ORIGINAL_ARTICLE
Designing and selecting the optimal design in terms of risks in new product development
New Product Development (NPD) is one of the key factors for achieving competitive advantage and maintaining firm growth. Therefore, given the importance of this type of projects, this paper is an endeavor to make these projects more successful by effectively managing their risks in the conceptual design phase of new product development. For this purpose, a multi-objective model, with three objective functions, including risk, overall effectiveness of the design and cost, has been developed in this study. Among the innovations and features of this model, one can refer to its considering the mutual effect of risks to the outcomes of one another, as well as its taking into consideration the risks associated with the continuous design variables in calculating the risk measure of product design, which will ultimately lead to an increase in the accuracy of risk measure calculation for each product design.
https://imj.ut.ac.ir/article_50694_1f728556a7a52f7440c457e45d043b08.pdf
2016-04-20
1
22
10.22059/imj.2016.50694
New product development
risk
Effectiveness
Multi-objective optimization
NSGA-II
Adel
Azar
azara@modares.ac.ir
1
استاد گروه مدیریت صنعتی، دانشگاه تربیت مدرس، تهران، ایران
AUTHOR
Jafar
Ghaidar kheljani
kheljani@mut.ac.ir
2
استادیار گروه مدیریت سیستم و بهرهوری، دانشگاه صنعتی مالک اشتر، تهران، ایران
AUTHOR
Seyyed Mojtaba
Hashemi Majoumerd
mojtaba.hashemi@modares.ac.ir
3
کارشناسارشد مدیریت صنعتی، دانشگاه تربیت مدرس، تهران، ایران
LEAD_AUTHOR
Avazkhah, H. & Mohebi, A. (2009). Project risk management, Vol. 1, Tehran: Kian Rayaneh. (in Persian)
1
Blanchard, B. S. (2012). System engineering management (Vol. 64): Wiley. com.
2
Brown, A. & Mierzwicki, T. (2004b). Risk Metric for Multi‐Objective Design of Naval Ships. Naval Engineers Journal, 116(2): 55-72.
3
Brown, A. & Thomas, L. M. (1998). Reengineering the Naval Ship Concept Design Process, From Research to Reality. in Ship Systems Engineering Symposium, ASNE.
4
Chang, K.L. (2013). Combined MCDM approaches for century-old Taiwanese food firm new product development project selection. British Food Journal, 115(8): 8-8.
5
Chiang, T.A. & Che, Z. (2010). A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA. Expert Systems with Applications, 37(11): 7408-7418.
6
Chin, K.S., Tang, D.-W., Yang, J. B., Wong, S. Y. & Wang, H. (2009). Assessing new product development project risk by Bayesian network with a systematic probability generation methodology. Expert Systems with Applications, 36(6): 9879-9890.
7
Choi, H.G. & Ahn, J. (2010). Risk analysis models and risk degree determination in newproduct development: A case study. Journal of Engineering and Technology Management, 27(1): 110-124.
8
College, D. S. M. (2006). Risk Management Guide for DOD Acquisition (6th ed.): Defense Acquisition University Press.
9
Cooper, L. P. (2003). A research agenda to reduce risk in new product development through knowledge management: a practitioner perspective. Journal of Engineering and Technology Management, 20(1): 117-140.
10
Cooper, R. G. & Kleinschmidt, E. J. (1995). Benchmarking the firm's critical success factors in new product development. Journal of Product Innovation Management, 12(5): 374-391.
11
Demko, D. (2005). Tools for multi-objective and multi-disciplinary optimization in naval ship design. Virginia Polytechnic Institute and State University.
12
Dorri, B. & Hamzehei, E. (2010). Determining the Best Responding Strategy to Project Risk Using ANP Technique (Case Study: North Azadegan Oil Field Development Project). Journal of Industrial Management, (4): 77-94.
13
(in Persian)
14
Hartley, R. F. (2011). Marketing mistakes and successes. John Wiley and Sons.
15
Jafarnejad, A. & Yousofi Zenooz, R. (2008). A Fuzzy Model of Ranking Risks at Petropars Company’s Excavation of Oil Well Projects. Journal of Industrial Management, 1(1): 21-38. (in Persian)
16
Jerrard, R. N., Barnes, N. & Reid, A. (2008). Design, risk and new product development in five small creative companies. International Journal of Design, 2(1): 21-30.
17
Kwak, Y. H. & LaPlace, K. S. (2005). Examining risk tolerance in project-driven organization. Technovation, 25(6): 691-695.
18
Moeiniaghkalriz, M. (2008). Complexity management in compelex new products and systems developement projects. (M.S.). Tehran: Sharif University of Technology. (in Persian)
19
PMI. (2009). Project Management Body of Knowledge (PMBOK). M. Z. Ashtiani, Trans. 4 ed. Tehran: Adineh. (in Persian)
20
Sabaghchi, S. (2011). Selection of knowledge management tools in software new product development process. (M.S). Tehran: Tarbiat modares univercity.
21
(in Persian)
22
Wang, J. & Lin, Y.I. (2009). An overlapping process model to assess schedule risk for new product development. Computers & Industrial Engineering, 57(2): 460-474.
23
Wei, C.C. & Chang, H.W. (2011). A new approach for selecting portfolio of new product development projects. Expert Systems with Applications, 38(1): 429-434.
24
ORIGINAL_ARTICLE
Prioritizing organization’s quality management projects starting with customer expectations
Customer satisfaction level depends on level that his expectations would be answered. With identifying customer expectations and implementation them in QFD, this ensures that important and critical demands of customer have been considered. QFD have been used and use for translating customer expectations to different subjects. What has been considered in this study, is QFD usage for translating expectation customers to importance mark of quality management projects. Those project that guarantee organization success for meeting customer needs. For this purpose, three matrix approach of QFD was used in “Mashhad urban and suburban railway company” that had concern about ranking of quality management projects. In first matrix, importance marks of technical specifications were identified. In second matrix, importance marks of key operations were identified and in third matrix, execution importance and priority of projects were identified. Study results depicts that “need and task assessment of jobs” has most role in satisfying of customer needs and is first priority for this company.
https://imj.ut.ac.ir/article_59592_a8d6ce16a87a587d9db4cc85274a83d2.pdf
2016-04-20
23
42
10.22059/imj.2016.59592
Quality Management Project
quality function deployment
QFD
Urban Transportation
Amir
Daneshmand
amirdaneshmand20@gmail.com
1
کارشناسارشد مدیریت، پردیس بینالملل، دانشگاه فردوسی مشهد، مشهد، ایران
AUTHOR
Shamsodin
Nazemi
nazemi_shm@um.ac.ir
2
Ferdowsi University of Mashhad
AUTHOR
Nasser
Motahari
n.motahari@um.ac.ir
3
عضو هیئت علمی
LEAD_AUTHOR
Athanassopoulos, A. D. (1997). Embodying service quality into operating efficiency for assessing the effort effectiveness in the provision of financial services. European Journal of Operational Research, 98(2): 300-313.
1
Azar, A., S. Jokar & Zangooeinezhad, A. (2010). Compilation of Research & Development Strategy using Technology Quality Function Deployment: Market Pull Approach. Industrial Management, 2(4): 3-18. (in Persian)
2
Bolton, R. & Drew, J.H. (1991). A multistage model of customers’ assessment of service quality and value. Journal of Consumer Research, 17(4): 375-84.
3
Chien, T. K. & Su, C. T. (2003). Using the QFD concept to resolve customer satisfaction strategy decisions. International Journal of Quality & Reliability Management, 20(3): 345-359.
4
Farsijani, H. & M. A. Torabandeh. (2013). Explaining the Role of Transferring Technology in Fuzzy QFD for Competitiveness of Product (Case Study: Iran Transfo Rey Corporation). Industrial Management, 5(2):103-120. (in Persian)
5
Ghianpour, H., Zeinalypour, H. & Ahmadi, R. (2012). Using QFD in converting expectations of in-service training customers to training requirements. Researchs of Public Management, (15): 85-112. (in Persian)
6
González, M. (2001). Quality function deployment: A road for listening to customer expectations. Mexico, DF: McGraw Hill.
7
González, M. E., Quesada, G., Picado, F. & Eckelman, C. A. (2004). Customer satisfaction using QFD: an e-banking case. Managing Service Quality: An International Journal, 14(4): 317-330.
8
González, M., Quesada, G. & Bahill, T. (2003). Improving product design using quality function deployment: the school furniture case in developing countries. Quality Engineering Journal, 16(1): 47-58.
9
Herrmann, A., Huber, F. & Braunstein, C. (2000). Market-driven product and service design: Bridging the gap between customer needs, quality management, and customer satisfaction. International Journal of production economics, 66(1): 77-96.
10
Jiang, Z., Fan, Z., Sutherland, J. W., Zhang, H. & Zhang, X. (2014). Development of an optimal method for remanufacturing process plan selection. The International Journal of Advanced Manufacturing Technology, 72(9-12): 1551-1558.
11
Kandampully, J. & Duddy, R. (1999). Competitive advantage through anticipation, innovation and relationships. Management Decision, 37(1): 51-56.
12
Ko, W. C. & Chen, L. H. (2014). An approach of new product planning using quality function deployment and fuzzy linear programming model. International Journal of Production Research, 52(6): 1728-1743.
13
Kwong, C. K., Chen, Y. & Chan, K. Y. (2011). A methodology of integrating marketing with engineering for defining design specifications of new products. Journal of engineering Design, 22(3): 201-213.
14
Liu, Y., Zhou, J. & Chen, Y. (2014). Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD. Neurocomputing, 142: 115-124.
15
Mehregan, M.R., Modares Yazdi, M., Hasangholipoor, T., Safari, H. & Dehghan Nazari, M. (2013). Multi Criteria Satisfaction Analysis: Employing and Weak Points of MUSA in Practice (Case of Banking Industry). Industrial Management, 5(1): 139-163. (in Persian)
16
Parasuraman, A., Berry, L.L. & Zeithaml, V.A. (1991). Perceived service quality as a customer-focused performance measure: an empirical examination of organizational barriers using and extended service quality model. Human Resource Management, 30(3): 335-364.
17
Park, H. S. & Noh, S. J. (2003). Enhancement of web design quality through the QFD approach. Quality control and applied statistics, 48(3): 341-342.
18
Shahin, A., Vaez Shahrestani, H. & Bagheri Iraj, E. (2014). Proposing an integrated approach of Kano Model and Taguchi Design of Experiments based on Kansei Engineering to product design based on customer needs in the automotive industry. Industrial Management, 6(2): 317-336. (in Persian)
19
Zhong, S., Zhou, J. & Chen, Y. (2014). Determination of target values of engineering characteristics in QFD using a fuzzy chance-constrained modelling approach. Neurocomputing, 142: 125-135.
20
ORIGINAL_ARTICLE
Analyzing Energy Consumption of Organizational Buildings Using Grey Set Theory
In particular, by identifying clusters of Individuals, households, organizations, cities, countries and nationalities with similar behavioural patterns, it can assist in the crafting of more effective interventions and incentives targeted to specific energy cultures. it also helps energy supply companies understand different behavioural clusters among their customers, so as to better tailor their tariff schemes and products. The purpose of this paper is clustering of buildings by using Grey Set Theory. This theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for this study rather than traditional statistical regression which requires massive data, normal distribution in the data and few variant factors. Gray clustering in this study has been used for two purposes. First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, Grey Clustering with Variable Weights has been used to classify all buildings in three categories named “standard”, “Moderate standard deviation” and “completely non-standard”. This classification can be the basis of behavioral research on each group and understanding of cultural differences in each cluster, regardless of technological and structural differences between the buildings. In addition it can be as a tool for understanding the potentials and possibilities for sites of action to achieve behaviour change, whether these are at a general policy level, or targeted at a specific group
https://imj.ut.ac.ir/article_59595_f463bfd96be71ca9b6f38b9e6b062461.pdf
2016-04-20
43
60
10.22059/imj.2016.59595
Energy audit
Gray Set Theory
Grey Clustering
Iran Oil Ministry
Mostafa
Razavi
mrazavi@ut.ac.ir
1
دانشیار گروه مدیریت صنعتی، دانشکدۀ مدیریت، دانشگاه تهران، تهران، ایران
AUTHOR
Mohammad Reza
Mehregan
mmehregan@ut.ac.ir
2
استاد گروه مدیریت صنعتی، دانشکدۀ مدیریت، دانشگاه تهران، تهران، ایران
AUTHOR
Hamed
Shakori
hshakori@ut.ac.ir
3
دانشیار گروه مهندسی صنایع، دانشکدۀ مهندسی صنایع، دانشگاه تهران، تهران، ایران
AUTHOR
Toraj
Karimi
tkarimi@ut.ac.ir
4
استادیار گروه مدیریت صنعتی، پردیس فارابی، دانشگاه تهران، قم، ایران
LEAD_AUTHOR
Barr, S., Gilg, A.W. & Ford, N. (2005). The household energy gap: examining the divide between habitual- and purchase-related conservation behaviours. Energy Policy, 33(11): 1425-1444.
1
Encinas, N. & Alfonso, D. (2007). Energy market segmentation for distributed energy resources implementation purposes, IET Generation Transmission & Distribution, 1 (2): 324-330.
2
Filippin, C., Larsen, S.F. & Mercado, V. (2011). Winter energy behaviour in multi-family block buildings in a temperate-cold climate in Argentina. Renewable and Sustainable Energy Reviews, 15(1): 203-219.
3
International Energy Conservation Code (2006). International code council, Inc.
4
Jian, L., Liu, S. & Lin, Y. (2011). Hybrid Rough Sets and Applications in Uncertain Decision-Making, by Taylor and Francis Group, LLC.
5
Jiang, W., Zhong, X., Qi, J. & Zhu, C. (2007). Grey Rough Sets Hybrid Scheme for Intelligent Fault Diagnosis. IEEE International Conference on Grey Systems and Intelligent Services, November 18-20, Nanjing, China.
6
Li, G.D., Yamaguchi, D. & Lin, H.S. (2006). The simulation modeling about the developments of GDP, population and primary energy consumption in china based on MATLAB. In: Proceedings of the IEEE International Conference on Cybernetics and Intelligent Systems (CIS 2006), Bangkok, Thailand, June 2006, pp 499–504.
7
Liu, S. & Lin, Y. (2006). Grey Information Theory and Practical Applications. Springer-Verlag London Limited.
8
Liu, S. & Lin, Y. (2010). Grey Systems Theory and Applications. Springer-Verlag Berlin Heidelberg.
9
Liu, S., Forrest, J. & Vallee, R. (2009). Emergence and development of grey systems theory. Kybernetes, 38(7/8): 1246-1256.
10
Michalik, G. & Mielczarski, W. (1996). Modeling of Energy Use Patterns in the Residential Sector Using Linguistic Variables. 8th International Conference on Intelligent Systems applications to Power Systems, Orlando, Florida, USA: 1996, pp. 278-282.
11
Raaij, W.V. & Verhallen, M.M. (1983). A Behavioral Model of Residential Energy Use. Journal of Economic Psychology, 3(1): 39-63.
12
Stephenson, J., Barton, B., Carrington, G., Gnoth, D., Lawson, R. & Thorsnes, P. (2010). Energy cultures: A framework for understanding energy behaviours. Energy Policy, 38(10): 6120-6129.
13
Wang, Q. (2009). Grey Prediction Model and Multivariate Statistical Techniques Forecasting Electrical Energy Consumption in Wenzhou, China. Intelligent Information Technology and Security Informatics. IITSI’09. Second International Symposium, pp. 167–170.
14
Wang, Q., Xia, F. & Wang, X. (2009). Integration of Grey Model and Multiple Regression Model to Predict Energy Consumption. Proceedings of the International Conference on Energy and Environment Technology (ICEET '09); October 2009; Guilin, China. pp. 194–197.
15
Xie, Y. & Li, M. (2009). Research on Prediction Model of Natural Gas Consumption Based on Grey Modeling Optimized by Genetic Algorithm. IITA International Conference on Control, Automation and Systems Engineering. 335–337. Article number 5194459.
16
Yu, Z., Fung, C.M., Haghighat, F., Yoshino, H. & Morofsky, E. (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43(6): 1409–1417.
17
ORIGINAL_ARTICLE
A decision-making model for operational efficiency in the banking workflow process of housing facilities
Banking and financial services are important parts of the service industry. Quality of service in the banking industry has a close relationship with customer satisfaction service system. Banks could create competitive advantage by increasing customer satisfaction and reduce costs in today's competitive environment. This paper presents a decision-making model to obtain optimum tasks assignement to personnels in workflow process of housing facilities for the purposes of minimization the average processing time of orders entered into workflow (average time) and also personnel operating costs to minimize banking workflow preocess (operating costs) which it would reduced the waiting times for customers and ultimately higher satisfaction level for the customers. This is a non-linear multi-objective optimization problem and with respect the nature of the variables would be define in a discrete integer space. Since the problems is in NP-hard problems in class, solving the optimization problem has been conducted using non-dominate Sorting Genetic Algorithm II.
https://imj.ut.ac.ir/article_59598_64a7f17cb53f16e9f14ed1688ee30e1f.pdf
2016-04-20
61
74
10.22059/imj.2016.59598
non-dominant Sorting Genetic Algorithm II
Optimization
Efficiency
workflow bank
Maryam
Mohammadpanah
maryam.mohammadpanah@yahoo.com
1
Kharazmi University
AUTHOR
reza
yosefi zenouz
reza.zenouz@gmail.com
2
University of kharazmi
LEAD_AUTHOR
Akbar
Hassanpoor
ak_hassanpoor@yahoo.com
3
University of kharazmi
AUTHOR
Augustine, A. (2013). Queuing Model as a Technique of Queu Solution in Nigeria Banking Industry. Developing Country Studies, 3(8): 188-195.
1
Bai, X. & Gopal, R. & Nunez, M. & Zhdanov, D. (2014). A decision methodology for managing operational efficiency and information disclosure risk in healthcare processes Network Security. Journal homepage, 57: 406 - 416.
2
Gross, D., Shortle, J.F., Thompson, J.M. & Harris, C.M. (2008). Fundamentals of Queuing Theory. Fourth edition, New York: John Wiley & Sons, Inc.
3
Hosaini, M., Ahmadinezhad, M. & Ghaderi, S. (2010). Review and assessment of service quality and its relation to customer satisfaction, Commercial Bank Case Study. Business Review Magazine, 42: 88-97. (in Persian)
4
Iwu1, H. C., Ogbonna1, C. & Jude, O. (2013). Graphical and queuing model of banking operations in Intercontinental Bank Plc, Nigeria. American Journal of Theoretical and Applied Statistics, 2(6): 282-292.
5
Jumaily, A. & Jobori, H. (2011). Automatic Queuing Model for Banking Applications. International Journal of Advanced Computer Science and Applications (IJACSA), 2 (7): 11-15.
6
Karimian nokabadi, A. (2004). Provide an optimal service to customers using Lehigh fashion the queue. Thesis Master, Tehran University. (in Persian)
7
Matinnafas, F. (2005). Effective risk management incentives. Conference - Tehran International Industrial Engineering, 2: 15-22 (in Persian)
8
Momeni, M. & Moshfegh, F. (2006). Queuing system performance (employees – cashiers) Bank Sepah. Journal of Knowledge Management, 3 (74): 111-131. (in Persian)
9
Pasandideh, S. H. R. & Akhavan Niaki, S. T. (2012). Genetic application in a facility location problem with random demand within queuing framework. Journal of Intelligent Manufacturing. (in Persian)
10
Sheikh, D., Kumar Singh, S. & Kumar Kashuap, A. (2013). Application of queuing theory for the improvement of banking service. International Journal of Advanced Computational Engineering and Networking, 1(4): 15-18.
11
ORIGINAL_ARTICLE
Solving of vehicle routing problem with cross docking by scatter search algorithm
One of methods of improvement of the material flow is cross docking that In is considered as a good method to reduce inventory and improve customer satisfaction. Too, the vehicle routing problem is one of the important problems in distribution management and its goal is to find paths for delivering various cargos. Therefore, this study was conducted in order to vehicle routing problem with Cross-docking in MEHRIZ SHADI MEHREGAN Biscuit and the problem was solved with a scatter search Algorithm. The findings concluded that the optimal distance by scatter search Algorithm was equal to 11,438 km that Compared with the current status that improved respectively of 30.54%. The optimal cost by scatter search algorithm was equal to 10186655 that Compared with the current status and improved 6.7%. Therefore, can be concluded that the scatter search Algorithm is a good solution to this problem.
https://imj.ut.ac.ir/article_59599_f8aa906c24d1301e8c68f3f5f3971412.pdf
2016-04-20
75
96
10.22059/imj.2016.59599
Supply Chain
Vehicle routing problem
cross docking
scatter search algorithm
Ali
Morovati Sharifabadi
alimorovati_ut@yahoo.com
1
استادیار گروه مدیریت صنعتی، دانشکدۀ اقتصاد، مدیریت و حسابداری دانشگاه یزد، یزد، ایران
LEAD_AUTHOR
Mahnaz
Bavarkob
mahnaz_barootkoob@yahoo.com
2
کارشناسارشد مدیریت صنعتی، دانشکدۀ اقتصاد، مدیریت و حسابداری دانشگاه یزد، یزد، ایران
AUTHOR
Amiri, M. & Kheirandish, M. (2000). A Model for Improving Supply Chain Management in Foolad Ardabil Company. Management Development Journal, 14 (71): 4-17. (in Persian)
1
Apte, UM. & Viswanathan, S. (2000). Effective Cross Docking for Improving Distribution Efficiencies. International Journal of Logistics: Research and Applications. 3 (3): 291–302.
2
Aras, N., Aksen, D. & Tagrl Tekin, M. (2011). Selective Multi-Depot Vehicle Routing Problem with pricing. Transportation Research Part C, 19(5): 866-884.
3
Belle, J.V., Valckenaers, P. & Cattrysse, D. (2012). Cross docking: State of the Art. Omega, 40(6): 846-827.
4
Boloori Arabani, A., Zandieh, M. & Fatemi Gomi, S-M-T. (2011). Multi Objective Genetic-Based Algorithms for A Cross-Docking Scheduling Problem. Applied soft computing, 11(8): 4954-4970.
5
Calvete, H.I., Galé, C., Oliveros, M.J. & Snchez-Valverde, B. (2007). A Goal Programming Approach to Vehicle Routing Problems with Soft Time Windows Star, Open. European Journal of Operational Research, 177(3): 1720–1733.
6
Cook, R.L., Gibson B. & Mac Curdy, D. (2005). A Lean Approach to Cross-Docking. Supply Chain Management, 9(2): 54-59.
7
Dantzig, G. & Ramser, J. (1959). The Truck Dispatching Problem. Journal of Management Science, 6(1): 80-91.
8
Dareh Miraki, M. (2013). A new heuristic algorithm to solve vehicle routing problem, operations research journal and its applications, 4(35):1-7.
9
(in Persian)
10
Dehbari, S., Purrusta, A., Naderi, M., Ghobadian, E. & Tavakoli Moghadam, R. (2013). Multi objective vehicle routing with probable service time and fuzzy demand under time window constraints, Operations Research in Applications, 4 (35): 85-106. (in Persian)
11
Dondo, R. & Cerda, J. (2013). A Sweep-Heuristic Based Formulation for the Vehicle Routing Problem with Cross Docking. Computers and Chemical Engineering, 48(2013): 1-54.
12
Dondo, R., Mendez, C.A. & Cerda, J. (2011). The Multi-Echelon Vehicle Routing Problem with Cross Docking in Supply Chain Management. Computers and chemical engineering, 35(12): 3002-3024.
13
Dondo, R.G. & Cerda, J. (2009). A Hybrid Local Improvement Algorithm for Large-Scale Multi-Depot Vehicle Routing Problems with Time Windows. Computers and Chemical Engineering, 33(2): 513–530.
14
Eydi, A.R. & Abdorahimi, H. (2012). Model and solution approach for multi-period and multi-depot vehicle routing problem with flexibility in specifying the last depot of each rout. International journal of industrial engineering & production management, 23 (3): 333-349. (in Persian)
15
Fu, Z., Eglese, R. & Li, L.Y.O. (2008). A Unified TABU Search Algorithm for Vehicle Routing Problems with Soft Time Windows. Journal of the Operational Research Society, 59(5): 663–673.
16
Gary, M. & Johnson, D. (1979). Computers and Intractability: A Guide to the Theory of NP Completeness. Freeman, San Francisco.
17
Hosseini Nasab, S., Safaadeh, M. & Mamduhi, A. (2012). A method for routing optimization in public transportation integrating bus network and extremist bus, Transportation Engineering, 4 (8): 303-316. (in Persian)
18
Jia, H., Li, Y., Dong, B. & Ya, H. (2013). An improved tabu search approach to vehicle routing problem. Social and behavioral sciences, 96 (6): 1208-1217.
19
Kinnear, E. (1997). Is There Any Magic in Cross-Docking? Supply Chain Management: An International Journal, 2 (2): 49-52.
20
Laporte, G. (1992). The Vehicle Routing Problem: An Overview of Exact and Approximate Algorithms. European journal of operational research, 59(3): 345-358.
21
Lee, Y.H., Jung, J.W. & Lee, K.M. (2006). Vehicle Routing Scheduling for Cross-Docking in the Supply Chain, Computers & Industrial Engineering, 51(2): 247–256.
22
Li, Y., Lim, A. & Rodrigues, B. (2004). Cross-Docking: JIT Scheduling with Time Windows. Journal of the Operational Research Society, 55 (12): 1342–51.
23
Lio, C. J., Lin, Y & Shih, S.C. (2010). Vehicle Routing with Cross-Docking in the Supply Chain, expert systems with applications, 37(10): 6868-6873.
24
Liu, R. & Jiang, Z. (2012). The close-open mixed vehicle routing problem. European Journal of Operational Research, 220(2): 349–360.
25
Macedo, R., Alves, C., Carvalho, J.M.V.D; Clautiaux, F. & Hanafi, S. (2011). Solving the Vehicle Routing Problem with Time Windows and Multiple Routes Exactly Using a Pseudo-Polynomial model. European Journal of Operational Research, 214(3): 536–545.
26
Mahdavi-Asl, V., Khademi-Zare, H. & Hosseini-Nasab, H. (2012). Offering a mathematical model and heuristic method for solving multi-depot and multi-product vehicle routing problem with heterogeneous vehicle, International journal of industrial engineering & production management, 23 (3): 303-315. (in Persian)
27
Marinakis, Y. & Marinaki, M. (2010). A Hybrid Genetic – Particle Swarm Optimization Algorithm for the Vehicle Routing Problem. Expert Systems with Applications, 37(2): 1446–1455.
28
Mester, D., Braysy, O. & Dullaert, W. (2007). A Multi-Parametric Evolution Strategies Algorithm for Vehicle Routing Problems. Expert Systems with applications, 32(2): 508-517.
29
Mohammadi Zanjirani, D. & Asadi Aghageri, M. (2009). Designing Mathematical Model for Transportation Routing in Supply Chain, With a Case Study in DonarKhazar Company. Journal of industrial management, 1 (3): 119-136. (in Persian)
30
Moon, I., Lee, J.H. & Seong, J. (2012). Vehicle Routing Problem with Time Windows Considering Overtime and Outsourcing Vehicles. Expert Systems with Applications, 39(18): 13202–13213.
31
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(in Persian)
68
ORIGINAL_ARTICLE
Estimating warranty costs for the manufacturer and buyer based on a new Pro-Rata Warranty policy
Currently, a large number of products are being sold with warranty policies. Pro-rata warranties are relatively a complex concept. This paper focuses on developing a new Pro-rata warranty policy. In this model components of the product are grouped into two disjointed sets, failures of components belonging to set one are covered by PRW warranty and those components that belonging to set two are not covered under warranty. In order to design the new policy, two different approaches were designed that in any approach the product failure rates are estimated separately. The proposed model helps manufacturer to have a precise estimate of the expected costs and determine the warranty price based on this estimation. As a result of reduction of expected warranty costs and consequently the price, the customer will also benefit. Finally, the sensitivity of warranty model is analyzed with a numerical example.
https://imj.ut.ac.ir/article_59601_37a83eb9f6d428aa4f90249c27a8cdc2.pdf
2016-04-20
97
112
10.22059/imj.2016.59601
Pro-Rata Warranty
Warranty
Warranty cost
Warranty policies
Mahdi
Nasrollahi
m.nasrollahi@ut.ac.ir
1
Faculty member of the International University of Imam Khomeini
LEAD_AUTHOR
Ezzatollah
Asgharizadeh
asghari@ut.ac.ir
2
Associate Professor of Industrial Management University of Tehran
AUTHOR
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)
1
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2
Blischke, W. R. & Murthy, D. N. P. (1992). Product warranty management-I: A taxonomy for warranty policies. European Journal of Operational Research, 62(2): 127–148.
3
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4
Blischke, W.R. & Murthy, D.N.P. (1994). Warranty cost analysis. New York: Marcel Dekker, Inc.
5
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6
Blischke, W.R., Rezaul, M.K. & Murthy, D.N.P. (2011). Warranty data collection and analysis. London: Springer Verlag.
7
Jain, M. & Maheshwari, S. (2006). Discounted costs for repairable units under hybrid warranty. Applied Mathematics and Computation, 173(2): 887–901.
8
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9
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10
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11
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13
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14
Murthy, D.N.P. & Blischke, W.R. (2005). Warranty management and Product manufacture. USA: Springer Series in reliability engineering.
15
Nasrollahi, M., Asgharizadeh, E., Jafarnezhad, A. & Saniee Monfared, M.A. (2014). Development of a new Pro-rata warranty policy for estimating costs. Quarterly Journal of Industrial management, 6 (1): 127-140. (in Persian)
16
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17
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18
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19
Shafiee. M., Chukova. S. & Yun, W. (2014). Optimal burn-in and warranty for a product with post-warranty failure penalty. The International Journal of Advanced Manufacturing Technology, 70 (1): 297-307.
20
Stamenković, D., Popović, V., Spasojević-Brkić, V. & Radivojević, J. (2011). Combination free replacement and pro-rata warranty policy optimization model. Journal of Applied Engineering Science, 9 (4): 457-464.
21
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22
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23
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24
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25
Yeh, C.W. & Fang, C.C. (2014). Optimal pro-rata warranty decision with consideration of the marketing strategy under insufficient historical reliability data, International Journal of Advanced Manufacturing and Technology, 71(9):1757–1772.
26
Yang, D., He, Z. & He, S. (2016). Warranty claims forecasting based on a general imperfect repair model considering usage rate. Reliability Engineering & System Safety, 145: 147-154.
27
ORIGINAL_ARTICLE
An Inventory–Scheduling Model for Supply Chain of Construction Project
One of the important problems in project management is lack of available resources in proper time at project site that leads to lack of project implementation in due date. This problem may be lead to time delay in sending resources from suppliers and related origins in supply chain to project site. Therfore, to better resources managmenet and also the improvement of supply chain condition, the time delay problem in resources supply can be solved by determining the optimal quantity of order in supply chain based on project constraints and required time for implementation of supply chain activities. Accordingly, in this paper a mamathemical model is proposed for determining the optimal quantity of orders and activities duration of supply chain, in which the total holding cost in project site and also supply chain cost are minimized. The mentioned model is first linearized and then by using the numerical examples, the results analyziz are presented.
https://imj.ut.ac.ir/article_59602_4b417737383d647bbc31ae114d2ca7e2.pdf
2016-04-20
113
140
10.22059/imj.2016.59602
Construction Supply Chain
Duration of Supply Chain's Activities
Optimal Quantity of Order
Mathematical model
Mohammad Reza
Vakili
m_vakili@ind.iust.ac.ir
1
کارشناسارشد مهندسی صنایع، دانشکدۀ صنایع، دانشگاه علم و صنعت ایران، تهران، ایران
AUTHOR
Siamk
Nori
snoori@iust.ac.ir
2
دانشیار گروه مدیریت بهرهوری، دانشکدۀ مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران
AUTHOR
Sseid
Yaghobi
yaghoubi@iust.ac.ir
3
عضو هیئت علمی
LEAD_AUTHOR
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33