهکارهای منطقی بهبود عملکرد شعب بانکها به کمک مدلهای تعمیمیافته تحلیل پوششی دادهها. پژوهشنامه اقتصادی، ۷(۴)، 263- ۲۸۳. کاظمی، مصطفی؛ منظم ابراهیمپور، شیلا؛ ایل بیگی، علیرضا (۱۳۹۲). بررسی کارایی نواحی مختلف شهرداری مشهد با رویکرد تحلیل پوششی دادهها. فصلنامه برنامهریزی شهری، ۴(۱۵)، 113- ۱۳۲. هیلیر، فردریک؛ لیبرمن، جرالد (۱۳۹۱). پیشبینی و مدیریت موجودیها (ترجمه محمدعلی فائضی راد و عطیه حقیقت). تهران: نشر ترمه. References Adhikari, R. (2015). A neural network based linear ensemble framework for time series forecasting. Neurocomputing, 157, 231-242. Ajalli, M. & Safari, H. (2011). Analysis of the Technical Efficiency of the Decision Making Units Making Use of the Synthetic Model of Performance Predictor Neural Networks, and Data Envelopment Analysis (Case Study: Gas National Co. of Iran). Journal of Industrial Engineering, 45(1), 13-29. (in Persian) Alinezhad, A. (2018). A combined method of data envelopment analysis and SERVQUAL model in ranking of service quality. Industrial Management Studies, 16(48), 153-181. (in Persian) Alirezaee, M. R., Afsharian, M. & Taslimi, V. (2008). Provide Rational Solutions for Improving Bank's Branch Performance by Generalized Models of DEA. Economics Research, 7(4), 263-283. (in Persian) Ashrafi, A., Seow, H., Lee, L.S., & and Lee, C.G. (2013). The efficiency of the hotel industry in Singapore Tourism Management, 37, 31-4. Athanassopoulos, A. D. & Curram, S. (1996). A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units. Journal of Operational Research Society, 47(8), 1000-1017. Cecchini, L., Venanzi, S., Pierri, A. & Chiorri, M. (2018). Environmental efficiency analysis and estimation of CO2 abatement costs in dairy cattle farms in Umbria (Italy): A SBM-DEA model with undesirable output. Journal of Cleaner Production, 197(1), 895-907. Cooper, W. W., Deng, H., Gu, B., Li, S. & Thrall., R. M. (2001). Using DEA to improve the management of congestion in Chinese industries (1981–1997). Socio-Economic Planning Sciences, 35(4), 227-242. Cooper, W. W., Seiford, L. M. & Zhu, J. (2011). Data Envelopment Analysis: History, Models and Interpretations. Handbook on Data Envelopment Analysis, US: Springer. Costa, A., & Markellos, R. N., (1997). Evaluating public transport efficiency with neural network models. Transportatior research, 5(5), 301-312. Enke, D., & Suraphan, T. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927-940. Hagan, M. T., Demuth, H. B. & Beal, M. (2002). Neural Network Design. Singapore: Thamson Asia Pte Ltd. Hejazi, R., Anvari Rostami, A. A. & Moghadasy, M. (2008). Total Productivity Analysis of Export Development Bank of Iran and Productivity Growth in Branches- A Data Envelopment Analysis Application. Journal of Industrial Management, 1(1), 39-50. (in Persian) Hillier, F. S. & Lieberman, G. J. (2013). Inventories Management and Forecasting (translated by Faezirad, M. A. & Haghighat, A. Trans.). Tehran, Termeh Pub. (in Persian) Jahanshahloo, G. R. & Khodabakhshi, M. (2004). Suitable combination of inputs for improving outputs in DEA with determining input congestion: Considering textile industry of China. Applied Mathematics and Computation, 151(1), 263-73. Kazemi, M., Monazam Ebrahimpour, S. & Ilbeigi, A. R. (2014). Evaluating the efficiency of Mashhad Municipalities by Data Envelopment Analysis. Journal of Urban Planning, 4(15), 113-132. (in Persian) Kheirkhah, A., Azadeh, A., Saberi, M., Azaron, A. & Shakouri, H. (2013). Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis. Computers & Industrial Engineering, (64)1, 425-441. Kocadağlı, O. & Aşıkgil, B. (2014). Nonlinear time series forecasting with Bayesian neural networks. Expert Systems with Applications, 41(15), 6596-6610. Kwon, H. B. & Lee, J. (2015). Two-stage production modeling of large U.S. banks: A DEA-neural network approach. Expert Systems with Applications, 42(19), 6758-6766. Kwon, H.B. (2017) Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling. International Journal of Production Economics, 183(A), 159-170. Liang, F. (2005). Bayesian neural networks for nonlinear time series forecasting. Statistics and Computing, 15(1), 13-29. MA, J. (2015). A two-stage DEA model considering shared inputs and free intermediate measures. Expert Systems with Applications, 42(9), 4339-4347. Neely, A.D., Gregory, M. & Platts, K. (1995). Performance measurement system design: a literature review and research agenda. International Journal of Operations & Production Management, 15(4), 80-116. Poldrugovac, K., Tekavcic, M. & Jankovic, S. (2016). Efficiency in the hotel industry: an empirical examination of the most influential factors. Economic Research-Ekonomska Istraživanja, 29(1), 583-597. Samoilenko, S. & Osei-Bryson, K. M. (2010) Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks. European Journal of Operational Research, 206(2), 479-487. Seifert, L. M. & Zhu, J. (1998). Identifying excesses and deficits in Chinese industrial productivity (1953–1990): a weighted data envelopment analysis approach. Omega, 26(2), 279-96. Shabanpour, H., Yousefi, S. & Farzipoor Saen, R. (2017). Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. Journal of Cleaner Production, 142(2), 1098-1107. Silva, D. A., Alves, G. A, de Mattos Neto, P. S. G. & Ferreira, T. A. E. (2014). Measurement of Fitness Function efficiency using Data Envelopment Analysis. Expert Systems with Applications, 41(16), 7147-7160. Tone, K. (2001). A slack-based measure of efficiency in date envelopment analysis. European Journal of Operational Research, 130(3), 498-509. Tsai, C.F. & Lu, Y.H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553. Zeng, Y., Zeng, Y., Choi, B., & Wang, L. (2017). Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy, 127, 381-396.
1
ORIGINAL_ARTICLE
Managing Exploratory-exploitative Innovation in Banking Sector
Objective: The main issue for banks is how much and how to invest in a variety of innovative activities. Innovative activities in banks are carried out in two paradoxical exploratory and exploitative poles.
Methods: To answer such questions, a longitudinal multiple case study method was used. To do so, 4 banks were selected as the cases for this study and were analyzed in-depth between 2006 and 2017.
Results: At first, the tensions of exploratory and exploitative innovation approaches were identified in these banks. Then, the required strategies to respond to these tensions were extracted in these banks.
Conclusion: Banks strategies of response were extracted within 17 themes and were classified into the following 4 categories (dimensions): selection, separation, balance, and transcendence.
https://imj.ut.ac.ir/article_67510_f2a16ee378ac0f361dcf961baea6a61f.pdf
2018-03-21
35
60
10.22059/imj.2018.252539.1007392
Exploratory innovation
Exploitative innovation
Tensions
Strategies of response to tensions
Case study
Bank
Gholamreza
Khoshsima
grkhoshsima@ut.ac.ir
1
PhD Student, Production & Operations Management, Faculty of Management, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Seyed Mostafa
Razavi
mrazavi@ut.ac.ir
2
Associate Prof. of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
AUTHOR
Ali
Divandari
divandari@ut.ac.ir
3
Prof. in Business Management, Faculty of Management, University of Tehran, Tehran, Iran
AUTHOR
Seyed Majid
Shariatpanahi
majidshp@yahoo.com
4
Assistant Prof. in Accounting, Faculty of Management and Accounting, University of Allame Tabatabaei, Tehran, Iran
AUTHOR
References
1
Abernathy, W. J., & Clark, K. B. (1985). Innovation: Mapping the winds of creative destruction. Research policy, 14(1), 3-22.
2
Andriopoulos, C., & Lewis, M. W. (2009). Exploitation-exploration tensions and organizational ambidexterity: Managing paradoxes of innovation. Organization Science, 20(4), 696-717.
3
Andriopoulos, C., & Lewis, M. W. (2010). Managing innovation paradoxes: ambidexterity lessons from leading product design companies. Long range planning, 43(1), 104-122.
4
Auh, S., & Menguc, B. (2005). Balancing exploration and exploitation: The moderating role of competitive intensity. Journal of Business Research, 58(12), 1652-1661.
5
Baden-Fuller, C., & Volberda, H. W. (1997). Strategic renewal: How large complex organizations prepare for the future. International Studies of Management & Organization, 27(2), 95-120.
6
Battilana, J., & Dorado, S. (2010). Building sustainable hybrid organizations: The case of commercial microfinance organizations. Academy of management Journal, 53(6), 1419-1440.
7
Belderbos, R., Faems, D., Leten, B., & Looy, B. V. (2010). Technological activities and their impact on the financial performance of the firm: Exploitation and exploration within and between firms. Journal of Product Innovation Management, 27(6), 869-882.
8
Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of management review, 28(2), 238-256.
9
Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological methods & research, 10(2), 141-163.
10
Birkinshaw, J., Brannen, M. Y., & Tung, R. L. (2011). From a distance and generalizable to up close and grounded: Reclaiming a place for qualitative methods in international business research. Journal of International Business Studies, 42(5), 573-581.
11
Bos, J. W., Kolari, J. W., & Van Lamoen, R. C. (2013). Competition and innovation: Evidence from financial services. Journal of Banking & Finance, 37(5), 1590-1601.
12
Burgelman, R. A. (2011). Bridging history and reductionism: A key role for longitudinal qualitative research. Journal of International Business Studies, 42(5), 591-601.
13
Cameron, K. S., & Quinn, R. E. (1988). Organizational paradox and transformation. In R. E. Quinn, & K. S. Cameron (Eds.), Paradox and transformation: toward a theory of change in organization and management: 1-18. Cambridge: Ballinger Pub. Co.
14
Campanella, F., Del Giudice, M., Thrassou, A., & Vrontis, D. (2016). Ambidextrous organizations in the banking sector: an empirical verification of banks’ performance and conceptual development. The International Journal of Human Resource Management, 1-31. DOI: 10.1080/09585192.2016.1239122.
15
Canales, R. (2013). Weaving straw into gold: Managing organizational tensions between standardization and flexibility in microfinance. Organization Science, 25(1), 1-28.
16
Chen, N. F. (2009). Banking reforms for the 21st century: A perfectly stable banking system based on financial innovations. International Review of Finance, 9(3), 177-209.
17
Cheng, Y.T., & Van de Ven, A. H. (1996). Learning the innovation journey: Order out of chaos? Organization Science, 7(6), 593-614.
18
Christiane, P., & Schlegelmilch, B. (2010). Heading for the next innovation archetype? Journal of Business Strategy, 31(1), 46-55.
19
Corley, K. G., & Gioia, D. A. (2004). Identity ambiguity and change in the wake of a corporate spin-off. Administrative Science Quarterly, 49(2), 173-208.
20
Corley, K. G., & Gioia, D. A. (2011). Building theory about theory building: what constitutes a theoretical contribution? Academy of management review, 36(1), 12-32.
21
da Cunha, J. V., Clegg, S. R., & e Cunha, M. P. (2002). Management, paradox, and permanent dialectics. Advances in Organization Studies, 9, 11-40.
22
Davis, G. A., Peterson, J. M., & Farley, F. H. (1974). Attitudes, motivation, sensation seeking, and belief in ESP as predictors of real creative behavior. The Journal of Creative Behavior, 8(1), 31-39.
23
de Wit, B., & Meyer, R. (2014). Strategy: An International Perspective. Cengage Learning.
24
Dierickx, I., & Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management science, 35(12), 1504-1511.
25
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of management review, 14(4), 532-550.
26
Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: opportunities and challenges. Academy of management journal, 50(1), 25-32.
27
Firestone, W. A. (1993). Alternative arguments for generalizing from data as applied to qualitative research. Educational researcher, 22(4), 16-23.
28
Ford, J. D., & Ford, L. W. (1994). Logics of identity, contradiction, and attraction in change. Academy of Management Review, 19(4), 756-785.
29
Frame, W. S., & White, L. J. (2004). Empirical studies of financial innovation: lots of talk, little action? Journal of Economic Literature, 42(1), 116-144.
30
Galbraith, J. R. (1973). Designing complex organizations. Addison-Wesley Pub. Co.
31
Geertz, C. (1973). Thick description: Toward an interpretive theory of culture. In G. Clifford (Ed.), The interpretation of cultures: 3-30.
32
Gersick, C. J. G. (1991). Revolutionary Change Theories: A Multilevel Exploration of the Punctuated Equilibrium Paradigm. The Academy of Management Review, 16(1), 10-36.
33
Gibson, C. B., & Birkinshaw, J. (2004). The antecedents, consequences, and mediating role of organizational ambidexterity. Academy of management Journal, 47(2), 209-226.
34
Gibson, C., & Birkinshaw, J. (2002). Contextual determinants of organizational ambidexterity. Center for Effective Organizations: Marshall School of Business.
35
Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15-31.
36
Glaser, B., & Strauss, A. (1967). The Discovery of Grounded Theory. Strategies for Qualitative Research: Aldine.
37
Goodman, L. A. (1961). Snowball sampling. The annals of mathematical statistics, 32(1), 148-170.
38
Greve, H. R. (2002). Sticky aspirations: Organizational time perspective and competitiveness. Organization Science, 13(1), 1-17.
39
Greve, H. R. (2007). Exploration and exploitation in product innovation. Industrial and Corporate Change, 16(5), 945-975.
40
Gupta, A. K., Smith, K. G., & Shalley, C. E. (2006). The interplay between exploration and exploitation. Academy of management journal, 49(4), 693-706.
41
He, Z.L., & Wong, P.-K. (2004). Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization science, 15(4), 481-494.
42
Holmqvist, M. (2003). A dynamic model of intra-and interorganizational learning. Organization studies, 24(1), 95-123.
43
Holmqvist, M. (2004). Experiential learning processes of exploitation and exploration within and between organizations: An empirical study of product development. Organization science, 15(1), 70-81.
44
Houghton, C., Casey, D., Shaw, D., & Murphy, K. (2013). Rigour in qualitative case-study research. Nurse Researcher, 20(4), 12-17.
45
Hu, T., & Xie, C. (2016). Competition, Innovation, Risk-Taking, and Profitability in the Chinese Banking Sector: An Empirical Analysis Based on Structural Equation Modeling. Discrete Dynamics in Nature and Society, 2016.
46
Huber, A., Miles, M., & Saldana, J. (2014). Qualitative data analysis: A methods sourcebook. Thousand Oaks: Sage.
47
Huff, A. S. (1990). Mapping strategic thought. John Wiley & Sons.
48
Hunter, S. T., Cushenbery, L. D., & Jayne, B. (2017). Why dual leaders will drive innovation: Resolving the exploration and exploitation dilemma with a conservation of resources solution. Journal of Organizational Behavior, 38(8), 1183-1195.
49
Jansen, J. (2005). Ambidextrous organizations: a multiple-level study of absorptive capacity, exploratory and exploitative innovation and performance. Erasmus Research Institute of Management (ERIM).
50
Jansen, J. J. P. (2008). Combining competence building and leveraging: managing paradoxes in ambidextrous organizations. In A. Heene, R. Martens, & R. Sanchez (Eds.), Advances in Applies Busimess Strategy: Competence Perspectives on Learning and Dynamic Capabilities, Vol. 10, 99-119.
51
Jansen, J. J. P., Tempelaar, M. P., Bosch, F. A. J. V. d., & Volberda, H. W. (2009). Structural Differentiation and Ambidexterity: The Mediating Role of Integration Mechanisms. Organization Science, 20(4), 797-811.
52
Jansen, J. J. P., Van Den Bosch, F. A. J., & Volberda, H. W. (2006). Exploratory innovation, exploitative innovation, and performance: Effects of organizational antecedents and environmental moderators. Management science, 52(11), 1661-1674.
53
Jansen, J. J., Van den Bosch, F. A., & Volberda, H. W. (2005a). Exploratory innovation, exploitative innovation, and ambidexterity: The impact of environmental and organizational antecedents. Schmalenbach Business Review, 57(4), 351-363.
54
Jansen, J. J., Van den Bosch, F. A., & Volberda, H. W. (2005b). Exploratory innovation, exploitative innovation, and ambidexterity: The impact of environmental and organizational antecedents. Schmalenbach Business Review, 6(4), 351-363.
55
Jarzabkowski, P. A., Lê, J. K., & Feldman, M. S. (2012). Toward a theory of coordinating: Creating coordinating mechanisms in practice. Organization Science, 23(4), 907-927.
56
Jarzabkowski, P., & Sillince, J. (2007). A rhetoric-in-context approach to building commitment to multiple strategic goals. Organization Studies, 28(11), 1639-1665.
57
Jarzabkowski, P., Lê, J. K., & Van de Ven, A. H. (2013). Responding to competing strategic demands: How organizing, belonging, and performing paradoxes coevolve. Strategic Organization, 11(3), 245-280.
58
Jay, J. (2013). Navigating paradox as a mechanism of change and innovation in hybrid organizations. Academy of Management Journal, 56(1), 137-159.
59
Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative science quarterly, 24(4), 602-611.
60
Katila, R., & Ahuja, G. (2002). Something old, something new: A longitudinal study of search behavior and new product introduction. Academy of management journal, 45(6), 1183-1194.
61
Koch, T. (2006). Establishing rigour in qualitative research: the decision trail. Journal of advanced nursing, 53(1), 91-100.
62
Kraatz, M. S., & Block, E. S. (2008). Organizational Implications of Institutional Pluralism. In Royston Greenwood, Christine Oliver, Roy Suddaby, & K. Sahlin-Andersson (Eds.), The SAGE Handbook of Organizational Institutionalism, 243-275. Thousank Oaks: Sage.
63
Kvale, S. (1983). The qualitative research interview: A phenomenological and a hermeneutical mode of understanding. Journal of Phenomenological Psychology, 14(2), 171-196.
64
Langley, A. (1999). Strategies for Theorizing from Process Data. Academy of Management Review, 24(4), 691-710.
65
Langley, A. (2007). Process thinking in strategic organization. Strategic organization, 5(3), 271-282.
66
Lavie, D., Stettner, U., & Tushman, M. L. (2010). Exploration and exploitation within and across organizations. Academy of Management annals, 4(1), 109-155.
67
Lawrence, P. R., & Lorsch, J. W. (1967). Differentiation and integration in complex organizations. Administrative science quarterly, 12(1), 1-47.
68
Leonard-Barton, D. (1990). A dual methodology for case studies: Synergistic use of a longitudinal single site with replicated multiple sites. Organization science, 1(3), 248-266.
69
Leonard‐Barton, D. (1992). Core capabilities and core rigidities: A paradox in managing new product development. Strategic management journal, 13(S1), 111-125.
70
Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic management journal, 14(S2), 95-112.
71
Levitt, B., & March, J. G. (1988). Organizational learning. Annual review of sociology, 319-340.
72
Lewin, A. Y., Long, C. P., & Carroll, T. N. (1999). The coevolution of new organizational forms. Organization science, 10(5), 535-550.
73
Lewis, M. W. (2000). Exploring paradox: Toward a more comprehensive guide. Academy of Management Review, 25(4), 760-776.
74
Lewis, M. W., & Dehler, G. E. (2000). Learning through paradox: A pedagogical strategy for exploring contradictions and complexity. Journal of Management Education, 24(6), 708-725.
75
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. SAGE Publications.
76
Locke, K., Golden-Biddle, K., & Feldman, M. S. (2008). Perspective—making doubt generative: Rethinking the role of doubt in the research process. Organization science, 19(6), 907-918.
77
Lüscher, L. S., & Lewis, M. W. (2008). Organizational change and managerial sensemaking: Working through paradox. Academy of Management Journal, 51(2), 221-240.
78
Luscher, L. S., Lewis, M., & Ingram, A. (2006). The social construction of organizational change paradoxes. Journal of Organizational Change Management, 19(4), 491-502.
79
Maitlis, S. (2005). The social processes of organizational sensemaking. Academy of management journal, 48(1), 21-49.
80
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization science, 2(1), 71-87.
81
McGrath, R. G. (2001). Exploratory learning, innovative capacity, and managerial oversight. Academy of management journal, 44(1), 118-131.
82
Miles, M., B., & Huberman, M.A. (1994). Qualitative Data Analysis: An Expanded Sourcebook. SAGE Publications.
83
Miller, C. C., Cardinal, L. B., & Glick, W. H. (1997). Retrospective reports in organizational research: A reexamination of recent evidence. Academy of management journal, 40(1), 189-204.
84
Miller, D., & Chen, M.J. (1994). Sources and consequences of competitive inertia: A study of the US airline industry. Administrative Science Quarterly, 39(1),1-23.
85
Miller, D., & Friesen, P. H. (1986a). Porter's (1980) generic strategies and performance: an empirical examination with American data: part I: testing Porter. Organization studies, 7(1), 37-55.
86
Miller, D., & Friesen, P. H. (1986b). Porter's (1980) Generic Strategies and Performance: An Empirical Examination with American Data: Part II: Performance Implications. Organization Studies, 7(3), 255-261.
87
Murnighan, J. K., & Conlon, D. E. (1991). The dynamics of intense work groups: A study of British string quartets. Administrative Science Quarterly, 36(2), 165-186.
88
Nemanich, L. A., Keller, R. T., & Vera, D. (2007). Managing the exploration/exploitation paradox in new product development: how top executives define their firm's innovation trajectory. International Journal of Innovation and Technology Management, 4(03), 351-374.
89
Nooteboom, B. (1999). Innovation, learning and industrial organisation. Cambridge Journal of economics, 23(2), 127-150.
90
Papachroni, A., Heracleous, L., & Paroutis, S. (2014). Organizational Ambidexterity through the Lens of Paradox Theory Building a Novel Research Agenda. The Journal of Applied Behavioral Science, 51(1), 71-93.
91
Petruzzelli, A. M. (2014). Balancing knowledge exploration and exploitation within and across technological and geographical domains. Knowledge Management Research & Practice, 12(2), 123-132.
92
Pettigrew, A.M. (1990). Longitudinal field research on change: Theory and practice. Organization science, 1(3), 267-292.
93
Poole, M. S., & Van de Ven, A. H. (1989). Using paradox to build management and organization theories. Academy of management review, 14(4), 562-578.
94
Prange, C., & Schlegelmilch, B. B. (2009a). The role of ambidexterity in marketing strategy implementation: Resolving the exploration-exploitation dilemma. Business Research, 2(2), 215-240.
95
Prange, C., & Schlegelmilch, B. B. (2009b). The role of ambidexterity in marketing strategy implementation: resolving the exploration-exploitation dilemma. BuR-Business Research, 2(2), 215-240.
96
Putnam, L. L., Fairhurst, G. T., & Banghart, S. (2016). Contradictions, dialectics, and paradoxes in organizations: A constitutive approach. The Academy of Management Annals, 10(1), 65-107.
97
Raisch, S., & Birkinshaw, J. (2008). Organizational ambidexterity: Antecedents, outcomes, and moderators. Journal of management, 34(3), 375-409.
98
Romanelli, E., & Tushman, M. L. (1994). Organizational transformation as punctuated equilibrium: An empirical test. Academy of Management Journal, 37(5), 1141-1166.
99
Sanday, P. R. (1979). The ethnographic paradigm (s). Administrative Science Quarterly, 24(4), 527-538.
100
Schad, J., Lewis, M. W., Raisch, S., & Smith, W. K. (2016). Paradox research in management science: Looking back to move forward. The Academy of Management Annals, 10(1), 5-64.
101
Schilling, M. A. (1998). Technological lockout: An integrative model of the economic and strategic factors driving technology success and failure. Academy of Management Review, 23(2), 267-284.
102
Seo, M., Putnam, L. L., & Bartunek, J. M. (2004). Dualities and tensions of planned organizational change, Handbook of organizational change and innovation, 73-107.
103
Sfirtsis, T., & Moenaert, R. (2010). Managing the interaction of exploration and exploitation: Ambidexterity as a high-order dynamic capability. In Ron Sanchez, Aimé Heene, & T. E. Zimmermann (Eds.), A Focussed Issue on Identifying, Building, and Linking Competences, Vol. 5, 35-57: Emerald Group Publishing Limited.
104
Sheremata, W. A. (2000). Centrifugal and centripetal forces in radical new product development under time pressure. Academy of management review, 25(2), 389-408.
105
Sidhu, J. S., Commandeur, H. R., & Volberda, H. W. (2007). The multifaceted nature of exploration and exploitation: Value of supply, demand, and spatial search for innovation. Organization Science, 18(1), 20-38.
106
Simsek, Z., Heavey, C., Veiga, J. F., & Souder, D. (2009). A typology for aligning organizational ambidexterity's conceptualizations, antecedents, and outcomes. Journal of Management Studies, 46(5), 864-894.
107
Smith, K. K., & Berg, D. N. (1987). Paradoxes of group life: understanding conflict, paralysis, and movement in group dynamics. Jossey-Bass.
108
Smith, W. K. (2014). Dynamic decision making: A model of senior leaders managing strategic paradoxes. Academy of Management Journal, 57(6), 1592-1623.
109
Smith, W. K., & Lewis, M. W. (2011). Toward a theory of paradox: A dynamic equilibrium model of organizing. Academy of Management Review, 36(2), 381-403.
110
Smith, W. K., & Tushman, M. L. (2005). Managing strategic contradictions: A top management model for managing innovation streams. Organization science, 16(5), 522-536.
111
Smith, W. K., Binns, A., & Tushman, M. L. (2010). Complex business models: Managing strategic paradoxes simultaneously. Long range planning, 43(2), 448-461.
112
Stadler, C., Rajwani, T., & Karaba, F. (2014). Solutions to the exploration/exploitation dilemma: Networks as a new level of analysis. International Journal of Management Reviews, 16(2), 172-193.
113
Stake, R. E. (1995). The Art of Case Study Research. SAGE Publications.
114
Strauss, A. L., & Corbin, J. M. (1990). Basics of qualitative research: grounded theory procedures and techniques. Sage Publications.
115
Thompson, J. D. (1967). Organizations in action: Social science bases of administrative theory. Transaction publishers.
116
Tracy, S. J. (2004). Dialectic, contradiction, or double bind? Analyzing and theorizing employee reactions to organizational tension. Journal of Applied Communication Research, 32(2), 119-146.
117
Tushman, M. L., & Anderson, P. (1986). Technological discontinuities and organizational environments. Administrative science quarterly, 31(3), 439-465.
118
Tushman, M. L., & O'Reilly Iii, C. A. (1996). Ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review, 38(4), 8-30.
119
Tushman, M. L., & O'Reilly, C.-A. (2002). Winning through innovations. Harvard Business School Press, Boston.
120
Tushman, M. L., & Romanelli, E. (1985). Organizational evolution: A metamorphosis model of convergence and reorientation. Research in Organizational Behavior, 7, 171-222.
121
Tushman, M. L., Anderson, P. C., & O’Reilly, C. (1997). Technology cycles, innovation streams, and ambidextrous organizations: organization renewal through innovation streams and strategic change. In Managing Strategic Innovation and Change, edited by P. Anderson and M. Tushman. New York: Oxford University Press.
122
Tushman, M., Smith, W. K., Wood, R. C., Westerman, G., & O’Reilly, C. (2010). Organizational designs and innovation streams. Industrial and Corporate Change, 19(5), 1331-1366.
123
Van de Ven, A. H., & Poole, M. S. (1988). Paradoxical requirements for a theory of organizational change. In R. E. Quinn, & K. S. Cameron (Eds.), Paradox and transformation: Toward a theory of change in organization and management, 19-59. University of Michigan: Ballinger Pub. Co.
124
Veider, V., & Matzler, K. (2016). The ability and willingness of family-controlled firms to arrive at organizational ambidexterity. Journal of Family Business Strategy, 7(2), 105-116.
125
Volberda, H. W. (1998). Building the Flexible Firm: How to Remain Competitive. Oxford University Press.
126
Yin, R. K. (2009). Case Study Research: Design and Methods. SAGE Publications.
127
Zarutskie, R. (2013). Competition, financial innovation and commercial bank loan portfolios. Journal of Financial Intermediation, 22(3), 373-396.
128
ORIGINAL_ARTICLE
Designing Green Closed-loop Supply Chain Network with Financial Decisions under Uncertainty
Objective: Effective design of supply chain networks is necessary for micro-economic development. The aim of this study is to design green closed-loop supply chain network with financial decisions considering economic and environmental dimensions of development. Such decisions consist of non-supply chain investments and available loans. Uncertainty of demand and investments related to other investments (ROI) are taken into account, too.
Methods:Proposed model is multi-product, multi-objective, multi-period, stochastic and closed-loop which is modeled as a mixed integer linear programming problem. A scenario path model is applied in order to deal with the uncertainties.
Results: The results approved the effectiveness of considering financial decisions. By increasing the number of available loans, the level of the service delivered to the whole system will increase accompanied by a decreasing inclination. Obtained results are based on a case study in plastic recycling industry.
Conclusion: Simultaneous consideration of financial decisions and uncertainty in supply chain network design can lead to an improvement in the profit of the supply chain.
https://imj.ut.ac.ir/article_67511_70dcb3cad197c9c23957d7b18cb6a3c0.pdf
2018-03-21
61
84
10.22059/imj.2018.240867.1007303
Green supply chain
Mathematical Programming
Supply chain network
uncertainty
Financial decisions
Amir Salar
Mohammadi
am_mohammadi@sbu.ac.ir
1
Ph.D. Student in Operation Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
AUTHOR
Akbar
Alem Tabriz
a-tabriz@sbu.ac.ir
2
Prof. of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Mir Saman
Pishvaee
pishvaee@iust.ac.ir
3
Assistant Prof. of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
ابراهیمی، مهران؛ صفری، حسین؛ صادقیمقدم، محمدرضا (1396). ارائه مدل تداوم زنجیره تأمین بر اساس رویکرد طراحی آگزوماتیک، 9 (4)، 563-586.
1
کلانتری، محدثه؛ پیشوایی، میرسامان؛ یعقوبی، سعید (1394). یک مدل بهینهسازی چندهدفه برای یکپارچهسازی جریان فیزیکی در برنامهریزی اصلی زنجیره تأمین. چشمانداز مدیریت صنعتی، 19، 9–31.
2
فلاح لاجیمی، حمیدرضا؛ عرب، علیرضا؛ بهرام زاده، هوشمند (1395). بررسی موانع پیادهسازی زنجیره تأمین سبز در صنایع فولاد استان مازندران با رویکرد ترکیبی BSC/BWM. فصلنامه مدیریت صنعتی، 8 (4)، 653-684.
3
معزز، هاشم؛ عزیزی، جواد (1395). توسعه مدل مدیریت زنجیره تأمین سبز یانگ در شرکت سینره. فصلنامه مدیریت صنعتی، 8 (2)، 309-332.
4
References
5
Aghaei, J., Amjadi, N., & Shayanfar, H.A. (2011). Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Applied Soft Computing, 11(4), 3846–3858.
6
Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165–4176.
7
Babazadeh, R., Razmi, J., Pishvaee, M. S., & Rabbani, M. (2017). A sustainable second-generation biodiesel supply chain network design problem under risk. Omega (United Kingdom), 66, 258–277.
8
Bloemhof-Ruwaard, J., Van Wassenhovel, L. N., Gabel, H. L., & Weaver, P. M. (1996). An environmental life cycle optimization model for the European pulp and paper industry. Omega, 6(24), 615–629.
9
Corsano, G., Vecchietti, A. R., & Montagna, J. M. (2011). Optimal design for sustainable bioethanol supply chain considering detailed plant performance model. Computers and Chemical Engineering, 35(8, S1), 1384–1398.
10
Costi, P., Minciardi, R., Robba, M., Rovatti, M., & Sacile, R. (2004). An environmentally sustainable decision model for urban solid waste management. Waste Management, 24(3), 277–295.
11
Ebrahimi, M., Safari, H., & Sadeghi-Moghadam, M. R. (2018). A Supply chain continuity model based on axiomatic design approach. Industrial Management Journal, 9(4), 563-586. (in Persian)
12
Fallah-Lajimi, H. R., Arab, A., & Bahramzadeh, H. (2017). Investigate the barriers of implement green supply chain in Mazandaran steel industry with a combined approach BSC / BWM. Industrial Management Journal, 8(4), 653-684. (in Persian)
13
Grossmann, I. E., & Guillén-Gosálbez, G. (2010). Scope for the application of mathematical programming techniques in the synthesis and planning of sustainable processes. Computers & Chemical Engineering, 34(9), 1365–1376.
14
Giarola, S., Shah, N., & Bezzo, F. (2012). A comprehensive approach to the design of ethanol supply chains including carbon trading effects. Bioresource Technology, 107, 175–185.
15
Golpira, H., Zandieh, M., Najaa, E., & Sadi-Nezhad, S. (2017). A multi-objective, multi-echelon green supply chain network design problem with risk-averse retailers in an uncertain environment. Scientia Iranica E, 24(1), 413–423.
16
Guillén-Gosálbez, G., & Grossmann, I. (2010). A global optimization strategy for the environmentally conscious design of chemical supply chains under uncertainty in the damage assessment model. Computers & Chemical Engineering, 34(1), 42–58.
17
Guillén-Gosálbez, G., & Grossmann, I. (2009). Optimal design and planning of sustainable chemical supply chains under uncertainty. AICHE journal, 55(1), 99–121.
18
Guillen, G., Badell, M., & Puigjaner, L. (2007). A holistic framework for short-term supply chain management integrating production and corporate financial planning. International Journal of Production Economics, 106(1), 288–306.
19
Kalantari, M., Pishvaee, M. S., & Yaghoubi, S. (2015). A multi-objective optimization model for integrating financial and phisical flow in supply chain master planning. Journal of Industrial Management Perspective, 19, 9-31. (in Persian)
20
Laı, J.M., Guille, G., Badell, M., Espun, A., & Puigjaner, L. (2007). Enhancing Corporate Value in the Optimal Design of Chemical Supply Chains. Industrial and Engineering Chemistry Research, 46(23), 7739–7757.
21
Lira-Barragán, L. F., Ponce-Ortega, J. M., Serna-González, M., & El-Halwagi, M. (2011). An MINLP Model for the Optimal Location of a New Industrial Plant with Simultaneous Consideration of Economic and Environmental Criteria. Industrial Engineering Chemistry Research, 50(2), 953–964.
22
Longinidis, P., & Georgiadis, M. C. (2013). Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty. Computers and Chemical Engineering, 48, 264–279.
23
MA, R., YAO, L., JIN, M., REN, P., & LV, Z. (2016). Robust environmental closed-loop supply chain design under uncertainty. Chaos, Solitons and Fractals, 89, 195–202.
24
Mavrotas, G. (2009). Effective implementation of the ??-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455–465.
25
Moazzez, H., & Azizi, J. (2016). Developing the green supply chain management model of Yang in Cinere company. Industrial Management Journal, 8(2), 309-332. (in Persian)
26
Mohammadi, M., Torabi, S. a., & Tavakkoli-Moghaddam, R. (2014). Sustainable hub location under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 62, 89–115.
27
Mohammed, F., Selim, S. Z., Hassan, A., & Syed, M. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transportation Research Part D: Transport and Environment, 51, 146–172.
28
Mohseni, S., & Pishvaee, M. S. (2016). A robust programming approach towards design and optimization of microalgae-based biofuel supply chain. Computers & Industrial Engineering, 100, 58-71.
29
Nickel, S., Saldanha-da-Gama, F., & Ziegler, H.-P. (2012). A multi-stage stochastic supply network design problem with financial decisions and risk management. Omega, 40(5), 511–524.
30
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433–3446.
31
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20.
32
Pishvaee, M. S., Torabi, S. A., & Razmi, J. (2012). Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, 62(2), 624–632.
33
Pishvaee, M. S., Zanjirani Farahani, R., & Dullaert, W. (2006). C A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & Operations Research, 37(6), 1100–1112.
34
PlasticsEurope. (2016). Plastic - the facts 2016, 38. Retrieved from http://www.plasticseurope. es/Document/plastics---the-facts-2016-15787.aspx?FolID=2.
35
Ruiz-Femenia, R., Guillen-Gosalbez, G., Jimenez, L., & Caballero, J. a. (2013). Multi-objective optimization of environmentally conscious chemical supply chains under demand uncertainty. Chemical Engineering Science, 95, 1–11.
36
Saffar, M. M., G, H. S., & Razmi, J. (2015). A new multi objective optimization model for designing a green supply chain network under uncertainty. International Journal of Industrial Engineering Computations, 6, 15–32.
37
Saffar, M. M., Shakouri G., H., & Razmi, J. (2014). A new bi-objective mixed integer linear programming for designing a supply chain considering CO2 emission. Uncertain Supply Chain Management, 2(4), 275–292.
38
Shapiro, J. F. (2004). Challenges of strategic supply chain planning and modeling.. Computers & Chemical Engineering, 28(6), 855–861.
39
Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy Multi-Objective Sustainable and Green Closed-Loop Supply Chain Network Design. Computers & Industrial Engineering, 109, 191-203.
40
Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review. International Journal of Management Reviews, 9(1), 53–80.
41
Stadtler, H., & Kilger, C. (2005). Supply chain management and advance planning (3rd ed.). Springer.
42
Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill, USA.
43
Verma, M., Gendreau, M., & Laporte, G. (2013). Optimal location and capability of oil-spill response facilities for the south coast of Newfoundland. Omega, 41(5), 856–867.
44
Yilmaz Balaman, Ş., & Selim, H. (2016). Sustainable design of renewable energy supply chains integrated with district heating systems: A fuzzy optimization approach. Journal of Cleaner Production, 133, 863-885.
45
ORIGINAL_ARTICLE
Investigating the Relationship between Organizational Ambidexterity and Performance-Related Dimensions in Banking Industry (Case Study: Gilan Bank Branches)
Objective: Organizational ambidexterity refers to an organization’s capacity to pursue two distinct goals at the same time, and is known as an important criterion in achieving competitive advantage. So far, many studies have been documented on the concept of organizational ambidexterityand its impact on performance. Nevertheless, few studies have examined its dual dimensions. Evidences suggest that ambidexterity has been positively correlated with performance, and for an organization, it is a valuable tool for continuously improvement in the performance. In this study, ambidexterity has been considered as a construct with two dimensions of balance and combination, and attempt has been made to measure the relationship between this two dimensions and the performance in banking industry. Methods: To do so, initially, reviewing the literature, the conceptual model of this study was extracted, and then in order to analyze the research model and hypotheses, we examined 161 bank branches. The structural equation modeling based on partial least squares (PLS) was used to analyze the data. Results: The data analysis suggested a significantly positive impact of ambidexterity on performance which means if the organizations achieve high level of exploitation and exploration, the performance will improve. Conclusion: In this study, the increase in the combined amount of two exploitation and exploration activities or on the absolute value of the difference between these two activities was pointed out and according to the two aforementioned viewpoints, the role of ambidexterity on the performance was investigated. The contradictory results of the research in the two balanced and combined approaches indicated that it is not the balance between the two dimensions of exploitation and exploration that has a positive effect on performance and any increase in each of these two dimensions can lead to the improvement of the performance.
https://imj.ut.ac.ir/article_67512_5d779c12eaf0cfecacd1ba1ecb51aa3c.pdf
2018-03-21
85
100
10.22059/imj.2018.141514.1007005
Organizational Ambidexterity
Balanced ambidexterity
Combined ambidexterity
organizational performance
Partial Least Squares (PLS)
Sara
Khodadadi
sara.khodadadi4169@yahoo.com
1
MSc in Industrial Management, University of Guilan, Rasht, Iran
AUTHOR
Mahmoud
Moradi
m.moradi@guilan.ac.ir
2
Associate Prof. of Industrial Management, University of Guilan, Rasht, Iran
LEAD_AUTHOR
Keikhosro
Yakideh
yakideh@guilan.ac.ir
3
Assistant Prof. of Industrial Management, University of Guilan, Rasht, Iran
AUTHOR
بندریان، رضا (1392). دوسوتوانی همزمان، الگوی مناسب سازماندهی فعالیتهای اکتشاف و بهرهبرداری در سازمانهای پژوهش و فناوری. دو فصلنامه توسعه تکنولوژی صنعتی، (22)، 21- 32.
1
داوری، علی؛ رضازاده، آرش (1392). مدلسازی معادلات ساختاری با نرمافزار PLS. چاپ اول. تهران: انتشارات جهاد دانشگاهی.
2
فلاح شمس لیالستانی، میرفیض؛ راجی، معصومه؛ خواجهپور، محمود (1392). ارزیابی عملکرد سازمان با رویکرد ترکیبی AHP، BSC و TOPSIS. فصلنامه مدیریت صنعتی، 5(1)، 81- 100.
3
قاسمی، عبدالرسول؛ جهانگرد، اسفندیار (1390). برآورد کارایی مؤلفهای شعب بانک مسکن در تجهیز منابع و تخصیص
4
تسهیلات: رویکرد مدل ابرکارایی با محدودیتهای وزنی. فصلنامه مدیریت صنعتی، 3 (6)، 113- 128.
5
مرادی، محمود؛ زنجانی، بهناز؛ جمالی، علی (1393). مدلسازی عملکرد شغلی با استفاده از سیستم بهینه استنتاج
6
فازی ـ عصبی تطبیقی (مطالعه موردی: شرکت گاز استان گیلان). فصلنامه مدیریت صنعتی، 6(1)، 111- 136.
7
References
8
Adler, P. S., Goldoftas, B., & Levine, D. I. (1999). Flexibility versus efficiency? A case study of model changeovers in the Toyota production system. Organization science, 10(1), 43-68.
9
Bandarian, R. (2013). Simultaneous Ambidexterity, the Appropriate Model of Organizing Exploration and Exploitation Activities in Research and Technology Organizations. Journal of Industrial Technology, (22), 21-32. (in Persian)
10
Birkinshaw, J., & Gupta, K. (2013). Clarifying the distinctive contribution of ambidexterity to the field of organization studies. The Academy of Management Perspectives, 27(4), 287-298.
11
Cao, Q., Gedajlovic, E., & Zhang, H. (2009). Unpacking organizational ambidexterity: Dimensions, contingencies, and synergistic effects. Organization Science, 20(4), 781-796.
12
Davari, A., Rezazade, A. (2013). Structural Equation Modeling with PLS. Tehran: Jahad daneshgahi Press (in Persian)
13
Fallah Shams Lialestanei, M. F., Raji, M., & Khajehpour, M. (2013). Organization Performance Evaluation with a combined approach BSC, AHP and TOPSIS. Industrial Management, 5(1), 81-100. (in Persian)
14
Geerts, A., Blindenbach-Driessen, F., & Gemmel, P. (2010). Ambidextrous Innovation Behaviour in Service Firms. Status: published.
15
Ghasemi, A. R., Jahangard, S. (2011). Estimates the Component Efficiency of Housing Bank Branches in Resource Mobilization and Facilities Allocation: Super Efficiency model approach with weight limitations. Industrial Management, 3(6), 113-128. (in Persian)
16
Gibson, C. B., & Birkinshaw, J. (2004). The antecedents, consequences, and mediating role of organizational ambidexterity. Academy of management Journal, 47(2), 209-226.
17
Gulati, R., & Puranam, P. (2009). Renewal through reorganization: The value of inconsistencies between formal and informal organization. Organization Science, 20(2), 422-440.
18
He, Z. L., & Wong, P. K. (2004). Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization science, 15(4), 481-494.
19
Im, G., & Rai, A. (2008). Knowledge sharing ambidexterity in long-term interorganizational relationships. Management Science, 54(7), 1281-1296.
20
Junni, P., Sarala, R., Taras, V., & Tarba, S. (2013). Organizational ambidexterity and performance: A meta-analysis. The Academy of Management Perspectives, amp-2012.
21
Kang, S. C., & Snell, S. A. (2009). Intellectual capital architectures and ambidextrous learning: a framework for human resource management. Journal of Management Studies, 46(1), 65-92.
22
Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic management journal, 14(S2), 95-112.
23
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization science, 2(1), 71-87.
24
Moradi, M., Zanjani, B., & Jamali, A. (2014). Job Performance Modeling by using Comparative Fuzzy-Neural Inference optimal system (Case Study: Guilan Gas Company. Industrial Management, 6(1), 111-136. (in Persian)
25
O'Reilly, C., & Tushman, M. (2013). Organizational ambidexterity: Past, present and future. The Academy of Management Perspectives, 27(4), 324-338.
26
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879-903.
27
Raisch, S., & Birkinshaw, J. (2008). Organizational ambidexterity: Antecedents, outcomes, and moderators. Journal of Management, 34(3), 375-409.
28
Richard, P. J., Devinney, T. M., Yip, G. S., & Johnson, G. (2009). Measuring organizational performance: Towards methodological best practice. Journal of management, 35(3), 718-804.
29
Simsek, Z., Heavey, C., Veiga, J. F., & Souder, D. (2009). A typology for aligning organizational ambidexterity's conceptualizations, antecedents, and outcomes. Journal of Management Studies, 46(5), 864-894.
30
Tangen, S. (2004). Professional Practice Performance Measurement: From Philosophy to Practice. International Journal of Productivity and performance Management, 53(8), 26-37.
31
Turner, N., Swart, J., & Maylor, H. (2013). Mechanisms for managing ambidexterity: a review and research agenda. International Journal of Management Reviews, 15(3), 317-332.
32
Tushman, M. L., & O'Reilly, C. A. (1996). Ambidextrous Organizations: managing evolutionary and revolutionary change. California Management Review, 38(4), 8-30.
33
Warner, R. M. (2008). Applied statistics: From bivariate through multivariate techniques. Sage.
34
Zhou, J., & Xue, Q. Z. (2013, January). Organizational Learning, Ambidexterity, and Firm Performance. In The 19th International Conference on Industrial Engineering and Engineering Management (pp. 537-546). Springer Berlin Heidelberg.
35
ORIGINAL_ARTICLE
Explaining the Role of Integrated Supply Chain on Attainment of World Class Manufacturing in Electronic Domestic Appliance Industries
Objective: Current dynamic and complicated environment has made organizations and industries compete at an international level. If a world-class level production is pursued, they have to manufacture their products in a world-class level. To meet the world-class production indicators and remain competitive, they need to be more integrated at the level of organization, partners and supply chain. The present study aims to investigate the relationship between supply chain integration (SCI) and world class manufacturing (WCM).
Methods: This study uses a descriptive-correlative method and its research population covers Electronic Domestic Appliance Industry companies. The samples in this research were selected using simple random sampling method. Fuzzy Delphi method was used to identify the dimensions and indicators of production in the world class level and to collect data, a researcher-made questionnaire was used. AMOS software was used to test the hypotheses through a statistical test of Structural Equation Modeling (SEM).
Results: The results showed that three dimensions of SCI namely, internal integration, supplier and customer have significantly positive impacts on obtaining products in world class level. The impact of customer integrity on delivery was not confirmed while high level of integral integrity is claimed to have more effects on cost and on-time delivery rather than other integration dimensions of supply chain integration.
Conclusion: As the results showed, the companies in the industry are required to take actions on integrating supply chain comprising internal, supplier and customer integration in order to obtain WCM dimensions such as cost, innovation, quality, delivery, flexibility and services. Also result of. This paper helps managers to distinguish the effects of the different dimensions of SCI on obtaining WCM.
https://imj.ut.ac.ir/article_67513_7394abd504fb61e5e0328cf268bdfc3b.pdf
2018-03-21
101
120
10.22059/imj.2018.247134.1007355
Fuzzy Delphi
Global Competition
Supply chain integration
Structural Equation Modeling
World class manufacturing
Seied Davoud
Mirhabibi
d.mirhabibi@yahoo.com
1
Ph.D. Student in Industrial Management, Faculty of Management, Tehran South Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Hasan
Farsijani
h-farsi@sbu.ac.ir
2
Associate Prof. in Industrial Management, Faculty of Management, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Mahmoud
Modiri
m_modiri@azad.ac.ir
3
Assistant Prof. in Industrial Management Faculty of Management, Tehran South Branch, Islamic Azad University, Tehran, Iran
AUTHOR
kaveh
khalili Damghani
k_khalili@azad.ac.ir
4
Associate Prof. in Industrial Engineering, Faculty of Industrial Engineering, Tehran South Branch, Islamic Azad University, Tehran, Iran
AUTHOR
ایرانزاده، سلیمان؛ سرایینیا، الهام (1395). ارائه مدلی جهت ارزیابی یکپارچگی استراتژیک زنجیره تأمین بارویکرد خلق ارزش (مطالعه موردی: زنجیره تأمین ایران خودرو). پژوهشنامه مدیریت اجرایی، 8(16)، 87- 110.
1
ثابت مطلق، محمد؛ محقر، علی (1395). بهکارگیری الگوریتم ژنتیک برای برنامهریزی تأمین، تولید و توزیع یکپارچه سیستمهای مونتاژ، مدیریت صنعتی، 8 (2)، 163- 190.
2
سلسبیل، مهسا؛ شفیعا، محمدعلی؛ پیشوایی، میرسامان؛ شهانقی، کامران (1394). برنامهریزی تاکتیکی استوار زنجیره تأمین جهانی سه سطحی تحت شرایط اختلال تحریم با در نظر گرفتن عمر قفسهای (مطالعه موردی: زنجیره تأمین دارو). مدیریت صنعتی، 7 (2)، 305- 332.
3
سید حسینی، سید محمد؛ سلوکدار، علیرضا (1386). نقد و بررسی دیدگاهها و عوامل مختلف در مورد مدل پویای تولید در کلاس جهانی. پنجمین کنفرانس بینالمللی مدیریت، تهران.
4
عالم تبریز، اکبر؛ طلایی، حمیدرضا؛ مرادی، الناز (1392). ارزیابی عوامل کلید پیادهسازی موفق تولید در کلاس جهانی با استفاده از رویکرد یکپارچه مدلسازی ساختاری تفسیری، تئوری گراف ورویکرد ماتریسی (مطالعه موردی: گروه ایران خودرو وسایپا). مدیریت صنعتی، 5(1)، 63-80.
5
مشایخی، المیرا؛ عالم تبریز، اکبر (1395). تأثیر یکپارچگی بالادستی و پاییندستی زنجیره تأمین بر عملکرد و برنامه کیفیت. چشمانداز مدیریت صنعتی، (4)6، 37-57.
6
فارسیجانی، حسن (1392). روشهای تولید و عملیات در کلاس جهانی. تهران: انتشارات سمت.
7
فارسیجانی، حسن؛ فلاح حسینی، علی (1391). شناسایی و اولویتبندی عوامل مؤثر دستیابی مدیریت زنجیره تأمین به کلاس جهانی و ارائه راهکارهای مناسب. چشمانداز مدیریت صنعتی، (2)3، 25-44.
8
فکور ثقیه، امیرمحمد؛ الفت، لعیا؛ فیضی، کامران؛ امیری، مقصود (1393). مدلی برای قابلیت ارتجاعی زنجیره تأمین برای رقابتپذیری در شرکتهای خودروسازی ایران. مدیریت تولید و عملیات، 5(1)، 143- 164.
9
فیروزآبادی، سید محمدعلی؛ الفت، لعیا؛ امیری، مقصود. شریفی، حمید (1396). اولویتبندی پیشرانهای پیچیدگی زنجیره تأمین با استفاده از فرایند تحلیل سلسلهمراتبی فازی. مدیریت صنعتی، 9 (1)، 79- 102.
10
ناظمی، شمسالدین؛ خریدار، فاطمه (1391). تأثیر ابعاد زنجیره تأمین یکپارچه بر توانمندیهای رقابتی در صنایع غذایی و آشامیدنی شهر مشهد. مطالعات مدیریت صنعتی، 9 (25)، 1- 26.
11
یعسوبی، عزیزاله؛ ربیعه، مسعود (1396). تحلیل دینامیکی مسئله نوسان موجودیها در زنجیره تأمین با رویکرد پویاییشناسی سیستمها. مدیریت صنعتی، 9(3)، 539- 561.
12
References
13
Alem Tabriz, A., Talaie, H., Moradi, E. (2013). Evaluating the Key Factors of Successful Implementation of World Class Manufacturing Using an Integrated Approach of Interpretive Structural Modeling(ISM), Graph Theory and Matrix Approach (GTMA): A Case Study for Iran Khodro and Saipa in Iran. Journal of industrial management, 5(1), 63-80. (in Persian)
14
Baofeng, H. (2012). The Impact of Supply Chain Integration of Company Performance, an Organizational Capability Perspective. Supply Chain Management: International Journal, 17(6), 596-610.
15
Baofeng, H., Yinan, Q., Zhiqiang, W. & Xiande, Z. (2014). Supply chain integration on firm performance. Supply Chain Management: International Journal, 19 (4), 384-369.
16
Bruque-Cámara, S., Moyano-Fuentes, J., Maqueira-Marín, J.M. (2016). Supply Chain Integration through community could: Affection operational performance. Journal of purchasing & supply chain management, 2 (22), 141-153.
17
Chopra, S., Meindl, P. (2010). Supply Chain Management Strategy, Planning and Operation (fourth Edition). Pearson Education Publishing as Prentice Hall. Cigdem, A., Anand, N. (2017). Assessment of supply chain integration and performance relationships: A meta-analytic investigation of the literature. International Journal of Production Economics, 185, 252-265.
18
Danese, P., Romano, P., Formentini, M. (2013). The impact of Supply chain integration on responsiveness: The moderating effect of using an international supplier network. Transportation Research, 49 (1), 125-140.
19
De Feliece, F. & Petrillo, A. (2015).Optimization of Manufacturing System through World Class Manufacturing. IFAC-Paper Online, 48 (3), 741-746.
20
Dogan, O.I. (2013). The Impact on the Operational Performance of World Class Manufacturing Strategies. International Journal of Business, Humanities and Technology, 3 (8), 141-149.
21
Fakoor Sagihe, A., Olfat, L., Feizi, K., Amiri, M. (2014) A model of Supply chain resilience for competitiveness in Iranian automotive companies. Journal of Production and operation Management, 5(1), 143-164. (in Persian)
22
Farsijani, H. (2012). World class production and operations methods. Samt, Tehran. (in Persian)
23
Farsijani, H., & Fallah Hoseini, A. (2012). Identifying and prioritizing the effective factors in the supply chain in order to achieve world class and appropriate methods. Journal of industrial management perspective, 1(6), 25-44. (in Persian)
24
Fianko, O.A., Annan, J., Quansah, E. (2013). Assessing the Challenges and Implementation of Supply Chain Integration in The Cocoa Industries: A factor farmer in Ashanti Region of Ghana. International Journal of Business and Social Science, 4(5), 112-123.
25
Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: a contingency and configuration approach. Journal of Operation Management, 28 (1), 58-71.
26
Fornell, C., Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18 (1), 39-50.
27
Giffi, C., Roth, A., Seal, G. (1990). Competing in world class manufacturing: America 21st. Homewood, IL: Business One Irwin.
28
Greene, A. (1991). Plant-wide systems: a world class perspective. Production Inventory Management, 11(7), 14-15.
29
Haleem, A., Sushil, QadrI, M. A. & Kumar, S. (2012). Analysis of Critical Success Factor of World Class Manufacturing Practices: An Application of Interpretative Structural Modeling and Interpretative Ranking Process. Production Planning & Control: The Management of Operations, 23(10-11), 722-734.
30
Hosseini Baharanchi, S.R. (2009). Investigation of the Impact of Supply Chain Integration on Product Innovation and Quality. Transaction E: Industrial Engineering, 16(1), 81-89.
31
Iranzade, S., Saraeenia, E. (2016). Developing a model to assess the strategic integration of supply chain with value creation approach (Case Study: Irankhodro Industrial Group supply chain management). Journal of Executive Management, 16(8), 87-110. (in Persian)
32
Khaled, A. F., Mohamad, Z. (2003). The role of Supply Chain Management in World Class manufacturing. International Journal of Physical Distribution & Logistics Management, 33 (5), 396-407.
33
Khatami firoozabadi, M., Olfat, L., Amir, M., Sharifi, H(2017) Prioritizing Supply Chain Complexity Drivers using Fuzzy Hierarchical Analytical Process, Journal of industrial management, 9(1),79-102. (in Persian)
34
Krishnaprya, V, Rupashree, B.(2014). Supply Chain Integration – A Competency based Perspective. International Journal of Managing value and Supply Chain, 5 (3), 45-60.
35
Lee, H.L., Hang, S. (2004). Business and Supply Chain Integration. Springer, New York.
36
Lind, J. (2001). Control in World Class Manufacturing- A Longitudinal Case Study. Management Accounting Research, 12(1), 41-74.
37
Mashayekhi, E., Alamtabriz, A. (2016). The impact of upstream and down stream supply chain integration on quality performance and plan.Journal of industrial management perspective, 4(6), 37-57. (in Persian)
38
Maskell, B. (1991). Performance measurement for world class manufacturing: part 3. Manufacturing System, 7 (9), 36-41.
39
Nazemi, S., Kharida, F. (2012). Impact of Supply chain integration on competitive capabilities in Food and Beverages Industries. Journal of Industrial Management Studies, 25(9), 1-26. (in Persian)
40
Rabieh, M., Yasoubi, A. (2017). Dynamic Analysis of Inventory Fluctuations in Supply Chain based on System Dynamics Approach. Journal of industrial management, 9(3), 561-539. (in Persian)
41
Sabet Motlagh, M., Mohaghar, A.(2016) Applying Genetic Algorithm for An integrated Supply and Production/Distribution Planning in assembly systems, Journal of industrial management, 8(2),163-190. (in Persian)
42
Salsabil, M., Shafia, M., Pishvaee, M., Shahanaghi, K. (2015). Tactical Planning of Three-Level Supply Chain considering Sanction Disruption and Shelf Life: A case Study of ATRA Drug Supply Chain, Journal of industrial management, 7(2), 305-332. (in Persian)
43
Sandeep, Attri, R.K., Panwar, N. (2016). Identification of barriers in implementation of world class manufacturing (wcm) practices: A Literature Analysis. International Research Journal of Engineering and Technology, 3 (5), 2363-2366.
44
Schonberger, R. J. (1986). World Class Manufacturing: The Lessons of Simplicity Applied. Free Press, New York.
45
Seied Hoseini, M., Soloukdar, A. (2007). Review the different perspectives on the dynamic model of World class Manufacturing. 5th International Management Conference, Tehran, Iran. (in Persian)
46
Sengupta, K., Daniel, R., & Loris, C. (2006). Manufacturing and Service Supply Chain Performance: A Comparative Analysis. The Journal of Supply Chain Management, 4(142), 5-16.
47
Sherry, A. (2016). Examination the impact of design for environment and themediating effect of quality management innovation on firm performance. International Journal of Production Economics, 25 (6), 142-152.
48
Stevens, G.S. (1989). Integrating The supply chain. International Journal of Physical Distribution and Material, 19(8), 3-8.
49
Swink, M., Narasimhan, R., Wang, C. (2007), Mnagingbeyond the factory walls: effect of four types of sterategic integration on manufacturing plant performance. Journal of Operations Management, 25, 148-164.
50
Venpouke, E., Vereecke, A., & Wetzels, M. (2014). Developing supplier integration capabilities for sustainable competitive advantage: A dynamic capabilities approach. Journal of Operations Management, 32 (7), 446-461.
51
Vikas, K., Esinaulo, N., Jose, A. (2017). The Impact of supply chain integration on Performance: Evidence from the UK food factor. 27th International Conference on Flexible Automation and Intelligent Manufacturing, Modena, Italy 27-30 June 2017. Procedia Mnufacturing11, 814-821.
52
Wong, C.Y., Boon-itt, S., Wong, C.W.Y. (2011). The Contingency Effect of Environmental Uncertainly on the Relationship between Supply Chain Integration and Operational Performance. Journal of Operations Management, 29 (6), 694-515.
53
Yu, W., Chavez, R., Feng, M., Wiengarten, F. (2014). Integrated green Supply Chain. Management and Operational Performance. Supply Chain Management: International Journal, 19 (5/6), 683-696.
54
ORIGINAL_ARTICLE
Inventory Control in multi-item Systems with Probable Demand Using Particle Swarm Algorithm (Case study: Novin Ghate Caspian Company)
Objective: Inventory control and orders planning are among the key issues in developing the economic policies of industrial units, which requires attention to the factors and conditions governing the organization and the market. In this context, an optimal balance among inventories, ordering costs and maintenance costs can have a crucial role in preventing the loss of capital and shortages in the inventories. The purpose of this paper is to control the inventory in multi-item systems under the conditions of probabilistic demand and warehouse limit.
Methods: The problem is studied by replacing the scheduling horizons with short-term periods in the general model of periodic orders which has solved the problem using the Particles swarm optimization algorithm.
Results: The results illustrated the connection of the inventory amount at two times of t-1 & t. The model's advantage is the dynamics of the general model of orders, especially in conditions of uncertainty in the business environment due to dramatic changes in the market conditions that are close to each other. This can make the general model of orders more dynamic and reflect the real conditions better and it can help managers determine the economic value at different times which is of high importance considering the limitations of definitive inventory control formulas. The model has been implemented on four different products in Novin Gateh Co.
Conclusion: Since in manufacturing organizations, due to the presence of raw materials, particles, and inventories in the process, the role of inventory control is more evident, the proposed model can be used to create a reliable stream of items and inventory of the organization taking into account the elements of time, location, quantity, quality and costs.
https://imj.ut.ac.ir/article_67516_e9b95030e0f74917fde1cdeb453416c7.pdf
2018-03-21
121
138
10.22059/imj.2018.247600.1007359
Particle Swarm Algorithm (PSO)
Inventory Control
Probabilistic demand
multi-item Systems
Orders planning
Mehrdad
Malekmohamadi
mehrdad.malekmohamadi@gmail.com
1
MSc. in Industrial Management, Faculty of Social Sciences, Imam Khomeini International University (IKIU), Qazvin, Iran
AUTHOR
Mahdi
Nasrollahi
m.nasrollahi@ut.ac.ir
2
Assistant Prof. in Industrial Management, Faculty of Social Sciences, Imam Khomeini International University (IKIU), Qazvin, Iran
LEAD_AUTHOR
Mohsen
Alvandi
mohsenalvandi@ikiu.ac.ir
3
Assistant Prof., Faculty of Social Sciences, Imam Khomeini International University (IKIU), Qazvin, Iran
AUTHOR
اصغریزاده، عزتالله (1379). مقدمهای بر خطمشیها و مدلهای وارانتی: مولود تازه مدیریت مهندسی و تولید. دانش مدیریت، 51(1)، 61-87.
1
ثابت مطلق، محمد؛ محقر، علی (1395). به کارگیری الگوریتم ژنتیک برای برنامهریزی تأمین، تولید و توزیع یکپارچه سیستمهای مونتاژ، مدیریت صنعتی، 8(2)، 163-190.
2
حاج شیر محمدی، علی (1395). اصول برنامهریزی و کنترل تولید و موجودیها. اصفهان، انتشارات ارکان.
3
رضایی صدرآبادی، زهرا؛ طالبی، داود (1390). ارائه یک مدل کنترل موجودی دوسطحی (R,Q) و حل آن با الگوریتم های ژنتیک و رقابت استعماری. چشمانداز مدیریت صنعتی، 2(1)، 79-92.
4
رنجبران، هادی (1395). آمار و احتمال و کاربرد آن در مدیریت و حسابداری. تهران، انتشارات اثبات.
5
طالعی زاده، عطا؛ صالحی، علی (1394). مدل کنترل موجودی با طول دوره بازپرسازی تصادفی و پرداخت معوقه برای کالاهای فسادپذیر. پژوهشهای مهندسی صنایع در سیستمهای تولید، 3(5)، 13-25.
6
عندلیب، علیرضا (1393). روش تدوین پایاننامه کارشناسی ارشد و دکتری. تهران، انتشارات آذرخش.
7
کاظمی، ابوالفضل؛ ملکیان، محمدرضا؛ صرافها، کیوان (1391). ارائه یک مدل جدید کنترل موجودی مقدار تولید اقتصادی (EPQ) چند کالایی با تقاضای فازی تصادفی. نشریه مهندسی صنایع، 46 (1)، 53-62.
8
کاوسی داودی، سیدمجتبی؛ بالاوند، علیرضا؛ نجفی، اسماعیل (۱۳۹۳). تعیین مقدار بهینه سفارش سیستمهای کنترل موجودی با تقاضای دینامیک تک محصولی توسط الگوریتم PSO. کنفرانس بینالمللی توسعه و تعالی کسبوکار، تهران.
9
منعم، محمد جواد؛ نوری، محمد علی (1389). کاربرد الگوریتم بهینهسازی PSO در توزیع و تحویل بهینه آب در شبکههای آبیاری، مجله آبیاری و زهکشی ایران، 4 (1)، 73-82.
10
وکیلی، پریزاد؛ حسینی مطلق، سید مهدی؛ غلامیان، محمدرضا؛ جوکار، عباس (1396). ارائه مدل ریاضی مسیریابی موجودی چند محصوله برای اقلام دارویی در زنجیره تأمین سرد و روش حل ابتکاری مبتنی بر جست وجوی همسایگی انطباقی، مدیریت صنعتی، 9(2)، 383-407.
11
References
12
Andalib, A. (2013). Method of compiling Master's thesis and Ph.D. Tehran, Azarakhsh Press. (in Persian)
13
Asgharizadeh, E. (2000). Introduction to Warranty policies and models: newly born in engineering and production management. Quarterly Journal of management knowledge, 51(1), 61-87. (in Persian)
14
Axsäter, S. (2013). Initiation of an inventory control system when the demand starts at a given time. Journal of Production Economics, 143(2), 553–556.
15
Babbie, E.R. (2007). The Practice of Social Research.10thedition. Wadsworth, Thomson Learning Inc.
16
Dutta, P., Chakraborty, D. & Roy, A.R. (2007). Continuous review inventory model in mixed fuzzy and stochastic environment. Journal of Applied Mathematics and Computation, 188(1), 970–980.
17
Haj Shirmohammadi, A. (2015). Principles of production and inventory management. Isfahan, Arkan Press. (in Persian)
18
Kavoosi, M., Balavand, A., & Najafi, A. (2014). Determining the optimal order quantity in inventory control system with single-product dynamical demand by the PSO algorithm. International conference on business development and excellence, Tehran, Iran.
19
(in Persian)
20
Kazemi, A., Malekian, M. R. & Sarrafha, K. (2012). Presenting a New Model for Inventory Control of Multi-item Economic Production Quantity (EPQ) with Fuzzy Random Demand. Journal of Industrial Engineering, 46(1), 53-62. (in Persian)
21
Monem, M.J., & Nouri, M.A. (2010). Application of PSO Method for Optimal Water Delivery in Irrigation Networks. Iranian Journal of irrigation and drainage, 1(4), 73-82.
22
(in Persian)
23
Pervin, M., Roy, S. K., & Weber, G. W. (2018). Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration. Annals of Operations Research, 260(1-2), 437-460.
24
Petrovic, R., & Petrovic, D., (2001). Multi criteria ranking of inventory replenishment policies in the presence of uncertainty in customer demand. International Journal of Production Economic, 71(3), 439-446.
25
Ranjbaran, H. (2016). Statistics and probability, its application in management and accounting. Tehran, Esbat Press. (in Persian)
26
Rego, M. & Mesquita, A. (2015). Demand forecasting and inventory control: A simulation study on automotive spare parts. International Journal of Production Economics, 161(1), 1-16.
27
Rezaei Sadrabadi, Z., & Talebi, D. (2011). Presenting a two-level inventory control model (R, Q) and solving it with genetic algorithms and colonial competition. Journal of Industrial Management Perspective, 2(1), 79-92. (in Persian)
28
Rizkya, I., Syahputri, K., Sari, R. M., Siregar, I., & Ginting, E. (2018, January). Comparison of Periodic Review Policy and Continuous Review Policy for the Automotive Industry Inventory System. In IOP Conference Series: Materials Science and Engineering (Vol. 288, No. 1, p. 012085). IOP Publishing.
29
Roya, A., Maityb, K., karc, S. & Maitid, M. (2009). A production–inventory model with remanufacturing for defective and usable items in fuzzy-environment. Journal of Computers & Industrial Engineering, 56(1), 87–96.
30
Sabet Motlagh, M. & Mohaghar, A. (2016). Applying Genetic Algorithm for An integrated Supply and Production/Distribution Planning in assembly systems. Industrial Management Journal, 8(2), 163-190. (in Persian)
31
Taleizadeh, A, & Salehi, A. (2015). Inventory Control Model with Stochastic Replenishment Period Length and Delayed Payment for Deteriorating Item. Journal of Industrial Engineering Research in Production Systems, 3(5), 13-25. (in Persian)
32
Vakili, P., Hosseini-Motlagh, S.M., Gholamian, M.R. & Jokar, A. (2017). A developed model and heuristic algorithm for inventory routing problem in a cold chain with pharmaceutical products. Industrial Management Journal, 9(2), 383-407. (in Persian)
33
Wacker, J.G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16, 361–385.
34
Zhou, W., Chen, L. & Ming Ge, H., (2013). A multi production-echelon inventory control model with joint replenishment strategy. Applied Mathematical Modeling, 37(4), 2039-2050.
35
ORIGINAL_ARTICLE
Hybrid Rule-Based Decision Support System to the EOQ Problem in the Form of Posynomial Geometric Programming Formulation with Linear Constraints
Objective: The main objective of this paper is to solve theeconomic order quantity problem, which is formulated as a hybrid posynomial geometric programming, using a rule-based decision support system. Avoiding the complexities of the optimization process problems and using the optimum knowledge to build an inference system, which is easier to understand for the decision makers, are the main features of this article.
Methods: The main approach taken in this paper is to use uncertain decision variables, extracting the optimal knowledge through the hybrid optimization problem and applying this knowledge to design a hybrid inference system.
Results: The developed hybrid inference system was applied to 100 random problems and inferred values of the objective function as well as decision variables were compared to the obtained optimum values. Alike decision variables, more than 97% of the deviations between inferred and optimum values for objective function are less than 2%. These results indicated that the developed hybrid inference system is highly efficient to be implemented as an optimized decision support system and its results are quite reliable.
Conclusion: Unlike other works in the literature, in this paper, the optimization problem is not replaced with a rule-base which is presented by group of experts. But, an approach is provided to build the optimal rule-based decision support system in which the optimum knowledge is obtained through an optimization problem. This approach will provide decision makers with all optimal decisions that may be needed in the future by replacing the optimal deterministic values for decision variables with the optimal hybrid distribution.
https://imj.ut.ac.ir/article_67514_bfff7bc2af2e45b13f12e2b73d041435.pdf
2018-03-21
139
160
10.22059/imj.2018.245233.1007339
Economic order quantity
Hybrid geometric programming
Rule- based inference system
Uncertain decision variable
Hybrid rule base
Amir
Yousefli
yousefli@soc.ikiu.ac.ir
1
Assistant Prof. of Industrial Management, Social Science Department, Imam Khomeini International University, Qazvin, Iran
LEAD_AUTHOR
علامه، غزاله؛ اسمعیلی، مریم؛ تجویدی، ترانه (1393). توسعه چندین مدل قیمتگذاری در زنجیره تأمین سبز تحت ریسک با رویکرد نظریه بازیها. فصلنامه مدیریت صنعتی، 6 (4)، 767-789.
1
فارسیجانی، حسن؛ عبدوس، محمدرضا (1390). استفاده از مدلهای فازی در سیستمهای سفارشدهی کنترل موجودی. فصلنامه مدیریت صنعتی، 6 (14)، 99-112. جعفرنژاد، احمد؛ آذر، عادل؛ ابراهیمی، سید عباس (1395). طراحی مدل ریاضی مدیریت سفارشات زنجیرۀ تأمین با تکیه بر رویکرد بهینهسازی استوار و ساختار هزینهیابی بر مبنای فعالیت. فصلنامه مدیریت صنعتی، 8 (3)، 333-358.
2
References
3
Alinovi, A., Eleonora B., Roberto, M. (2012). Reverse Logistics: a stochastic EOQ-based inventory control model for mixed manufacturing/remanufacturing systems with return policies. International Journal of Production Research, 50 (5), 1243-1264.
4
Allameh, G., Esmaeili, M., Tajvidi, T. (2014). Developing several pricing models in green supply chain under risk by Game Theory Approach. Journal of Industrial Management, 6 (4), 767-789. (in Persian)
5
Beheshti, H. M. (2010). A decision support system for improving performance of inventory management in a supply chain network. International Journal of Productivity and Performance Management, 59(5), 452-467.
6
Bushuev, M. A., Guiffrida, A., Jaber, M. Y., Khan, M. (2015). A review of inventory lot sizing review papers. Management Research Review, 38(3), 283-298.
7
Carlsson, C., Fuller, R. (1994a). Fuzzy if-then rules for modeling interdependencies in FMOP problems, in: Proceedings of EUFIT’94 Conference, Aachen, Germany, Verlag der Augustinus Buchhandlung, 1504-1508.
8
Carlsson, C., Fuller, R. (1994b). Fuzzy reasoning for solving fuzzy multiple objective linear programs, in: R. Trappl ed., Cybernetics and Systems ’94, Proceedings of the Twelfth European Meeting on Cybernetics and Systems Research, World Scientific Publisher, London, 1: 295-301.
9
Carlsson, C., Fuller, R. (1998a). Multiobjective optimization with linguistic variables, in: Proceedings of the Sixth European Congress on Intelligent Techniques and Soft Computing (EUFIT’98), Aachen, Verlag Mainz, Aachen, 2, 1038-1042.
10
Carlsson, C., Fuller, R. (1998b). Optimization with linguistic values. TUCS Technical Reports, Turku Centre for Computer Science. Available in: http://uni-obuda.hu/users/fuller.robert/ TR157.pdf.
11
Carlsson, C., Fuller, R. (2000). Multi objective linguistic optimization, Fuzzy sets and systems, 115, 5-10.
12
De, L. N., Goswami, A. (2009). Probabilistic EOQ model for deteriorating items under trade credit financing. International Journal of Systems Science, 40(4), 335–346.
13
De, S. K., Sana, S. S. (2015). Multi-criterion multi-attribute decision-making for an EOQ model in a hesitant fuzzy environment. Pacific Science Review A: Natural Science and Engineering, 17(2), 61-68.
14
Dutta, P., Chakraborty, D., Roy, A. R. (2005). A single-period inventory model with fuzzy random variable demand. Mathematical and Computer Modelling, 41 (8-9), 915–922.
15
Dutta, P., Chakraborty, D., Roy, A. R. (2007). Continuous review inventory model in mixed fuzzy and stochastic environment. Applied Mathematics and Computation, 188 (1), 970–980.
16
Eynan, A., Kropp, D.H. (2007). Effective and simple EOQ-like solutions for stochastic demand periodic review systems. European Journal of Operational Research, 180(3), 1135-1143.
17
Farsijani, F., Abdoos, M.R. (2011), Using the Fuzzy Models for Ordering System in Inventory Control, Journal of Industrial Management, 3 (6), 99-112. (in Persian)
18
Friedman, M. F. (1984). On a stochastic extension of the EOQ formula. European Journal of Operational Research, 17 (1), 125–127.
19
Hayya, J. C., Harrison, T. P., Chatfield, D. C. (2009). A solution for the intractable inventory model when both demand and lead time are stochastic. International Journal of Production Economics, 122 (2), 595–605.
20
Jafanejad, A., Azar, A., Ebrahimi, S.A. (2016). Mathematical Model of Supply Chain Order Management Relying on Robust Optimization and Activity-Based Costing. Journal of Industrial Management, 8 (3), 333-358. (in Persian)
21
Kalantari, H., Yousefli, A., Ghazanfari, M., Shahanaghi, K. (2014). Fuzzy transfer point location problem: a possibilistic unconstrained nonlinear programming approach. The International Journal of Advanced Manufacturing Technology, 70 (5-8), 1043-1051.
22
Khan, M., Jaber, M. Y., Guiffrida, A. L., Zolfaghari, S. (2011). A review of the extensions of a modified EOQ model for imperfect quality items. International Journal of Production Economics, 132 (1), 1–12.
23
Lee, W. C., Wu, J. W. (2002). An EOQ model for items with Weibull distributed deterioration, shortages and power demand pattern, International Journal of Information and Management Sciences, 13 (2), 19–34.
24
Liu, B. (2008). Theory and practice of uncertain programming (second edition). Springer- Verlag.
25
Maddah, B., & Noueihed, N. (2017). EOQ holds under stochastic demand, a technical note. Applied Mathematical Modelling, 45, 205-208.
26
Mondal, S., Maiti, M. (2003), Multi-item fuzzy EOQ models using genetic algorithm. Computers and Industrial Engineering, 44 (1), 105–117.
27
Muriana, C. (2016). An EOQ model for perishable products with fixed shelf life under stochastic demand conditions. European Journal of Operational Research, 255(2), 388-396.
28
Omrani, H., Keshavarz, M. (2014). An interval programming approach for developing economic order quantity model with imprecise exponents and coefficients. Applied Mathematical Modelling, 38(15), 3917-3928.
29
Panda, D., Kar, S., Maiti, M. (2008). Multi-item EOQ model with hybrid cost parameters under fuzzy/fuzzy-stochastic resource constraints: a geometric programming approach. Computers and Mathematics with Applications, 56 (11), 2970–2985.
30
Park, K. S. (1987). Fuzzy-set theoretic interpretation of economic order quantity, IEEE Transactions on Systems, Man and Cybernetics, 17 (6), 1082–1084.
31
Pentico, D. W., Drake, M. J. (2011). A survey of deterministic models for the EOQ and EPQ with partial backordering. European Journal of Operational Research, 214 (2), 179–198.
32
Pereira, V., Costa, H. G. (2015). A literature review on lot size with quantity discounts: 1995-2013. Journal of Modelling in Management, 10 (3), 341-359.
33
Render, B., Stair Jr, R. M., & Hanna, M. E. (2009). Quantitative Analysis for management (10th ed.). Pearson Education, Upper Saddle River, NJ.
34
Roy, T. K., Maiti, M. (1997). A fuzzy EOQ model with demand dependent unit cost under limited storage capacity. European Journal of Operational Research, 99 (2), 425–432.
35
Sadjadi, S. J., Ghazanfari, M, Yousefli, A. (2010). Fuzzy pricing and marketing planning model: A possibility geometric programming approach. Expert Systems with Applications, 37 (4), 3392-3397.
36
Samanta, B., Al-Araimi, S. A. (2001). An inventory control model using fuzzy logic, International Journal of Production Economics, 73 (3), 217–226.
37
Sana, S. S. (2011). The stochastic EOQ model with random sales price, Applied Mathematics and Computation, 218 (2), 239–248.
38
Waliv, R. H., Hemant, P. U. (2016). Fuzzy stochastic inventory model for deteriorating item. Yugoslav Journal of Operations Research, 27(1), 91-97.
39
Wang, C. H. (2010). Some remarks on an optimal order quantity and reorder point when supply and demand are uncertain. Computers and Industrial Engineering, 58 (4), 809– 813.
40
Wang, X., Tang, W., Zhao, R. (2007). Random fuzzy EOQ model with imperfect quality items. Fuzzy Optimization and Decision Making, 6 (2), 139–153.
41
Yousefli, A., Ghazanfari, M., & Abiri, M. B. (2014). An Integrated Model for Optimization Oriented Decision Aiding and Rule Based Decision Making in Fuzzy Environment. Journal of Fuzzy Set Valued Analysis, 2014, 1-13.
42
Yousefli, A., Kalantari, H., & Ghazanfari, M. (2018). Stochastic transfer point location problem: A probabilistic rule-based approach. Uncertain Supply Chain Management, 6(1), 65-74.
43
Yu, G. (1997). Robust economic order quantity models. European Journal of Operational Research, 100 (3), 482-493.
44
علامه، غزاله؛ اسمعیلی، مریم؛ تجویدی، ترانه (1393). توسعه چندین مدل قیمتگذاری در زنجیره تأمین سبز تحت ریسک با رویکرد نظریه بازیها.فصلنامه مدیریت صنعتی، 6 (4)، 767-789.
45
فارسیجانی، حسن؛ عبدوس، محمدرضا (1390). استفاده از مدلهای فازی در سیستمهای سفارشدهی کنترل موجودی. فصلنامه مدیریت صنعتی، 6 (14)، 99-112. جعفرنژاد، احمد؛ آذر، عادل؛ ابراهیمی، سید عباس (1395). طراحی مدل ریاضی مدیریت سفارشات زنجیرۀ تأمین با تکیه بر رویکرد بهینهسازی استوار و ساختار هزینهیابی بر مبنای فعالیت. فصلنامه مدیریت صنعتی، 8 (3)، 333-358.
46
References
47
Alinovi, A., Eleonora B., Roberto, M. (2012). Reverse Logistics: a stochastic EOQ-based inventory control model for mixed manufacturing/remanufacturing systems with return policies. International Journal of Production Research, 50 (5), 1243-1264.
48
Allameh, G., Esmaeili, M., Tajvidi, T. (2014). Developing several pricing models in green supply chain under risk by Game Theory Approach. Journal of Industrial Management, 6 (4), 767-789. (in Persian)
49
Beheshti, H. M. (2010). A decision support system for improving performance of inventory management in a supply chain network.International Journal of Productivity and Performance Management,59(5), 452-467.
50
Bushuev, M. A., Guiffrida, A., Jaber, M. Y., Khan, M. (2015). A review of inventory lot sizing review papers.Management Research Review,38(3), 283-298.
51
Carlsson, C., Fuller, R. (1994a). Fuzzy if-then rules for modeling interdependencies in FMOP problems, in: Proceedings of EUFIT’94 Conference, Aachen, Germany, Verlag der Augustinus Buchhandlung, 1504-1508.
52
Carlsson, C., Fuller, R. (1994b). Fuzzy reasoning for solving fuzzy multiple objective linear programs, in: R. Trappl ed., Cybernetics and Systems ’94, Proceedings of the Twelfth European Meeting on Cybernetics and Systems Research, World Scientific Publisher, London, 1: 295-301.
53
Carlsson, C., Fuller, R. (1998a). Multiobjective optimization with linguistic variables, in: Proceedings of the Sixth European Congress on Intelligent Techniques and Soft Computing (EUFIT’98), Aachen, Verlag Mainz, Aachen, 2, 1038-1042.
54
Carlsson, C., Fuller, R. (1998b). Optimization with linguistic values. TUCS Technical Reports, Turku Centre for Computer Science. Available in: http://uni-obuda.hu/users/fuller.robert/ TR157.pdf.
55
Carlsson, C., Fuller, R. (2000). Multi objective linguistic optimization, Fuzzy sets and systems, 115, 5-10.
56
De, L. N., Goswami, A. (2009). Probabilistic EOQ model for deteriorating items under trade credit financing. International Journal of Systems Science, 40(4), 335–346.
57
De, S. K., Sana, S. S. (2015). Multi-criterion multi-attribute decision-making for an EOQ model in a hesitant fuzzy environment.Pacific Science Review A: Natural Science and Engineering,17(2), 61-68.
58
Dutta, P., Chakraborty, D., Roy, A. R. (2005). A single-period inventory model with fuzzy random variable demand. Mathematical and Computer Modelling, 41 (8-9), 915–922.
59
Dutta, P., Chakraborty, D., Roy, A. R. (2007). Continuous review inventory model in mixed fuzzy and stochastic environment. Applied Mathematics and Computation, 188 (1), 970–980.
60
Eynan, A., Kropp, D.H. (2007). Effective and simple EOQ-like solutions for stochastic demand periodic review systems. European Journal of Operational Research, 180(3), 1135-1143.
61
Farsijani, F., Abdoos, M.R. (2011), Using the Fuzzy Models for Ordering System in Inventory Control, Journal of Industrial Management, 3 (6), 99-112. (in Persian)
62
Friedman, M. F. (1984). On a stochastic extension of the EOQ formula. European Journal of Operational Research, 17 (1), 125–127.
63
Hayya, J. C., Harrison, T. P., Chatfield, D. C. (2009). A solution for the intractable inventory model when both demand and lead time are stochastic. International Journal of Production Economics, 122 (2), 595–605.
64
Jafanejad, A., Azar, A., Ebrahimi, S.A. (2016). Mathematical Model of Supply Chain Order Management Relying on Robust Optimization and Activity-Based Costing. Journal of Industrial Management, 8 (3), 333-358. (in Persian)
65
Kalantari, H., Yousefli, A., Ghazanfari, M., Shahanaghi, K. (2014). Fuzzy transfer point location problem: a possibilistic unconstrained nonlinear programming approach. The International Journal of Advanced Manufacturing Technology, 70 (5-8), 1043-1051.
66
Khan, M., Jaber, M. Y., Guiffrida, A. L., Zolfaghari, S. (2011). A review of the extensions of a modified EOQ model for imperfect quality items. International Journal of Production Economics, 132 (1), 1–12.
67
Lee, W. C., Wu, J. W. (2002). An EOQ model for items with Weibull distributed deterioration, shortages and power demand pattern, International Journal of Information and Management Sciences, 13 (2), 19–34.
68
Liu, B. (2008). Theory and practice of uncertain programming (second edition). Springer- Verlag.
69
Maddah, B., & Noueihed, N. (2017). EOQ holds under stochastic demand, a technical note. Applied Mathematical Modelling, 45, 205-208.
70
Mondal, S., Maiti, M. (2003), Multi-item fuzzy EOQ models using genetic algorithm. Computers and Industrial Engineering, 44 (1), 105–117.
71
Muriana, C. (2016). An EOQ model for perishable products with fixed shelf life under stochastic demand conditions. European Journal of Operational Research, 255(2), 388-396.
72
Omrani, H., Keshavarz, M. (2014). An interval programming approach for developing economic order quantity model with imprecise exponents and coefficients.Applied Mathematical Modelling,38(15), 3917-3928.
73
Panda, D., Kar, S., Maiti, M. (2008). Multi-item EOQ model with hybrid cost parameters under fuzzy/fuzzy-stochastic resource constraints: a geometric programming approach. Computers and Mathematics with Applications, 56 (11), 2970–2985.
74
Park, K. S. (1987). Fuzzy-set theoretic interpretation of economic order quantity, IEEE Transactions on Systems, Man and Cybernetics, 17 (6), 1082–1084.
75
Pentico, D. W., Drake, M. J. (2011). A survey of deterministic models for the EOQ and EPQ with partial backordering. European Journal of Operational Research, 214 (2), 179–198.
76
Pereira, V., Costa, H. G. (2015). A literature review on lot size with quantity discounts: 1995-2013.Journal of Modelling in Management,10 (3), 341-359.
77
Render, B., Stair Jr, R. M., & Hanna, M. E. (2009). Quantitative Analysis for management (10th ed.). Pearson Education, Upper Saddle River, NJ.
78
Roy, T. K., Maiti, M. (1997). A fuzzy EOQ model with demand dependent unit cost under limited storage capacity. European Journal of Operational Research, 99 (2), 425–432.
79
Sadjadi, S. J., Ghazanfari, M, Yousefli, A. (2010). Fuzzy pricing and marketing planning model: A possibility geometric programming approach. Expert Systems with Applications, 37 (4), 3392-3397.
80
Samanta, B., Al-Araimi, S. A. (2001). An inventory control model using fuzzy logic, International Journal of Production Economics, 73 (3), 217–226.
81
Sana, S. S. (2011). The stochastic EOQ model with random sales price, Applied Mathematics and Computation, 218 (2), 239–248.
82
Waliv, R. H., Hemant, P. U. (2016). Fuzzy stochastic inventory model for deteriorating item. Yugoslav Journal of Operations Research, 27(1), 91-97.
83
Wang, C. H. (2010). Some remarks on an optimal order quantity and reorder point when supply and demand are uncertain. Computers and Industrial Engineering, 58 (4), 809– 813.
84
Wang, X., Tang, W., Zhao, R. (2007). Random fuzzy EOQ model with imperfect quality items. Fuzzy Optimization and Decision Making, 6 (2), 139–153.
85
Yousefli, A., Ghazanfari, M., & Abiri, M. B. (2014). An Integrated Model for Optimization Oriented Decision Aiding and Rule Based Decision Making in Fuzzy Environment. Journal of Fuzzy Set Valued Analysis, 2014, 1-13.
86
Yousefli, A., Kalantari, H., & Ghazanfari, M. (2018). Stochastic transfer point location problem: A probabilistic rule-based approach. Uncertain Supply Chain Management, 6(1), 65-74.
87
Yu, G. (1997). Robust economic order quantity models. European Journal of Operational Research, 100 (3), 482-493.
88