ORIGINAL_ARTICLE
A New Mathematical Model to Solve the Assignment Problems Caused by Multiple Heterogeneous Inputs and Outputs
Nowadays assignment issues, as one of the optimization problems in the field of Operations Research is studied by many researchers. Assignment issue is known as a type of NP-Hard issues. However, in real applications, various inputs and outputs are usually concerned in an assignment problem. This paper, based on some of Electrical Engineering concepts that can be considered equivalent to the concept of efficiency in data envelopment analysis, provides a new linear programming model to resolve assignment problems with multiple diverse inputs and outputs for each possible assignment. The objective function in this model is maximum of comparative efficiency rather than cost or profit. The main advantages of this new mathematical model include faster convergence to the optimum solution, focusing on a single mathematical model at the same time not several models, stability in the number of variables and constraints of the proposed model considering any increase in the number of inputs or outputs of problem and also less computational time compared to the other conventional approaches. The proposed model was described along with an applied example, then the results were compared with that of the model proposed by Chen and Lu.
https://imj.ut.ac.ir/article_63871_4e184dd2264acd394ee6ab2ad7e33118.pdf
2017-04-21
1
18
10.22059/imj.2017.126879.1006872
Assignment problem
Data Envelopment Analysis
Efficiency
Electrical circuits
Multiple inputs and outputs
Adel
Azar
azara@modares.ac.ir
1
Prof. of Industrial Management, Tarbiat Modares University, Tehran, Iran
AUTHOR
Hossein
Mohebbi
h.mohebbi.64@gmail.com
2
Assistant Prof. in Industrial Management, Ayatollah Haeri University of Meybod , Iran
LEAD_AUTHOR
Ameneh
Khadivar
a_khadivar@yahoo.com
3
Assistant Prof., Faculty of Management, Alzahra University, Tehran, Iran
AUTHOR
Abasali
Heydari
aheidari@yazd.ac.ir
4
Associate Prof., Faculty of Electrical Engineering, Yazd University, Yazd, Iran
AUTHOR
آذر، ع. (1394). تحقیق در عملیات: مفاهیم و کاربردهای برنامهریزی خطی (چاپ ششم)، تهران: انتشارات سمت.
1
داناییفرد، ح.، الوانی، س. م.، آذر، ع. (1392). روششناسی پژوهش کمی در مدیریت: رویکردی جامع (چاپ دوم)، تهران: انتشارات صفار.
2
Azar, A. (2015). Operation Research: Concepts and applications of linear programming. Tehran: Oloume Novin. (in Persian)
3
Bazaraa, M.S. & Jarvis, J.J. Sherali, H.D. (2010). Linear Programming and Network Flows. New Jersey: A John Wiley & Sons, Inc.
4
Campbell, G.M. & Diaby, M. (2002). Development and evaluation of an assignment heuristic for allocating cross-trained workers. Euro Journal Operation Research, 138(1), 9-12.
5
Chen, L.H. & Lu, H.W. (2007). An extended assignment problem considering multiple inputs and outputs. Applied Mathematical Modelling, 31(10), 2239-2248.
6
Chen, L.H. & Lu, H.W. (2008). Responses and comments to "A comment on An extended assignment problem considering multiple inputs and outputs". Applied Mathematical Modelling, 32(11), 2463–2466.
7
Danaeifard, H. & Alvani, S.M. & Azar, M. (2007). Qualitative research methodology in management: a comprehensive approach. Tehran: Safar.
8
(in Persian)
9
Dorf, R.C. & Svoboda, J.A. (2014). Introduction to Electric Circuits. California: Vaibhav Goel.
10
Fried, H.O. & Lovell, C.A.K. & Turner, J.A. (1996). An analysis of the performance of university-affiliated credit unions. Computer Operation Research, 23 (4), 375–384.
11
Hiller, G. & Liberman, B. & Frederick, S. (2002). Introduction to Operations Research. Mc Graw. Hill.
12
Jahanshahloo, G.R. & Afzalinejad, M. (2008). A comment on "An extended assignment problem considering multiple inputs and outputs". Applied Mathematical Modelling, 32(11), 2459–2462.
13
Kao, C. & Liu, S.T. (2000). Data envelopment analysis with missing data: an application to university libraries in Taiwan. Journal of Operation Research, 51(8), 897–905.
14
Lewin, A.Y. & Minton J.W. (1986). Determining organizational effectiveness: another look, and agenda for research. Management Science, 32(5), 514-538.
15
Marianoa, E.B. & Sobreirob, V.A. & Rebelattoc, D.A.N. (2015). Human development and data envelopment analysis: A structured literature review. Omega, 54, 33-49.
16
Nilsson, J.W. & Riedel, S.A. (2011). Electric Circuits. New Jersey: Prentice Hall.
17
Süer, G.A. & Bera, I.S. (1998). Optimal operator assignment and cell loading when lot-splitting is allowed. Computers & Industrial Engineering, 35(4), 431–434.
18
ORIGINAL_ARTICLE
Proposing a Two-Phase Integer Linear Programming for University-Course Timetabling
An integer linear programming model for university courses timetabling is proposed here. In order to reduce the number of decisive variables, a combination of a course, a professor schedule and the students ‘group was defined as an activity. In this context, the two integer programming models namely the activity-based model and a two-phase activity-based model were proposed. In the first model, all activities were scheduled based on the number of required weekly sessions in the weekdays intervals; however, in the second model, classes and training courses were determined according to the planned sessions considering their special restrictions. These models were formulated based on the process of assigning the university courses within specific intervals throughout the week considering fierce constraints for a given semester in the department of Economics at University of Isfahan. All regulation concerning the courses timetable of a semester were formulated in GAMS software. Then, 239 courses were successfully scheduled using the two-phase activity-based model in only 9 minutes and 16 seconds
https://imj.ut.ac.ir/article_63872_c70a32f03d12e767e5dba5fc11783382.pdf
2017-04-21
19
42
10.22059/imj.2017.217990.1007129
Bi-level model
Hard constraint
Integer linear programming
Mathematical modeling
University course timetabling
Majid
Esmaelian
majid_esmaelian@yahoo.com
1
Assistant Prof. in Management, Faculty of Administrative Sciences and Economics, Isfahan University, Isfahan, Iran
LEAD_AUTHOR
Sayedeh Maryam
Abdollahi
sm_abdollahi@hotmail.com
2
MSc of Industrial Management, Faculty of Administrative Sciences and Economics, Isfahan University, , Iran
AUTHOR
اسماعیلیان، م.، عبدالهی، س. م. (1395). زمانبندی کلاسهای درس با استفاده از برنامهریزی عدد صحیح. مطالعات مدیریت صنعتی، 14 (41)، 187-163.
1
بهداد، م.، دهقانی، ت.، ذاکر تولائی، م. (1385). رویکردی نوین در زمانبندی دروس دانشگاه با استفاده از الگوریتم ژنتیک. دوازدهمین کنفرانس سالانۀ انجمن کامپیوتر ایران. تهران: دانشگاه شهید بهشتی.
2
جودکی، م.، منتظری، م. ع.، موسوی، س. ر. (1390). بررسی مسئلۀ زمانبندی درسی دانشگاهی با استفاده از ترکیب الگوریتم ممتیک بهبود یافته و الگوریتم سردشدن شبیهسازی شده. مهندسی برق و مهندسی کامپیوتر ایران، 9(4)، 202-192.
3
راستگار امینی، ف.، میرمحمدی، س.م. (1391). مدلسازی و ارائۀ روش حل برای مسئلۀ زمانبندی دروس دانشگاهی و تخصیص استاد ـ درس (مطالعۀ موردی دانشکدۀ صنایع دانشگاه صنعتی اصفهان). نهمین کنفرانس بینالمللی مهندسی صنایع. تهران: انجمن مهندسی صنایع ایران، دانشگاه صنعتی خواجه نصیرالدین طوسی.
4
سلیمی فرد، خ.، باباییزاده، س. (1390). یک سیستم پشتیبانی تصمیم برای زمانبندی کلاسهای دانشگاه (مطالعۀ موردی: دانشگاه خلیج فارس)، مدیریت فناوری اطلاعات، 3 (7)، 92-77.
5
علیرضایی، م. ر.، منصورزاده، س. م.، خلیلی، م. (1385). برنامهریزی درسی در دانشگاه به کمک مدلسازی دو مرحلهای برنامهریزی ریاضی. دانشور، 17، 96-87.
6
فراهانی، ر.، زندیه، ز. (1392). زمانبندی چند معیارۀ دروس دانشگاهی با بهکارگیری الگوریتم جستوجوی ممنوعه و فرایند تحلیل سلسهمراتبی. دهمین کنفرانس بینالمللی مهندسی صنایع ایران.
7
منجمی، ا.ح.، مسعودیان، س.، استکی، ا.، نعمتبخش، ن. (1388). طراحی جدول زمانبندی خودکار برای دروس دانشگاهی با استفاده از الگوریتمهای ژنتیک. فناوری آموزش، 4 (2)، 127- 113.
8
Aladag, C. H., Hocaoglu, G. & Basaran, M. A. (2009). The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem. Expert Systems with Applications, 36(10), 12349-12356.
9
Alzaqebah, M. & Abdullah, S. (2015). Hybrid bee colony optimization for examination timetabling problems. Computers & Operations Research, 54, 142-154.
10
Amintoosi, M. & Haddadnia, J. (2005). Fuzzy C-means clustering algorithm to group students in a course into smaller sections. ACM and Springer, 147-160.
11
Asmuni, H. (2008). Fuzzy methodologies for automated University timetabling solution construction and evaluation. Doctoral dissertation, University of Nottingham.
12
Aycan, E. & Ayav, T. (2009). Solving the course scheduling problem using simulated annealing”. In Advance Computing Conference, 2009. IACC 2009. IEEE International (pp. 462-466), IEEE.
13
Babaei, H., Karimpour, J. & Hadidi, A. (2015). A survey of approaches for university course timetabling problem. Computers & Industrial Engineering, 86, 43-59.
14
Babaizadeh, S. & Salimifard, KH. (2011). A decision support system for university course time tabling (The case: Khalijefars University). Information Technology Management, 3(7), 77-92. (in Persian)
15
Badoni, R. P., Gupta, D. K. & Mishra, P. (2014). A new hybrid algorithm for university course timetabling problem using events based on groupings of students. Computers & Industrial Engineering, 78, 12-25.
16
Barrera, D., Velasco, N. & Amaya, C. A. (2012). A network-based approach to the multi-activity combined timetabling and crew scheduling problem: Workforce scheduling for public health policy implementation. Computers & Industrial Engineering, 63(4), 802-812.
17
Behdad, M., Dehghani, T. & Zaker Tavalai, M. (2006). A new approach in course timetabling using genetic algorithm”, In the Twelfth Conference of Iranian Computer Science Society. Tehran. (in Persian)
18
Burke, E. K. & Petrovic, S. (2002). Recent research directions in automated timetabling. European Journal of Operational Research, 140(2), 266-280.
19
Burke, E. K., Kendall, G., Mısır, M., Özcan, E., Burke, E. K., Kendall, G., ... & Mısır, M. (2004). Applications to timetabling. In Handbook of Graph Theory, chapter 5.6.
20
Burke, E. K., Mareček, J., Parkes, A. J., &Rudová, H. (2010). A super nodal formulation of vertex coloring with applications in course timetabling. Annals of Operations Research, 179(1), 105-130.
21
Burke, E. K., McCollum, B., Meisels, A., Petrovic, S. & Qu, R. (2007). A graph-based hyper-heuristic for educational timetabling problems. European Journal of Operational Research, 176(1), 177-192.
22
Burke, E., de Werra, D. & Kingston, J. (2013). Applications to Timetabling .In P. Zhang (Ed.), Handbook of Graph Theory, Second Edition (pp. 530-562), Chapman and Hall/CRC.
23
Carter, M. W. (1989). A Lagrangian Relaxation Approach to The Classroom Assignment Problem. INFOR: Information Systems and Operational Research, 27(2), 230-246.
24
Chaudhuri, A. & De, K. (2010). Fuzzy genetic heuristic for university course timetable problem. International Journal of Advances in Soft Computing and its Applications, 2(1), 100-123.
25
Deris, S., Omatu, S. & Ohta, H. (2000). Timetable planning using the constraint-based reasoning. Computers & Operations Research, 27(9), 819-840.
26
Esmaelian, M. & Abdollahi, S. M. (2016). Binary integer programming for university timetabling (The Case: Faculty of administrative sciences and economics of Esfahan university). Industrial Management Studies, 14(41), 163-187. (in Persian)
27
Farahani, R. & Zandieh, M. (2013). Multi criteria university course scheduling applying Tabue Search algorithm and analytical hierarchical processing (AHP). In the Tenth International Conference on Industrial Management, Tehran. (in Persian)
28
Feizi-Derakhshi, M.R., Babaei, H. & Heidarzadeh, J. (2012). A survey of approaches for university course timetabling problem. In Proceedings of 8th international symposium on intelligent and manufacturing systems (IMS 2012), (p.p. 307-321).
29
Geem, Z. W., Kim, J. H. & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.
30
Golabpour, A., Shirazi, H. M., Farahi, A., Kootiani, A. Z. M. & Beigi, H. (2008). A fuzzy solution based on Memetic algorithms for timetabling. In Multimedia and Information Technology, 2008. MMIT'08. International Conference on, (p.p. 108-110), IEEE.
31
Henry Obit, J. (2010). Developing novel meta-heuristic, hyper-heuristic and cooperative search for course timetabling problems. Doctoral dissertation, United kingdom: University of Nottingham.
32
Joodaki, M., Montazeri, M. A. & Moosavi, S. R. (2011). Investigation of university course timetabling problem using a combination of improved memetic algorithm and simulated annealing algorithm. Iran’s Electronic and Computer Engineering, 9(4), 192-202. (in Persian)
33
Jorge, A.S.A., Martín, C., Héctor, P. & Sotelo-Figueroa, M. A. (2013). Comparison of metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem. In Recent Advances on Hybrid Intelligent Systems (p.p. 289-302), Springer Berlin Heidelberg.
34
Jorge, S. A., Martin, C., Hector, P., Patricia, M., Hugo, T. M., Laura, C. & Marco, S. F. (2014). Generic Memetic Algorithm for Course Timetabling ITC2007. In Recent Advances on Hybrid Approaches for Designing Intelligent Systems (p.p. 481-492), Springer International Publishing.
35
Junginger, W. (1986). Timetabling in Germany-a survey. Interfaces, 16(4), 66-74.
36
Kaspi, M. & Raviv, T. (2013). Service-oriented line planning and timetabling for passenger trains. Transportation Science, 47(3), 295-311.
37
Khalili, M., Mansoorzadeh, M.S. & Alirezai, R.M. (2006). University course timetabling using two phase mathematical programming. Daneshvar Raftar, 17, 87-96. (in Persian)
38
Kostuch, P. (2004). The university course timetabling problem with a three-phase approach. In International Conference on the Practice and Theory of Automated Timetabling (p.p. 109-125), Springer Berlin Heidelberg.
39
Kroon, L. G. & Peeters, L. W. (2003). A variable trip time model for cyclic railway timetabling. Transportation Science, 37(2), 198-212.
40
Lewis, R. (2008). A survey of metaheuristic-based techniques for university timetabling problems. OR spectrum, 30(1), 167-190.
41
Lewis, R. (2012). A time-dependent metaheuristic algorithm for post enrolment-based course timetabling. Annals of Operations Research, 194(1), 273-289.
42
Lewis, R., Paechter, B. & Rossi-Doria, O. (2007). Metaheuristics for university course timetabling. In Evolutionary scheduling (p.p. 237-272), Springer Berlin Heidelberg.
43
Miranda, J. (2010). eClasSkeduler: a course scheduling system for the Executive Education Unit at the Universidad de Chile. Interfaces, 40(3), 196-207.
44
MirHassani, S. A. & Habibi, F. (2013). Solution approaches to the course timetabling problem. Artificial Intelligence Review, 39(2), 133-149.
45
Monajemi, S. A., Masoudian, S., Esteki, A. & Nematbakhsh, N. (2009). Automatic university course timetable designing using Genetic Algorithm. Education Technology, 4(2), 113-127. (in Persian)
46
Nothegger, C., Mayer, A., Chwatal, A. & Raidl, G. R. (2012). Solving the post enrolment course timetabling problem by ant colony optimization. Annals of Operations Research, 194 (1), 325-339.
47
Post, G., Kingston, J. H., Ahmadi, S., Daskalaki, S., Gogos, C., Kyngas, J., ... & Schaerf, A. (2014). XHSTT: an XML archive for high school timetabling problems in different countries. Annals of Operations Research, 218(1), 295-301.
48
Puente, J., Gómez, A., Fernández, I. & Priore, P. (2009). Medical doctor rostering problem in a hospital emergency department by means of genetic algorithms. Computers & Industrial Engineering, 56(4), 1232-1242.
49
Qu, R. & Burke, E. K. (2009). Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems”.Journal of the Operational Research Society, 60(9), 1273-1285.
50
Qu, R., Burke, E. K., McCollum, B., Merlot, L. T. & Lee, S. Y. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of scheduling, 12(1), 55-89.
51
RastgarAmini, F. & Mir Mohammadi, S. A. (2012). Modeling and solving method for university course timetabling problem and teacher-course allocation. In the Ninth International Conference on Industrial Management, Tehran. (in Persian)
52
Rossi-Doria, O., Sampels, M., Birattari, M., Chiarandini, M., Dorigo, M., Gambardella, L. M. & Paquete, L. (2002). A comparison of the performance of different metaheuristics on the timetabling problem. In International Conference on the Practice and Theory of Automated Timetabling (p.p. 329-351), Springer Berlin Heidelberg.
53
Santiago-Mozos, R., Salcedo-Sanz, S., DePrado-Cumplido, M. & Bousoño-Calzón, C. (2005). A two-phase heuristic evolutionary algorithm for personalizing course timetables: a case study in a Spanish university. Computers & operations research, 32(7), 1761-1776.
54
Schaerf, A. (1999). A survey of automated timetabling. Artificial intelligence review, 13(2), 87-127.
55
Shafia, M. A., Aghaee, M. P., Sadjadi, S. J. & Jamili, A. (2012). Robust Train Timetabling problem: Mathematical model and Branch and bound algorithm. IEEE Transactions on Intelligent Transportation Systems, 13(1), 307-317.
56
Shafia, M. A., Aghaee, M. P., Sadjadi, S. J. & Jamili, A. (2012). Robust Train Timetabling problem: Mathematical model and Branch and bound algorithm. IEEE Transactions on Intelligent Transportation Systems, 13(1), 307-317.
57
Shatnawi, S., Al-Rababah, K. & Bani-Ismail, B. (2010). Applying a novel clustering technique based on FP-tree to university timetabling problem: A case study. In Computer Engineering and Systems (ICCES), 2010 International Conference on (p.p. 314-319), IEEE.
58
Srinivasan, S., Singh, J. & Kumar, V. (2011). Multi-agent based decision support system using data mining and case based reasoning. IJCSI International Journal of Computer Science Issues, 8(4), 340-349.
59
Tripathy, A. (1984). School timetabling-a case in large binary integer linear programming. Management science, 30(12), 1473-1489.
60
Turabieh, H., Abdullah, S., McCollum, B. & McMullan, P. (2010). Fish swarm intelligent algorithm for the course timetabling problem. In International Conference on Rough Sets and Knowledge Technology (p.p. 588-595), Springer Berlin Heidelberg.
61
Wangmaeteekul, P. (2011). Using Distributed Agents to Create University Course Timetables Addressing Essential & Desirable Constraints and Fair Allocation of Resources. Doctoral dissertation, Durham University, United Kingdom: Durham University.
62
Yang, Y. & Paranjape, R. (2011). A multi-agent system for course timetabling. Intelligent Decision Technologies, 5(2), 113-131.
63
Yang, Y., Paranjape, R. & Benedicenti, L. (2006). An agent based general solution model for the course timetabling problem. In Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (p.p. 1430-1432), ACM.
64
Zhang, L. & Lau, S. (2005). Constructing university timetable using constraint satisfaction programming approach. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06) (Vol. 2, p.p. 55-60), IEEE.
65
ORIGINAL_ARTICLE
Analyzing Bullwhip Effect Sensitivity in a Four-level Supply Chain Using Average Moving Method to Forecast the Demand
In recent years, coping with a phenomenon known as bullwhip effect, has been among the most important issues in supply chain management. Bullwhip dramatically affects companies’ financial performance stating that the swing of changes in demands increases when we move from the end of the chain to the beginning of that chain. Many studies have been conducted on reducing the impact of supply chain bullwhip, each focusing on specific aspects. For this purpose in the present study, a linear four-level supply chain including store, retailer, wholesaler and factory was proposed, and the moving average method was used to predict the demand. To do so, nine different scenarios including demand changes (low, medium, high) and precautionary (low, moderate, high) were considered and the lag effect was calculated with a 95% confidence interval and throughout a one-year period. The findings showed that if we use average moving method to predict the demands, any increase in the customers’ demand change swing will result in the decrease of the whip effect on the whole chain. In addition, if the demand changes are considered as constant and fixed, any increase in precautionary saving in each part of the supply chain will increase the whip effect on the whole chain.
https://imj.ut.ac.ir/article_63874_bf8b43af364919e3070a4c144de2e698.pdf
2017-04-21
43
58
10.22059/imj.2017.223681.1007173
Bullwhip Effect
Moving Average Method
Order-up-to
Supply Chain
Sayyid Ali
Banihashemi
banihashemi1120@gmail.com
1
Instructor, Faculty of Industrial Engineering, Payame Noor University, Tehran, Iranپ
LEAD_AUTHOR
Sayyid Mohammad
Haji Molana
molana@aut.ac.ir
2
Assistant Prof., Faculty of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
AUTHOR
یوسفی زنوز، ر.، منهاج، م. ب. (1390). طرح یک چارچوب ترکیبی پیشبینی تقاضای متلاطم و کنترل پیشبین مدل بهمنظور کمینهسازی اثر شلاقی. فصلنامۀ مدیریت صنعتی، 3(6)، 190-171.
1
Chatfield, D. C. & Pritchard, A. M. (2013). Returns and the bullwhip effect. Transportation Research Part E: Logistics and Transportation Review, 49(1), 159-175.
2
Chatfield, D. C., Kim, J. G., Harrison, T. P. & Hayya, J. C. (2004). The bullwhip effect-impact of stochastic lead time, information quality, and information sharing: a simulation study. Production and Operations Management, 13(4), 340-353.
3
Chen, F., Drezner, Z., Ryan, J.K. & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information, Management Science, 463), 436-443.
4
Costantino, F., Gravio, G. D., Shaban, A. & Tronci, M. (2015). SPC forecasting system to mitigate the bullwhip effect and inventory variance in supply chains. Expert Systems with Applications, 42(3), 1773–1787.
5
Dejonckheere, J., Disney, S. M., Lambrecht, M. R., & Towill, D. R. (2004). The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective. European Journal of Operational Research, 153(3), 727-750.
6
Dominguez, R., Cannella, S. & Framinan, J. M. (2015). The impact of the supply chain structure on bullwhip effect. Applied Mathematical Modelling, 39(23), 7309-7325.
7
Forrester, J. W. (1961). Industrial dynamics, Camdridge, MIT Press.
8
Khosroshahi, H., Husseini, S. M. & Marjani, M. R. (2016). The bullwhip effect in a 3-stage supply chain considering multiple retailers using a moving average method for demand forecasting. Applied Mathematical Modelling, 40(21), 8934-8951.
9
Kim, J. G., Chatfield, D., Harrison, T. P. & Hayya, J. C. (2006). Quantifying the bullwhip effect in a supply chain with stochastic lead time. European Journal of operational research, 173(2), 617-636.
10
Lambrecht, M., Dejonckheere, J. (1999). A bullwhip effect explorer. Katholieke Universiteit Leuven pub.
11
Lee, H. L., Padmanabhan, V. & Whang, S. (1997). The Bullwhip Effect in supply chains. Sloan Management Review Journal, 38 (3), 93–102.
12
Ma, J., Bao, B. (2016). Research on bullwhip effect in energy-efficient air conditioning supply chain. Journal of Cleaner Production, 143, 854–865.
13
Metters R. (1997). Quantifying the Bullwhip Effect in supply chains. Journal of Operation Management, 15(2), 89–100.
14
Ponte, B., Sierra, E., Fuente, D. D. & Lozano, J. (2017). Exploring the interaction of inventory policies across the supply chain: An agent-based approach. Computers & Operation Research, 78, 335-348.
15
Sirikasemsuk, K. & Trung Luong, H. (2017). Measure of bullwhip effect in supply chains with first-order bivariate vector autoregression time-series demand model. Copmputer & Operations research, 78, 59-79.
16
Sterman, J. D. (1989). Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment. Management Science, 35 (3), 321–239.
17
Trapero, J. R. & Pedregal, D. J. (2016). A novel time-varying bullwhip effect metric: An application to promotional sales. International Journal of Production Economics, 182, 465-471.
18
Yousefi Z., R. & Mehnaj, M. B. (2011). Design of a Combined Lumpy Demands Forecasting and a model Predictive Scheme for Reduction of Bullwhip Effect. Journal of Industrial Management, 3(6), 171-190. (in Persian)
19
Zhang, X. (2004). The impact of forecasting methods on the bullwhip effect. International journal of production economics, 88(1), 15-27.
20
ORIGINAL_ARTICLE
Road Hub Location-Routing Issue in a Sparse
and Distant Area
In order to manage the expenditures in a road transportation network in which the transport demands between cities are less than a truckload capacity, one needs to determine the location of hubs at first, and then collect the cargo from the cities in some routes which are assigned to the appropriate hubs. In this paper, a special case of hub location-routing issue was considered that is suitable for the particular conditions of Iran as cities are located in the sparse and distant places. A mixed integer mathematical programming model was proposed. As the model is NP-hard in nature, a two-phase hybrid method including genetic algorithms and simulated annealing was designed to solve the model. The results of the comparison between the model and the outputs demonstrated the accuracy and speed of the proposed solution method. Finally, a real case including all 31 capital cities of Iran provinces was solved to illustrate the appropriate performance of the solution method.
https://imj.ut.ac.ir/article_63875_a15c958af87c6bbb9884f39ea4a0b214.pdf
2017-04-21
59
78
10.22059/imj.2017.213340.1007104
Genetic Algorithm
Hub location
Mathematical Programming
Simulated Annealing
Vehicle routing problem
Farzad
Bahrami
farzad.bahrami@ut.ac.ir
1
Ph.D. Candidate in Production and Operations Management, University of Tehran, Tehran, Iran
AUTHOR
Hossein
Safari
hsafari@ut.ac.ir
2
استاد گروه مدیریت صنعتی، دانشکدۀ مدیریت، دانشگاه تهران، تهران، ایران
LEAD_AUTHOR
Reza
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
3
Prof., School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Mohammad
Modarres Yazdi
modarres@sharif.edu
4
Prof., Dep. of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
رضوی، م.، سوخکیان، م.ع.، زیارتی، ک. (1390). ارائۀ الگوریتم فراابتکاری مبتنی بر سیستم کلونی مورچگان برای مسئلۀ مکانیابی مسیریابی با چندین انبار و فرض تخصیص چندین مسیر به هر وسیلۀ نقلیه. مجلۀ مدیریت صنعتی، 3 (6)، 38-17.
1
شاهین، م.، جبل عاملی، م. س.، جبارزاده، آ. (1395). مکانیابی هاب سلسلهمراتبی چند روش حمل و نقلی و چند کالایی در فضای غیرقطعی. مجلۀ مدیریت صنعتی، 8 (4)، 658-625.
2
Alumur, S., & Kara, B. Y. (2008). Network hub location problems: The state of the art. European journal of operational research, 190(1), 1-21.
3
Baker, B. M. & Ayechew, M. A. (2003). A genetic algorithm for the vehicle routing problem. Computers & Operations Research, 30(5), 787-800.
4
Campbell, A. M., Lowe, T. J. & Zhang, L. (2007). The p-hub center allocation problem. European Journal of Operational Research, 176(2), 819-835.
5
Campbell, J. F. (1994). Integer programming formulations of discrete hub location problems. European Journal of Operational Research, 72(2), 387-405.
6
Campbell, J. F., & O’Kelly, M. E. (2012). Twenty-five years of hub location research. Transportation Science, 46(2), 153-169.
7
Černý, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of optimization theory and applications, 45(1), 41-51.
8
Cunha, C. B. & Silva, M. R. (2007). A genetic algorithm for the problem of configuring a hub-and-spoke network for a LTL trucking company in Brazil. European Journal of Operational Research, 179(3), 747-758.
9
Doulabi, S. H. H. & Seifi, A. (2013). Lower and upper bounds for location-arc routing problems with vehicle capacity constraints. European Journal of Operational Research, 224(1), 189-208.
10
Ernst, A.T. & Krishnamoorthy, M. (1996). Efficient algorithms for the uncapacitated single allocation p-hub median problem. Location science, 4(3), 139-154.
11
Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-72.
12
Jarboui, B., Derbel, H., Hanafi, S. & Mladenović, N. (2013). Variable neighborhood search for location routing. Computers & Operations Research, 40(1), 47-57.
13
Karaoglan, I., Altiparmak, F., Kara, I. & Dengiz, B. (2012). The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach. Omega, 40(4), 465-477.
14
Krikpatrick, S., Gelatt Jr. C.D. & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220 (4598), 671-680.
15
Kuby, M. J. & Gray, R. G. (1993). The hub network design problem with stopovers and feeders: The case of Federal Express. Transportation Research Part A: Policy and Practice, 27(1), 1-12.
16
Laporte, G., (1988). Location-routing problems. In Golden, B.L., Assad, A.A. (eds) Vehicle Routing: Methods, Studies. North-Holland, Amsterdam.
17
Lee, J. H. & Moon, I. (2014). A hybrid hub-and-spoke postal logistics network with realistic restrictions: A case study of Korea Post. Expert systems with applications, 41(11), 5509-5519.
18
Lee, Y., Lim, B. H. & Park, J. S. (1996). A hub location problem in designing digital data service networks: Lagrangian relaxation approach. Location Science, 4(3), 185-194.
19
Lopes, R. B., Ferreira, C., Santos, B. S. & Barreto, S. (2013). A taxonomical analysis, current methods and objectives on location‐routing problems. International Transactions in Operational Research, 20(6), 795-822.
20
Martı́n, J. C. & Román, C. (2003). Hub location in the South-Atlantic airline market: A spatial competition game. Transportation Research Part A: Policy and Practice, 37(10), 865-888.
21
Nagy, G. & Salhi, S. (1998). The many-to-many location-routing problem. Top, 6(2), 261-275.
22
Nagy, G. & Salhi, S. (2007). Location-routing: Issues, models and methods. European Journal of Operational Research, 177(2), 649-672.
23
Norouzi, N., Sadegh-Amalnick, M. & Tavakkoli-Moghaddam, R. (2016). Modified particle swarm optimization in a time-dependent vehicle routing problem: minimizing fuel consumption. Optimization Letters, 11(1), 121-134.
24
O’kelly, M. E. (1986). The location of interacting hub facilities. Transportation science, 20(2), 92-106.
25
Razavi, M., Soukhakian, M. A. & Ziarati, K. (2011). A Meta Heuristic Algorithms Based on Ant Colony System For Solving Multi Depots Location-routing Problem with Multiple Using of Vehicle. Industrial Management, 3(6), 17-38. (in Persian)
26
Rieck, J., Ehrenberg, C. & Zimmermann, J. (2014). Many-to-many location-routing with inter-hub transport and multi-commodity pickup-and-delivery. European Journal of Operational Research, 236(3), 863-878.
27
Salhi, S., & Rand, G. K. (1989). The effect of ignoring routes when locating depots. European journal of operational research, 39(2), 150-156.
28
Sasaki, M., Campbell, J.F., Krishnamoorthy, M. & Ernst, A.T. (2014). A Stackelberg hub arc location model for a competitive environment. Computers & Operations Research, 47, 27-41.
29
Sasaki, M., Suzuki, A. & Drezner, Z. (1999). On the selection of hub airports for an airline hub-and-spoke system. Computers & Operations Research, 26(14), 1411-1422.
30
Shahin, M., Jabalameli, M. S., Jabbarzadeh, A. (2017). Multi-modal and multi-product hierarchical hub location under uncertainty. Industrial Management, 8(4), 625-658. (in Persian)
31
Taylor, G. D., Harit, S., English, J. R. & Whicker, G. (1995). Hub and spoke networks in truckload trucking: Configuration, testing and operational concerns. Logistics and Transportation Review, 31(3), 209-237.
32
Toh, R. S. & Higgins, R. G. (1985). The impact of hub and spoke network centralization and route monopoly on domestic airline profitability. Transportation Journal, 24(4), 16-27.
33
Wasner, M. & Zäpfel, G. (2004). An integrated multi-depot hub-location vehicle routing model for network planning of parcel service. International Journal of Production Economics, 90(3), 403-419.
34
Wolsey, L. A. (1998). Integer programming. New York, John Wiley & Sons, Inc.
35
Yaman, H., Kara, B. Y., & Tansel, B. Ç. (2007). The latest arrival hub location problem for cargo delivery systems with stopovers. Transportation Research Part B: Methodological, 41(8), 906-919.
36
ORIGINAL_ARTICLE
Prioritizing Supply Chain Complexity Drivers using Fuzzy Hierarchical Analytical Process
There are numerous complexities in supply chain nowadays, and these complexities are evolving due to globalization, customization, innovation, flexibility, sustainability and uncertainties. The increase in supply chain complexity yields negative effects on cost, customer service and reputation. Hence, the present research target was to prioritize supply chain complexity drivers in home appliance companies. After reviewing the related literature, supply chain complexity drivers involved in the process of prioritization have been identified. Then, using Fuzzy Hierarchical Analytic Process, these indicators were weighted up based on pairwise comparisons (obtained from a number of experts in the home appliance companies). The results showed that among the 46 Complexity Drivers examined in this study, “the mechanisms to return raw materials to suppliers" was of highest importance.
https://imj.ut.ac.ir/article_63904_ed0fac913a0b58f40c812ade820a7092.pdf
2017-04-21
79
102
10.22059/imj.2017.232896.1007229
Complexity
Fuzzy hierarchical analytical process
Home appliance companies
Supply Chain
Seyyed Mohammad Ali
Khatami firoozabadi
a.khatami@atu.ac.ir
1
Associate Prof. of Industrial Management, Allameh Tabataba'I University, Tehran, Iran
LEAD_AUTHOR
Laya
Olfat
olfat@atu.ac.ir
2
Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'I University, Tehran, Iran
AUTHOR
Maghsoud
Amiri
mg_amiri@yahoo.com
3
Prof., Dep. of Industrial Management, Allameh Tabataba'i University, Tehran, Iran
AUTHOR
Hamid
Sharifi
sharifi@vru.ac.ir
4
Ph.D. Candidate in Industrial Management, Allameh Tabataba'I University, Tehran, Iran
AUTHOR
باقری، م. (1394). بررسی رابطۀ بین پیچیدگی زنجیرۀ تأمین و عملکرد مالی با بررسی نقش تعدیلگری اهرمهای پیچیدگی در شرکتهای تولیدی استان خوزستان. پایاننامۀ کارشناسی ارشد. دانشگاه شهید چمران، اهواز.
1
رمضانیان، م. ر.، رحمانی، ز.، حسینیجو، س. ع.، مباشر امینی، ر. ع. (1392). برخورد با پیچیدگی زنجیرۀ تأمین با استفاده از رویکرد فرایند تفکر تئوری محدودیتها (مطالعۀ موردی: یک شرکت تولیدکنندۀ کاغذ). پژوهشهای مدیریت در ایران، 17(2)، 144-125.
2
سرمد، ز.، بازرگان، ع.، حجازی، الف. (1393). روشهای تحقیق در علوم رفتاری. تهران: نشر آگاه.
3
ماکویی، ا.، مددی، ع. ر. (1383). پیچیدگی در زنجیرههای تأمین. اولین کنفرانس لجستیک و زنجیرۀ تأمین، تهران، انجمن لجستیک ایران.
4
همتا، ن.، اکبرپور شیرازی، م.، قبادی، ش. (1394). مدلسازی و اندازهگیری پیچیدگی ساختاری در شبکههای زنجیرۀ تأمین مونتاژ. دوازدهمین کنفرانس بینالمللی مهندسی صنایع، تهران: دانشگاه خوارزمی و انجمن مهندسی صنایع ایران.
5
Abrahamsson, M., Christopher, M. & Stensson, B. I. (2015). Mastering supply chain management in an era of uncertainty at SKF. Global Business and Organizational Excellence, 34(6), 6-17.
6
Aitken, J., Aitken, J., Bozarth, C., Bozarth, C., Garn, W. & Garn, W. (2016). To eliminate or absorb supply chain complexity: A conceptual model and case study. Supply Chain Management: An International Journal, 21(6), 759-774.
7
Bagheri, M. (2015). Investigating the Relationship between Supply Chain Complexity and Financial Performance by Investigating the Role of Adjustment of Complexity Levers in Khuzestan Province Manufacturing Companies. Master of Science dissertation. Shahid Chamran University of Ahvaz, Ahvaz. (in Persian)
8
Battini, D., Persona, A. & Allesina, S. (2007). Towards a use of network analysis: quantifying the complexity of Supply Chain Networks. International Journal of Electronic Customer Relationship Management, 1(1), 75-90.
9
Blecker, T., Kersten, W. & Meyer, C. M. (2005, January). Development of an approach for analyzing supply chain complexity. In Mass Customization: Concepts–Tools–Realization. Proceedings of the International Mass Customization Meeting (pp. 47-59).
10
Blome, C., Schoenherr, T. & Eckstein, D. (2014). The impact of knowledge transfer and complexity on supply chain flexibility: A knowledge-based view. International Journal of Production Economics, 147, 307-316.
11
Bode, C. & Wagner, S. M. (2015). Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36, 215-228.
12
Bozarth, C. C., Warsing, D. P., Flynn, B. B. & Flynn, E. J. (2009). The impact of supply chain complexity on manufacturing plant performance. Journal of Operations Management, 27(1), 78-93.
13
Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.
14
Choi, T. Y., Dooley, K. J. & Rungtusanatham, M. (2001). Supply networks and complex adaptive systems: control versus emergence. Journal of Operations Management, 19(3), 351–366.
15
Choi, T.Y. & Krause, D.R. (2006). The supply base and its complexity: Implications for transaction costs, risks, responsiveness, and innovation. Journal of Operations Management, 24(5), 637-652.
16
Chopra, S. & Meindl, P. (2007). Supply Chain Management: Strategy, Planning, and Operation. New Jersey: Pearson Education, Inc.
17
Christopher, M. (2012). Managing supply chain complexity: Identifying the requisite skills. In Supply Chain Forum: An International Journal, 13(2), 4-9.
18
Cohen, M. A., Agrawal, N. & Agrawal, V. (2006). Achieving breakthrough service delivery through dynamic asset deployment strategies. Interfaces, 36(3), 259-271.
19
de Leeuw, S., Grotenhuis, R. & van Goor, A. R. (2013). Assessing complexity of supply chains: evidence from wholesalers. International Journal of Operations & Production Management, 33(8), 960-980.
20
ElMaraghy, W., ElMaraghy, H., Tomiyama, T. & Monostori, L. (2012). Complexity in engineering design and manufacturing. CIRP Annals-Manufacturing Technology, 61(2), 793-814.
21
Gogus, O. & Boucher, T. O. (1998). Strong transitivity, rationality and weak monotonicity in fuzzy pairwise comparisons. Fuzzy Sets and Systems, 94(1), 133-144.
22
Gunasekaran, A., Hong, P. & Fujimoto, T. (2014). Building supply chain system capabilities in the age of global complexity: Emerging theories and practices. International Journal of Production Economics, (147), 189-197.
23
Hamta, N., Akbarpour Shirazi, M. & Ghobadi, SH. (2016, January). Modeling and Measurement of Structural Complexity in Assembly Supply Chain Networks. Tehran: 12th International Conference on Industrial Engineering.
24
(in Persian)
25
Hashemi, A., Butcher, T. & Chhetri, P. (2013). A modeling framework for the analysis of supply chain complexity using product design and demand characteristics. International Journal of Engineering, Science and Technology, 5(2), 150-164.
26
Huan, S. H., Sheoran, S. K. & Wang, G. (2004). A review and analysis of supply chain operations reference (SCOR) model. Supply Chain Management: An International Journal, 9(1), 23-29.
27
Isik, F. (2010). An entropy-based approach for measuring complexity in supply chains. International Journal of Production Research, 48(12), 3681-3696.
28
Jacobs, M. A. (2013). Complexity: Toward an empirical measure. Technovation, 33(4), 111-118.
29
Jafarnejad, A., Azar, A. & Ebrahimi, A. (2017). Mathematical Model of Supply Chain Order Management Relying on Robust Optimization and Activity-Based Costing. Journal of Industrial Management, 8(3), 333-358.
30
Karami, I. & Foukerdi, R. (2016). A Hybrid Fuzzy Prioritization - PROMETHEE Model for Supplier Selection. Journal of Industrial Management, 8(3), 467-486.
31
Karp, A. & Ronen, B. (1992). Improving shop floor control: an entropy model approach. International Journal of Production Research, 30(4), 923–938.
32
Kavilal, E. G., Venkatesan, S. P. & Kumar, K. H. (2017). An integrated fuzzy approach for prioritizing supply chain complexity drivers of an Indian mining equipment manufacturer. Resources Policy, 51, 204-218.
33
Makouei, A. & Madadi, A. R. (2005, February). Complexity in Supply Chains. Tehran: 1st National Conference on Logistics & Supply Chain. (in Persian)
34
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). Journal of Industrial Management, 8(3), 487-513.
35
Novak, S. & Eppinger, S. D. (2001). Sourcing by design: Product complexity and the supply chain. Management science, 47(1), 189-204.
36
Pathak, S. D., Day, J. M., Nair, A., Sawaya, W. J. & Kristal, M. M. (2007). Complexity and adaptivity in supply networks: Building supply network theory using a complex adaptive systems perspective. Decision Sciences, 38(4), 547–580.
37
Perona, M. & Miragliotta, G. (2004). Complexity management and supply chain performance assessment. A field study and a conceptual framework. International Journal of Production Economics, 90(1), 103–115.
38
Ramzanian, M. R., Rahmany, Z., Hoseinijou, S. A. & Mobasher Amini, R. A. (2013). Dealing with Supply Chain Complexity using Theory of Constraints Thinking Processes (Case Study of a Paper Manufacturing Firm). Management Research in Iran, 17(2), 125-144. (in Persian)
39
Safaei Ghadikolaei, A., Madhoshi, M. & Jamalian, A. (2016). Presenting a Conceptual Model for Sustainable Supplier Selection (A case study in SAIPA supply chain). Journal of Industrial Management, 7(4), 767-784.
40
Sahin, E., Vidal, L.A. & Benzarti, E. (2013). A framework to evaluate the complexity of home care services. Kybernetes, 42(4), 569-592.
41
Sarmad, Z., Bazargan, A. & Hejazi, E. (2014). Research Methods in the Behavioral Sciences (26th ed.). Tehran: Agah. (in Persian)
42
Schuh, G., Monostori, L., Csáji, B. C. & Döring, S. (2008). Complexity-based modeling of reconfigurable collaborations in production industry. CIRP Annals-Manufacturing Technology, 57(1), 445-450.
43
Serdarasan, S. (2013). A review of supply chain complexity drivers.Computers & Industrial Engineering, 66(3), 533-540.
44
Sivadasan, S., Efstathiou, J., Calinescu, A., Huaccho Huatuco, L. (2004). Supply Chain Complexity. in New, S. & Westbrook, R. (Eds.), Understanding Supply Chains: Concepts, Critiques and Futures, Oxford University Press, UK, pp. 133-163.
45
Subramanian, N., Abdulrahman, M. D. & Rahman, S. (2014). Sourcing complexity factors on contractual relationship: Chinese suppliers’ perspective. Production & Manufacturing Research, 2(1), 558-585.
46
Subramanian, N., Rahman, S. & Abdulrahman, M. D. (2015). Sourcing complexity in the Chinese manufacturing sector: An assessment of intangible factors and contractual relationship strategies. International Journal of Production Economics, 166, 269-284.
47
Sun, C. & Rose, T. (2015). Supply Chain Complexity in the Semiconductor Industry: Assessment from System View and the Impact of Changes. IFAC-PapersOnLine, 48(3), 1210–1215.
48
Towill, D.R. (1999). Simplicity wins: twelve rules for designing effective supply chains. Control - Institute of Operations Management, 25(2), 9-13.
49
Vogel, W. & Lasch, R. (2015). Approach for complexity management in variant-rich product development. Operational Excellence and Supply Chains, Conference Paper, Hamburg.
50
Wang, H., Zhu, X., Wang, H., Hu, S. J., Lin, Z. & Chen, G. (2011). Multi-objective optimization of product variety and manufacturing complexity in mixed-model assembly systems. Journal of Manufacturing Systems, 30(1), 16-27.
51
Zhu, X., Hu, S. J., Koren, Y. & Marin, S. P. (2008). Modeling of manufacturing complexity in mixed-model assembly lines. Journal of Manufacturing Science and Engineering, 130(5), 051013.
52
ORIGINAL_ARTICLE
Presenting a Multi-objective Optimization Model to Improve Energy Consumption Efficiency of Residential Buildings
Nowadays, considering the importance of the energy issue, it has become a major concern of economic topic. According to the energy balance sheet data, more than one third of the total energy consumption in Iran is consumed in the building sector. With regard to the massive waste of energy in the existing buildings, as well as the low efficiency of heating and cooling systems, seeking the right solution to reduce energy consumption in this sector is of utmost important. In this study, based on mathematical modeling, a solution has been presented to help us choose a proper combination of primary building materials and active or inactive air conditioning systems for residential buildings. It aimed to minimize both costs and thermal energy consumption of building materials. The proposed model has been utilized for a residential building in Tehran. Then, the optimal combinations of materials have been determined based on national building regulations (code 19).
https://imj.ut.ac.ir/article_63905_33979567808949993eb8f2cab160543b.pdf
2017-04-21
103
128
10.22059/imj.2017.214428.1007114
Energy Efficiency
Heating and cooling systems
Multi-objective optimization
residential buildings
simulation
Mahboobeh
Rahmani
mahboobeh_rahmani@ymail.com
1
MSc. of Industrial Engineering, University of Tehran, Tehran, Iran
AUTHOR
Hamed
Shakouri Ganjavi
hshakouri@ut.ac.ir
2
Associate Prof. of Industrial Engineering, University of Tehran, Tehran, Iran
AUTHOR
Aliyeh
Kazemi
aliyehkazemi@ut.ac.ir
3
Associate Prof. of Industrial Management, University of Tehran, Tehran, Iran
LEAD_AUTHOR
ابراهیمپور، ع.، معرفت، م. و محمدکاری، ب. (1383). بهینهسازی عایقکاری در ساختمانهای با استفاده مداوم در شرایط اقلیمی ایران از لحاظ بارهای حرارتی سالیانه. مجلۀ علمی پژوهشی مدرس، 17، 52-33.
1
ابراهیمیسالاری، ت.، محتشمی، م.، ضیایی، ع. و صالحنیا، ن. (1390). ممیزی انرژی در ساختمانهای مسکونی شهر مشهد و مقایسۀ کارایی مصرف گاز در سیستمهای گرمایشی متفاوت. اولین کنفرانس بینالمللی رویکردهای نوین نگهداشت انرژی، تهران، دانشگاه صنعتی امیرکبیر.
2
اربابیان، ه. (1380). بهینهسازی مصرف انرژی در ساختمان. سومین همایش ملی انرژی، تهران.
3
ارشاد لنگرودی، س.، اکبری، م.، ارشادلنگرودی، ا.، یوسفی، ع. (1382). فناوریهای نوین در ساخت پنجرههای با اتلاف انرژی پایین. سومین همایش بینالمللی بهینهسازی مصرف سوخت در ساختمان، تهران.
4
امدادی، آ. علیپور، ن.، نعمتیچاری، م. دورمحمدی، ح. (1381). اثرات عایق سازی حرارتی دیوارهای ساختمانی ساخته شده با بتن سبک در کاهش مصرف سوخت و هزینۀ تمام شده. دومین همایش بینالمللی بهینهسازی مصرف سوخت در ساختمان، تهران.
5
براتی، ب. (1382). نقش ویژگیهای اقلیمی ساختمانهای مسکونی در تأمین بهینۀ گرمایش و سرمایش. سومین همایش بینالمللی بهینهسازی مصرف سوخت در ساختمان، تهران.
6
پولادی، ع.، مجتهدزاه، ف. (1381). کاهش تلفات حرارتی و برودتی و صرفهجویی انرژی در دو ساختمان نمونه و همجوار با عایقکاری حرارتی (نمونۀ طراحی شده و در حال اجرا). دومین همایش بینالمللی بهینهسازی مصرف سوخت در ساختمان، تهران.
7
ترازنامه انرژی سال 1393. (1395). وزارت نیرو، معاونت امور برق و انرژی، دفتر برنامهریزی کلان برق و انرژی.
8
توکلیمقدم، ر.، علینقیان، م.، نوروزی، ن.، سلامتبخش، ع. (1390). حل یک مدل جدید برای مسئلۀ مسیریابی وسائط نقلیه با در نظر گرفتن ایمنی در حمل و نقل مواد خطرناک. مهندسی حمل و نقل، 2 (3)، 237-223.
9
حسینآبادی، س.، لشکری، ح.، سلیمانیمقدم، م. (1391). طراحی اقلیمی ساختمانهای مسکونی شهر سبزوار با تأکید بر جهتگیری ساختمان و عمق سایبان. جغرافیا و توسعه، 27، 116-103.
10
حیدری، ش. (1388). دمای آسایش حرارتی مردم شهر تهران. نشریۀ هنرهای زیبا ـ معماری و شهرسازی، (38)، 44-5.
11
خلجیاسدی، م. عابدی، ز.، شرعی، ن. (1388). سیستمهای ترکیبی خورشیدی راهحلی نوین برای گرمایش در ساختمانها. علوم و تکنولوژی محیط زیست، (3)، 28-15.
12
دقیق، ر.، مشتاق، ج. (1382). انتخاب بهینۀ سیستمهای شیشه و پنجره بهمنظور صرفهجویی در مصرف انرژی. سومین همایش بینالمللی بهینهسازی مصرف سوخت در ساختمان. تهران.
13
ذوالفقاری، ع.، معرفت، م. (1389). معیارهای نوین طراحی سیستمهای سرمایش و گرمایش در ساختمانها بر مبنای ایجاد شرایط آسایش حرارتی. دومین همایش فناوریهای نوین ساختمانی و صنعتی سازی، تهران.
14
رضایی حریری، م.ت.، فیاض، ر. (1380). محدودۀ آسایش حرارتی در تهران. محیطشناسی، 27(28)، 17-13.
15
سلطانی، م.، یوسفیکما، ح. (1384). بررسی اثر پنجرههای با کارایی حرارتی بالادر تغییر هزینهها و انرژی مصرفی ساختمان. چهارمین همایش بینالمللی بهینهسازی مصرف سوخت در ساختمان، تهران.
16
شاهمحمدی، ف.، عظیمی، ع.، کاظمزادهحنانی، س. (1385). شبیهسازی و بهینهسازی مصرف انرژی گرمایشی ساختمان. پنجمین همایش بهینهسازی مصرف سوخت در ساختمان، تهران.
17
شکوریگنجوی، ح.، نظرزاده، ج. (1383). مطالعۀ اثر تغییرات دمای هوا بر میانگین زمان مصرف روزانۀ انرژی الکتریکی در کشور. نشریۀ انرژی ایران، (20)، 40-27.
18
عابدی، ا.، خسرویان، ک. (1391). ارزیابی فنی و اقتصادی انواع چیلر باتوجه به قانون هدفمندشدن یارانهها، نهمین کنفرانس بینالمللی انرژی، تهران.
19
عربی، م.، دهقانی، م. (1389). بررسی فنی و اقتصادی سیستم های چیلر جذبی خورشیدی در ایران. مجلۀ مهندسی شیمی ایران، (46)، 72-60.
20
عظمتی، ع.، حسینی، ح. (1392). بررسی تأثیر جهتگیری ساختمانهای آموزشی بر بارهای حرارتی و برودتی در اقلیمهای مختلف. علوم و تکنولوژی محیط زیست، (57)، 157-147.
21
علیپور، ن.، امدادی، آ.، جلیلی، م.، صادقآزر، م. (1381). بررسی روشهای مختلف عایقسازی ساختمانهای بتنی دیوار باربر و مقایسه فنی و اقتصادی آنها. دومین همایش بینالمللی بهینهسازی مصرف سوخت در ساختمان، تهران.
22
کاظمی، م. (1392). ارزیابی انرژی چرخۀ عمر ساختمانها با در نظر گرفتن مصارف انرژی ساخت مصالح ساختمانی مختلف در ایران و انتخاب ساختمان با الگوی مناسب، پایاننامۀ کارشناسی ارشد، دانشگاه تهران.
23
گیلانی، س.، محمدکاری، ب. (1390). بررسی عملکرد گرمایشی گلخانههای خورشیدی در ساختمانهای مسکونی اقلیم سرد (نمونۀ موردی: شهر اردبیل). مجلۀ مهندسی مکانیک مدرس، (2)، 157-147.
24
محمودیزرندی، م.، پاکاری، ن.، بهرامی، ح. (1391). ارزیابی چگونگی تأثیرگذاری بام سبز در کاهش دمای محیط. فصلنامۀ علمی پژوهشی باغ نظر، (20)، 82-73.
25
معرفت، م.، امیدوار، ا. (1387). رابطهای برای پیشبینی ساعت به ساعت دمای پانلهای فلزی با دمای متغیر تطبیقی در سیستمهای سرمایش تابشی سقفی. مجلۀ فنی و مهندسی مدرس، (32)، 31-13.
26
معرفت، م.، امیدوار، ا. (1388). پدیدۀ نامطلوب سرمایش موضعی و تأثیر آن بر مصرف انرژی در سامانههای گرمایش از کف. مجلۀ فنی و مهندسی مدرس ـ مکانیک، (37)، 39-50.
27
مهدوینژاد، م. (1392). الگوی انرژی دوستی در ساختمان براساس رفتار حرارتی بام. نقش جهان، 3(2)، 42-35.
28
مهدوینژاد، م.، هادیانپور، م. (1394). مقایسۀ تحلیلی عملکرد نرمافزارهای شبیهساز مبحث نوزده مقررات ملی. نامۀ معماری و شهرسازی، (15)، 56-43.
29
یزدانداد، ح.، امامی، س.، هاشمی، ن. (1389). ارزشها و کارکردهای محیط زیستی بامهای سبز در توسعه پایدار شهری. نخستین همایش ملی توسعۀ پایدار شهری، دانشگاه گیلان.
30
Abedi, H. & Khosravian, K. (2010). Technical and economical comparison of compression and sbsorption chillers. The first national conference on chiller and cooling tower, Iran. (in Persian)
31
Alipour, N., Emdadi, A, Jalili, M. & Sadegh-Azar, M.S. (2002). Technical and economic analysis of different methods for insulation of load-bearing wall concrete buildings, The second international conference on fuel conservation in building, Iran. Tehran. (in Persian)
32
Antipova, E., Boer, D, Guillen-Gosalbez, G., Cabeza, L.F. & Jimenez, L. (2014). Multi-objective optimization coupled with life cycle assessment for retrofitting building. Energy and Buildings, 82, 92-99.
33
Arabi, M. & Dehghani, M.R. (2000). Technical and economical study on application of absorption solar chiller in Iran. Iranian Chemical Engineering Journal, 9(46), 60-72. (in Persian)
34
Arbabian, H. (2001). Optimization of energy efficiency in buildings. The 3rd National Energy Congress, Iran, Tehran. (in Persian)
35
Asadi, E., Da Silva, M.G., Antunes, C.H. & Dias, L. (2012). Multi-objective optimization for building retrofit strategies: A model and an application. Energy and Buildings, 44, 81-87.
36
Asadi, E., Da Silva, M.G., Antunes, C.H., Dias, L. & Glicksman, L. (2014). Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application. Energy and Buildings, 81, 444-356.
37
Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers. (2009). ASHRAE Handbook, Fundamentals.
38
Azemati, A.A. & Hosseini, H. (2014). Effect of Educational Building’s Direction on Cooling and Heating Loads in Different Regions. Journal of Environmental Science and Technology, 15(2), 147-157. (in Persian)
39
Banting, D., Li, J., Missios, P., Au, A, N. Currie, B.A. & Verrati, M. (2005). Report on the Environmental Benefits and Costs of Green Roof Technology for the city of Toronto. Ryerson University.
40
Barati, Gh. (2003). The role of climatic characteristics of residential buildings to provide optimal heating and cooling. The third international conference on fuel conservation in building. Iran, Tehran. (in Persian)
41
Bolattürk, A. (2008). Optimum insulation thicknesses for building walls with respect to cooling and heating degree-hours in the warmest zone of Turkey. Building and Environment, 43(6), 1055-1064.
42
Carreras, J., Pozo, C., Boer, D., Guillen-Gosalbez, G., Caballero, J.A., Ruiz-Femenia, R. & Jimenez, L. (2016). Systematic approach for the life cycle multi-objective optimization of buildings combining objective reduction and surrogate modeling. Energy and Buildings, 130, 506-518.
43
Daghigh, R. & Moshtagh, J. (2003). The optimal choice of glass and window systems for energy conservation. The third international conference on fuel conservation in building, Iran, Tehran. (in Persian)
44
Daouas, N. (2011). A study on optimum insulation thickness in walls and energy savings in Tunisian buildings based on analytical calculation of cooling and heating transmission loads. Applied Energy, 88(1), 156-164.
45
Delgarm, N., Sajadi, B., Kowsary, F. & Delgarm, S. (2016). Mluti-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied Energy, 170, 293-303.
46
Diakaki, C., Grigoroudis, E. & Kolokotsa, D. (2008). Towards a multi-objective optimization approach for improving energy efficiency in buildings. Energy and Buildings, 40(9), 1747-1754.
47
Diakaki, C., Grigoroudis, E. & Kolokotsa, D. (2013). Performance study of a multi-objective mathematical programming modelling approach for energy decision-making in buildings. Energy, 59, 534-542.
48
Ebrahimi.S, T., Mohtashami, M., Ziaee, A. & Salehnia, N. (2011). Energy auditing of residential buildings in Mashhad and comparision of the gas consumption efficiency for different heating systems. The first international conference on new approaches towards energy conservation, Tehran, Amir Kabir University. (in Persian)
49
Ebrahimpour, A., Marefat, M. & Mohammadkari, B. (2004). Insulation in buildings used regularly in Iran climatic conditions in terms of annual thermal loads. Modares Technical and Engineering, 17, 33-52. (in Persian)
50
Emdadi, A, Alipour, N., Namatichari, M. & Dourmohammadi, H. (2002). The effects of thermal insulation of building walls made of lightweight concrete in reducing fuel consumption and cost. The second international conference on fuel conservation in building, Iran, Tehran. (in Persian)
51
Energy Balance. Ministry of Energy. Available at: http://pep,moe.org.ir, Accessed 17 June 2016. (in Persian)
52
Ershad.L, S, Akbari, M. & Yousefi, A.A. (2003). New technologies in the construction of windows with low energy loss. The third international conference on fuel conservation in building, Iran, Tehran. (in Persian)
53
Fanger, P.O. (1970). Thermal comfort: analysis and applications in environmental engineering. New York.
54
Gilani, S. & Mohammadkari, B. (2011). Investigation of Greenhouse’s Thermal Performance in Residential Buildings of Cold Climate Case Study: City of Ardebil. Modares Mechanical Engineering, 11(2), 147-157. (in Persian)
55
Hart, M. & De Dear, R. (2004). Weather sensitivity in household appliance energy end-use, Energy and Buildings, 1, 161-174.
56
Heidari, SH. (2009). Comfort Temperature of Iranian People in City of Tehran. Honar-Ha-Ye-Ziba (Memari-Va-Shahrsazi), 17, 5-14. (in Persian)
57
Hosseinabadi, S., Lashkari, H. & Salmanimoqadam, M. (2012). Climatic Design of Residential Building of Sabzevar with Emphasis on Building Orientation and Depth of Canopy. Geographiy and Development Iraninan Journal, 10(27), 103-116. (in Persian)
58
Kazemi. M (2014). Life cycle energy assessment of buildings with consideration energy consumption materials production in Iran to choose with appropriate pattern. Msc Thesis, University of Tehran. (in Persian)
59
Khalaji. A., Abedi, Z. & Sharee-Heidari, N. (2009). Solar combi-systems: a new solution for heating buildings. Journal of Environmental Science and Technology, 11(3), 15-28. (in Persian)
60
Mahdavinejad, M.J. & Fakhari, M. (2013). Stablishment of Optimum Designing Pattern in Buildings Roof Shape Based on Energy Loss. Naqshejahan, 3(2), 35-42. (in Persian)
61
Mahdavinejad, M.J. & Hadiyanpour, M. (2015). Analytical Comparison of the Performance of Simulation Software Programs for Iranian Building Code 19. Journal of Architecture and Urban Planning, 8(15), 43-56. (in Persian)
62
Mahmoody, Z., Pakari, N. & Bahrami, H. (2012). The effect of green roof on reducing environment temperature. Bagh-I-Nazar, 9(20), 73-82. (in Persian)
63
Marefat, M. & Omidvar, A. (2011). Undesired Local Cooling Phenomenon and Its Effect on Energy Consumption in Floor Heating Systems. Modares Mechanical Engineering, 9(1), 39-50. (in Persian)
64
Marefat. M. & Omidvar, A. (2014). Effects of Adaptive Temperature Metal Panels on Thermal Comfort and Energy Consumption of Radiant Ceiling Cooling Systems. Modares Mechanical Engineering, 8(1), 13-32. (in Persian)
65
Mihalakakou, G. & Ferrante, A. (2000). Energy conservation and potential of a sunspace: sensitivity analysis. Energy Conversion and Management, 41, 1264-1247.
66
Ortiga, J., Carles Bruno, J., Coronas, A. & Grossman, I.E. (2007). Review of optimization models for the design of polygeneration systems in district heating and cooling networks. The 17th European Symposium on Computer Aided Process Engineering.
67
Pouladi, A. & Mojtahedzadeh, F. (2002). Heating and cooling loss reduction and energy savings in two adjacent sample buildings by using thermal insulation. The second international conference on fuel conservation in building, Iran, Tehran. (in Persian)
68
Rezayi.H, M.T. & Fayyaz, R. (2001). Thermal comfort Condition in Tehran. Journal of Environmanetal Studies, 27(28), 13-17. (in Persian)
69
Shahmohammadi, F., Azimi, A. & Kazemizadeh, H. S. (2006). Simulation and optimization of heating energy consumption of buildings. The 5th international conference on fuel conservation in building, Iran, Tehran.
70
(in Persian)
71
Shakouri.G, H. & Nazarzadeh, J. (2004). Analysis of temperature changes effect on the average of daily electrical energy consumption in Iran. Iranian Journal of Energy, 9(1), 27-40. (in Persian)
72
Soltani, M. & Yousofikoma, H. (2005). Investigating the effect of high thermal efficiency windows on changing cost and energy consumption of buildings. The 4th international conference on fuel conservation in building, Iran, Tehran. (in Persian)
73
Tavakolimoghadam, R., Alinaghian, M., Norouzi, N. & Salamatbakhsh, AR. (2011). Solving a NewVehicle Routing Problem Considering Safety in Hazardous Materials Transportation. Quarterly Journal of Transportation Engineering, 2(3), 223-237. (in Persian)
74
Wang, F., Yoshida, H. & Ono, E. (2009). Methodology for optimizing the operation of heating/cooling plants with multi-heat-source equipments. Energy and Buildings, 41(4), 416-425.
75
Yazdandad, H, Emami, S. & Hashemi, N. (2010). Environmental values and functions of green roofs on urban sustainable development. The first national conference on sustainable urban development, Iran, Gilan. (in Persian)
76
Zinzi, M. & Agnoli, S. (2012). Cool and green roofs. An energy and comfort comparison between passive cooling and mitigation urban heat island techniques for residential buildings in the Mediterranean region, Energy and Buildings, 55, 66-76.
77
Zolfaghari, A. & Marefat, M. (2010). New criteria for designing of heating and cooling systems in buildings based on thermal comfort conditions. The Second Conference on New Building Technologies and Industrialization, Iran, Tehran. (in Persian)
78
ORIGINAL_ARTICLE
Seeking Simulation-based Optimization of Job shop Scheduling in Small and Medium Enterprises to Minimize the Cost of Tardiness and Earliness of Activities
Determining the optimal sequence of jobs in job shop scheduling for small and medium enterprises, affect the machine productivity, earliness and tardiness costs of delivery. The deterministic variant of the problem is well-known to be NP-Hard. If random elements are introduced into the problem, the level of complexity goes higher. Hence, many priority rules have been developed to tackle stochastic job shop scheduling problem. However, to devise a better solution approach, simulation-optimization approach might be used. In this study, a mathematical model was developed for job shop scheduling with random process times and possible machine breakdowns. Then, a simulation-optimization model was applied to choose among a list of priority rules using Rockwell Arena 14. Finally, a numerical example was used to evaluate the quality of the model. Results showed that the rule Longest Processing Time (LPT) yields the lowest total earliness and tardiness costs. However, the total costs of the following rules are also acceptable: First in First out (FIFO), Last in First out (LIFO), Earliest Due Date (EDD) and Slack time (Slack).
https://imj.ut.ac.ir/article_63902_8aa8bf202c281703773ebd14a5011e53.pdf
2017-04-21
129
146
10.22059/imj.2017.128474.1006891
Cost of tardiness and earliness
Job shop
Optimization
Queue
simulation
Seyed Mojtaba
Sajadi
msajadi@ut.ac.ir
1
Assistant Prof. in New Business Group, Faculty of Entrepreneurship, Tehran University, Tehran, Iran
LEAD_AUTHOR
Sadegh
Shahbazi
shahbazi.sadegh@ut.ac.ir
2
Ph.D Candidate of Industrial Engineering, Faculty of Industrial Engineering, Tehran University, Tehran, Iran
AUTHOR
زندیه، م.، احمدی، ا. (1393). زمانبندی مقاوم و پایدار برای محیط کار کارگاهی منعطف با شکست تصادفی ماشین. نشریۀ مدیریت صنعتی، 6(3)، 534- 511.
1
سلیمی فرد، خ.، انصاری، م. (1395). مدلسازی و شبیهسازی سامانة ترافیک شهری با شبکههای پتری رنگین. نشریۀ مدیریت صنعتی، 8(3)، 404- 381.
2
علیزاده، ل.، نورالسناء، ر.، رئیسی، ص. (1394). بهینهسازی همزمان چند هدفه فرایند دادرسی کیفری به کمک شبیهسازی کامپیوتری گسسته ـ پیشامد و طراحی آزمایشها. نشریۀ مدیریت صنعتی، 7(1)، 82- 65.
3
Alizadeh, L., Noorossana, R. & Raissi, S. (2015). Multi-objective optimization of criminal trial process using descrete event computer simulation and design of experiment. Industrial Management, 7(1), 65-82. (in Persian)
4
Azadivar, F. (1999). Simulation optimization methodologies, Presented at the Proceedings of the 31st conference on winter simulation: Simulation a bridge to the future - Volume 1, Phoenix, Arizona, United States.
5
Banks, J. & Nelson, B. L. (2010). Discrete-Event System Simulation: Prentice Hall.
6
Blackstone, J. H., Phillips, D. T. & Hogg, G. L. (1982). A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research, 20(1), 27-45.
7
Chan, F. T. S., Chan, H. K., Lau, H. C. W. & Ip, R. W. L. (2003). Analysis of dynamic dispatching rules for a flexible manufacturing system. Journal of Materials Processing Technology, 138(1-3), 325-331.
8
Hashim, S. A. M. (2017). Simulation for reducing energy consumption of multi core low voltage power cable manufacturing system. Journal on Technical and Vocational Education, 1(2), 1-10.
9
Jain, A. & Meeran, S. (1999). A State-Of-The-Art Review of Job-Shop Scheduling Techniques (113ed.) Available: http://citeseerx.ist.psu.edu/viewdoc/ summary?doi= 10.1.1.54.8522.
10
Klemmt, A., Horn, S., Beier, E. & Weigert, G. (2007). Investigation of modified heuristic algorithms for simulation-based ptimization. In Electronics Technology, 30th International Spring Seminar on, pp. 24-29.
11
Klemmt, A., Horn, Weigert, S. G. & Wolter, K.J. (2009). Simulation-based optimization vs.mathematical programming: A hybrid approach for optimizing scheduling problems. Robotics and Computer-Integrated Manufacturing, 25(6), 917-925.
12
Pinedo, M. L. (2012). Scheduling: Theory, Algorithms, and Systems: Springer.
13
Rane, A. B., Sunnapwar, V. K., Chari, N. R., Sharma, M. R. & Jorapur, V. S. (2017). Improving performance of lock assembly line using lean and simulation approach. International Journal of Business Performance Management, 18(1), 101-124.
14
Salimifard, K. & Ansari, M. (2016). Modeling and Simulation of Urban Traffic Network Using Colored Petri Nets. Industrial Management, 8(3), 381-404. (in Persian)
15
Sharma, S. K., Suraj, B. V. & Routroy, S. (2017). Positioning of Inventory in Supply Chain Using Simulation Modeling. The IUP Journal of Supply Chain Management, 13(2), 20-32.
16
Sajadi, S. M., Esfahani, M. M. S. & Sörensen, K. (2011). Production control in a failure-prone manufacturing network using discrete event simulation and automated response surface methodology. The International Journal of Advanced Manufacturing Technology, 53(1-4), 35-46.
17
Teles, J., Lopes, R. B. & Ramos, A. L. (2017). A Simulation-Based Analysis of a Cork Transformation System. In Engineering Systems and Networks (pp. 3-11). Springer, Cham Pinedo, M. L. (2012). Scheduling: Theory, Algorithms, and Systems, Springer.
18
Vieira,G. E., Herrmann, J. W. & Lin, E. (2000). Predicting the performance of escheduling strategies for parallel machine systems. Journal of Manufacturing Systems, 19(4), 256-266.
19
Weng, M. X. & Ren, H. (2006). An efficient priority rule for scheduling job shops to minimize mean tardiness. IIE Transactions, 38(9), 789-795.
20
Yan, Y. & Guoxin, W. (2007). A job shop scheduling approach based on simulation optimization, in Industrial Engineering and Engineering Management, IEEE International Conference on, pp. 1816-1822.
21
Zandieh, M. & Ahmadi, E. (2015). Robust and stable scheduling for FJSP under random machine breakdown by use of genetic algorithm and simulation. Industrial Management, 6(3), 511-534. (in Persian)
22
ORIGINAL_ARTICLE
A Mathematical Model Based on Capacitated Vehicle Routing Problem with Time Lapses
for Garbage Collection
Producing various types of waste and related environmental problems, has faced urban management with many problems in the areas like: collection, transportation and waste disposal. Applying a good way to reduce the costs of collecting waste seems necessary, because collection and transportation of the waste needs allocation of a significant part of garbage management budget. In the present study, a mathematical model is presented for waste collection by which we can reduce the costs of collecting the waste by minimizing the distance for the trucks. The model is flexible enough to manage collecting the waste of a node in different separate times. Then, some small-scale issues were solved using CPLEX software and meta-heuristic algorithm and the results were compared. The data for the case study were collected and the meta-heuristic algorithm was used to determine the vehicle schedule. Finally, a mathematical model was used to decide about the number of vehicles required
https://imj.ut.ac.ir/article_63903_7f2fa2c42e9c57953ccc54c51204716b.pdf
2017-04-21
147
166
10.22059/imj.2017.217587.1007124
Capacitated vehicle routing problem time windows
garbage collection
Simulated annealing algorithm
Urban Management
Hamid
Shahbandarzadeh
shabandarzadeh@gmail.com
1
Associate Prof. of Industrial Engineering, Persian Gulf University, Bushehr, Iran
LEAD_AUTHOR
Mohammad
Hassan Najmi
m.h.najmi.66@gmail.com
2
MSc Student of Industrial Engineering, Persian Gulf University, Bushehr, Iran
AUTHOR
Alireza
Ataei
ataei@pgu.ac.ir
3
Assistant Prof., Faculty of Science, Numerical Analysis, Persian Gulf University, Bushehr, Iran
AUTHOR
توکلی مقدم، ر.، علینقیان، م. (1388). ارائه و حل مدل برنامهریزی ریاضی جدید برای مسیریابی وسائط نقلیه در حالت رقابتی: یک مطالعۀ موردی. پژوهشنامۀ حمل و نقل، 6 (4)، 323-311.
1
ربانی، م.، توکلی مقدم، ر.، شریعت، م. ع.، صفایی، ن. (1385). ﺣﻞ ﻣﺴﺌﻠﻪ ﻣﺴﯿﺮﯾﺎﺑﯽ وﺳﺎﯾﻞ ﻧﻘﻠﯿﻪ ﺑﺎ ﭘﻨﺠﺮﻩﻫﺎی ﺯﻣﺎﻧﯽ ﻧﺮﻡ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﯾﮏ ﺍﻟﮕﻮﺭﯾﺘﻢ ﻓﺮﺍ ﺍﺑﺘﮑﺎﺭی ﺗﻠﻔﯿﻘﯽ. ﻧﺸﺮﯾﻪ ﺩﺍﻧﺸﮑﺪﻩ ﻓﻨﯽ دانشگاه تهران ، 40(4)، 476-469.
2
مجلسی، م. (1390). نقش مشارکتهای مردمی در سیستم مکانیزۀ جمعآوری زباله. تهران: سومین همایش ملی مدیریت پسماند. یکم و دوم اردیبهشت، تهران.
3
مجلسی، م.، زمانی، ا. ا.، مهدیپور، ف.، شمسایی، و.، شریفی ملک سری، ه.، دروار، پ. (1392). تجزیه و تحلیل هزینۀ جمعآوری و حمل و نقل پسماند منطقۀ 1 شهر بندرعباس. فصلنامۀ بهداشت در عرصه، 1(1)، 45-37.
4
مریخ بیات، ف. (1393). الگوریتمهای بهینهسازی الهامگرفته از طبیعت. تهران: نص.
5
مهدوی، ا.، توکلی مقدم، ر.، قاضیزاده هاشمی، س. م. (1389). مسیریابی وسایط نقلیه و تعیین تعداد ماشین های جمعآوری زباله با استفاده از یک روش فرا ابتکاری (یک مطالعۀ موردی). پژوهشنامۀ حمل و نقل ، 7(1)، 101-95.
6
Chalkias, A. & Lasarid, E. (2009). Optimizing municipal solid waste collection using GIS. Waste management, 47, 776- 790.
7
Chen, Y., Hwang Wang, C. & Lin, J. (2015). Amulti-objective geographic information system for route selectionof nuclearwaste transport. Omega, 36, 363-372.
8
Claassen, F. & Hendriks, T. (2007). An application of Special Ordered Sets to a periodic milk collection problem. European Journal of Operational Research, 180 (2), 754-769.
9
Dhahri, A., Zidi, K. & Ghedira, K. (2014). Variable Neighborhood Search Based Set covering ILP model for the Vehicle Routing Problem with time windows. Procedia Computer Science, 29, 844-854.
10
Faiz, S., Krichen, S. & Inoubli, W. (2014). A DSS based on GIS and Tabu search for solving the CVRP: The Tunisian case. The Egyptian Journal of Remote Sensing and Space Sciences, 17, 105-110.
11
Flavia, B., Guillermo, D., Larumbe, F. & Marenco, J. (2012). A Method for Optimizing Waste Collection Using Mathematical Programming: A Buenos Aires Case Study. Waste Management & Research, 30(3), 311-324.
12
Fooladi, S., Fazlollahtabar, H. & Mahdavi, I. (2013). Waste Collection Vehicle Routing Problem Considering Similarity Pattern of Trashcan. International Journal of Applied Operational Research, 3, 105-111.
13
Inghels, D., Dullaert, W. & Vigo, D. (2016). A service network design model for multimodal municipal solid waste transport. European Journal of Operational Research, 254(1), 68-79.
14
Mahdavi, I., Tavakoli Moghadam, R. & Ghazi zade Hashemi, S. (2010). Vehicle Routing Problem and determine the number of cars garbage collection using a meta-heuristic method (a case study). Journal of Transportation, 7(1), 95-101. (in Persian)
15
Majlesi, m. (2007). The role of public participation in a mechanized waste collection system. Third National Conference on Waste Management. Tehran,
16
(in Persian)
17
Majlesi, M., Zamani, A., Mahdipor, F., Shmsaei, V., Sharifi Maleksari, H. & Darvar, P. (2013). Analysis of the cost of collecting and transporting waste of area of Bandar Abbas city. Journal of Health in the field, 1(1), 37-45.
18
(in Persian)
19
Markov, I., Varone, S. & Bierlaire, M. (2016). Integrating a heterogeneous fixed fleet and a flexible assignment of destination depots in the waste collection VRP with intermediate facilities. Transportation Research Part B: Methodological, 84, 256-273.
20
Merikh Bayat, F. (2014). Optimization algorithms inspired by nature. Tehran: Nas.(in Persian)
21
Montoya-Torres, J., Franco, J., Isaza, S., Jiménez, H. F. & Herazo-Padilla, N. (2015). A literature review on the vehicle routing problem with multiple depots. Computers & Industrial Engineering, 79, 115-129.
22
Sbihi, A., & Eglese, R. (2010). Combinatorial optimization and Green Logistics. Annals of Operations Research, 125, 159-175.
23
Tavakoli Moghadam, R. & Alinaghiyan, M. (2009). Presentation and solving a new mathematical programming model for Competitive Vehicle Routing Problem: A case study. Journal of Transportation, 6(4), 311-323.
24
(in Persian)
25
Tavakoli Moghadam, R., Rabbani, M., Shariat, M. & Safaei, N. (2006). Solving Vehicle Routing Problem with soft time windows using a compilation meta-heuristic algorithm. Journal of Technical University of Tehran, 40(4), 469-476. (in Persian)
26
Toth, P. & Vigo, D. (2014). Vehicle Routing Problems, Methods, and Applications. The Society for Industrial and Applied Mathematics and the Mathematical Optimization Society. SIAM, Italy.
27
Wy, J. & Byung-In, k. (2013). A hybrid meta heuristic approach for the rollon–rolloff vehicle routin problem. Computers & Operations Research, 40, 1947-1952.
28
ORIGINAL_ARTICLE
Ranking and Prediction Formula of Time Waste Causes in Residential Building Projects
by LASSO Method
One of the effective ways in reducing time is eliminating or reducing time wastes. Wastes are activities and events that are prolonging the process of producing but would not provide any value-added. For identification, after literature review, interview and thematic analysis were used and according to three factors presence in executing, controllability and direct effect on time, 8 causes were chosen among 35 ones. After that, by distributing questionnaire and using LASSO method, causes ranking and the formula of time waste prediction were obtained. Performing most tasks in site, rework and contractor delay were found out as the most important causes in wasting time. The reason of uniqueness of this paper is using LASSO method for ranking and providing a formula for time waste prediction that not provided in any internal or external articles before now. This prediction is useable 5 to 7 floor residential building projects. According to formula if any 8 causes exist in project process, the amount of waste will be 38% of executing time. By quantifying the amount of time waste, decision making about how to deal with causes is simpler.
https://imj.ut.ac.ir/article_63906_e31aa0aa5f2c0da0838c5bcc9bc33aea.pdf
2017-04-21
167
188
10.22059/imj.2017.230790.1007215
Decision making
LASSO method
Prediction formula
Ranking
Residential building
time
Waste causes
Mahmood
Golabchi
golabchi@ut.ac.ir
1
Prof., Faculty of School of Architecture, Tehran University, Tehran, Iran
AUTHOR
Mahdi
Mohammadi Ghazimahalleh
maghazi@ut.ac.ir
2
Ph.D. Candidate in Project Management, School of Architecture, Tehran University, Tehran, Iran
LEAD_AUTHOR
حبیبپور، ک. صفری شالی، ر. (1393). راهنمای جامع کاربرد SPSS در تحقیقات پیمایشی. تهران: لویه.
1
ربانی، م.، رضایی، ک.، معنویزاده، ن.، عبادیان، م. (1385). تولید ناب. تهران: آتنا.
2
سرمد، ز.، بازرگان، الف. (1384). روشهای تحقیق در علوم رفتاری. تهران: آگاه.
3
Abd El-Razek, M. E., Bassioni, H. A. & Mobarak, A. M. (2008). Causes of delay in building construction projects in Egypt. Journal of Construction Engineering and Management, 134(11), 831–841.
4
Aibinu, A. A. & Odeyinka, H. A. (2006). Construction Delays and Their Causative Factors in Nigeria. Journal of Construction Engineering and Management, 132(7), 667–677.
5
Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
6
Cresswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Sage Publications Inc., Thousand Oaks, CA.
7
Daubechies, I., Defrise, M. & De Mol, C. (2004). An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 57(11), 1413–1457.
8
Ghoddousi, P. & Hosseini, M. R. (2012). A survey of the factors affecting the productivity of construction projects in Iran. Technological and Economic Development of Economy, 18(1), 99–116.
9
González, P., González, V., Molenaar, K. & Orozco, F. (2013). Analysis of causes of delay and time performance in construction projects. Journal of Construction Engineering and Management, 140(1), 1-9.
10
Gündüz, M., Nielsen, Y. & Özdemir, M. (2012). Quantification of Delay Factors Using the Relative Importance Index Method for Construction Projects in Turkey. Journal of Management in Engineering, 29(April), 133–139.
11
Habibpour Gatabi, K.,Safari Shali, R. (2015). Comprehensive Manual for using SPSS in survey researches. Tehran, Looyeh. (in Persian)
12
Habibpour Gatabi, K.,Safari Shali, R. (2015). Comprehensive Manual for using SPSS in survey researches. Tehran, Looyeh. (in Persian)
13
Hastie, T., Tibshirani, R. & Friedman, J. (2009). Unsupervised learning. In The elements of statistical learning (pp. 485–585). Springer.
14
Khoshgoftar, M., Bakar, A. H. A. & Osman, O. (2010). Causes of delays in Iranian construction projects. International Journal of Construction Management, 10(2), 53–69.
15
Koushki, P. A., Al Rashid, K. & Kartam, N. (2005). Delays and cost increases in the construction of private residential projects in Kuwait. Construction Management and Economics, 23(3), 285–294.
16
Larsen, J. K., Shen, G. Q., Lindhard, S. M. & Brunoe, T. D. (2015). Factors Affecting Schedule Delay, Cost Overrun, and Quality Level in Public Construction Projects. Journal of Management in Engineering, 32(1), 10.
17
Liker, J. K. (2005), The Toyota Way-14 Management Principles from the World's Greatest Manufacturer, McGraw-Hill, NewYork, NY.
18
Lo, T. Y., Fung, I. W. & Tung, K. C. (2006). Construction Delays in Hong Kong Civil Engineering Projects. Journal of Construction Engineering and Management, 132(6), 636–649.
19
Marzouk, M. M. & El-Rasas, T. I. (2014). Analyzing delay causes in egyptian construction projects. Journal of Advanced Research, 5(1), 49–55.
20
Mukuka, M. J., Aigbavboa, C. O. & Thwala, W. D. (2014). A Theoretical Review of the Causes and Effects of Construction Projects Cost and Schedule Overruns. International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 16–19.
21
Neuendorf, K. A. (2002). The content analysis guidebook.Sage Publications Inc., Thousand Oaks, CA.
22
Phaniraj, K. & Sreekumar, K. S. (2014). Practical Factors Affecting Delay in High Rise Construction – A Case Study in a Construction Organization. International Journal of Engineering Research & Technology (IJERT), 3(5), 875–881.
23
Rabbani, M., Rezaei, K., Manavizadeh, N. & Ebadian, M. (2006). Lean production (2th ed.). Atena, Tehran.
24
Rabbani, M., Rezaei, K., Manavizadeh, N. & Ebadian, M. (2006). Lean production (2th ed.). Atena, Tehran. (in Persian)
25
Sambasivan, M. & Soon, Y. W. (2007). Causes and effects of delays in Malaysian construction industry. International Journal of Project Management, 25(5), 517–526.
26
Sarmad, Z. & Bazargan, A. (2005). Behavorial research methodology. Agah, Tehran. (in Persian)
27
Sarmad, Z. & Bazargan, A. (2005). Behavorial research methodology. Agah, Tehran. (in Persian)
28
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267–288.
29
Womack, J. P., Jones, D. T. & Roos, D. (1990). Machine that changed the world. Simon and Schuster, 1990
30
Zakeri, M., Olomolaiye, P. O., Holt, G. D. & Harris, F. C. (1996). A survey of constraints on Iranian construction operatives’ productivity. Construction Management & Economics, 14(5), 417–426.
31
ORIGINAL_ARTICLE
Resilient Supply Network Design of a
Two Stage Supply Chain and Risk Analysis
Using Stochastic Programming
(A Case Study of GPPS Supply Chain)
This paper proposes a multi-period resilient supply network model for a two stage supply chain in which one of the main raw materials should be bought from different suppliers and various products should be produced for customers in several manufacturer production sites. We used two-stage stochastic programming provided that the customers have uncertain demands and the suppliers propose uncertain prices from time to time. In contrast with previous researches, we considered disruption risks of supply, mitigation strategies and contingency plans against risks to improve the resilience of our supply network. Also, to increase the authenticity of our model, we consider the production planning in each production site simultaneously. The supply network design was analyzed from a risk-neutral and a risk-averse decision maker point of view. To illustrate the applicability of our proposed approach, computational results of using this model in a real-life case was presented. Finally, to investigate the influence of cost parameter changes on the results of the model, we performed a sensitive analysis.
https://imj.ut.ac.ir/article_63907_caaf392d9dd524e96503725e4a7e7dbe.pdf
2017-04-21
189
216
10.22059/imj.2017.208998.1007078
Resilient supply network
Risk consideration
Scenario reduction
Two-stage stochastic program
Ali
Mohaghar
amohaghar@ut.ac.ir
1
Prof. of Industrial Management, Tehran University, Tehran, Iran
AUTHOR
Nima
Garusi Mokhtarzadeh
mokhtarzadeh@ut.ac.ir
2
Assistant Prof. of Industrial Management, Tehran University, Tehran, Iran
AUTHOR
Mohammad
Modarres Yazdi
modarres@sharif.edu
3
Prof., Faculty of Industrial Engineering, Sharif University, Tehran, Iran
AUTHOR
Moein
Hajimaghsoudi
hmaghsoudi@yahoo.com
4
Ph.D. Candidate of Industrial Management, University of Tehran, Tehran, Iran
LEAD_AUTHOR
دری، ب.، حمزهای، ا. (1389). تعیین استراتژی پاسخ به ریسک در مدیریت ریسک بهوسیلۀ تکنیک ANP (مطالعۀ موردی: پروژۀ توسعۀ میدان نفتی آزادگان شمالی)، نشریۀ مدیریت صنعتی، 2 (4)، 92-75.
1
سلسبیل، م.، شفیعا، م. ع.، پیشوایی، م.، شهانقی، ک. (1394). برنامهریزی تاکتیکی استوار زنجیرة تأمین جهانی سه سطحی تحت شرایط اختلال تحریم با در نظر گرفتن عمر قفسهای (مطالعۀ موردی: زنجیرۀ تأمین دارو)، نشریۀ مدیریت صنعتی، 7(2)، 332-305.
2
Ahmed, S. (2006). Convexity and decomposition of mean risk stochastic programs. Mathematical Programming, 106(3), 433-446.
3
Ayhan, M.B. & Kilic, H.S. )2015(. A two stage approach for supplier selection problem in multi item/ multi-supplier environment with quantity discounts. Computers & Industrial Engineering, 85, 1-12.
4
Ayhan, M.B. (2013). Fuzzy TOPSIS Application for supplier selection problem. International Journal of Information Business and Management, 5 (2), 159-174.
5
Burt, D.N., Dobler, D.W. & Starling, S.L. (2004). World Class Supply Management: The Key to Supply Chain Management (Seventh ed). McGrawHill, Boston, MA.
6
Carbone, J., (1999). Evaluation programs determine top suppliers. Purchasing, 127 (8), 31–35.
7
Cárdenas-Barrón, L.E., González-Velarde, J.L. & Treviño-Garza, G. )2015(. A new approach to solve the multi-product multi-period inventory lot sizing with supplier selection problem, Computers & Operations Research, 64, 225-232.
8
Eppen, G.D., Martin, R.K. & Schrage, L. (1989). OR practice- a scenario approach to capacity planning. Operations Research, 37(4), 517–527.
9
Fábián, C.I. (2013). Computational aspects of risk-averse optimization in two-stage stochastic models. Available in: http://www.optimization-online.org/DB_ FILE/2012 /08/3574.pdf.
10
Ghodsypour, S.H. & O’Brien, C. (1998). A Decision Support System for Supplier Selection Using an Integrated Analytic Hierarchy Process and Linear Programming. International Journal of Production Economics, 56-57, 199-212.
11
Govindan, K. & Fattahi, M. (2015). Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain. International Journal of Production Economics, 183, 680-699.
12
Haldar, A. & Ray, A. )2014(. Resilient supplier selection under a fuzzy environment. International Journal of Management Science and Engineering Management, 9(2), 147-156.
13
Handfield, R.B. & McCormack, K. (2008). Supply Chain Risk Management: Minimizing Disruptions in Global Sourcing. Series on Resource Management. New York, Auerbach Publications.
14
Heidarzade, A., Mahdavi, I. & Mahdavi-Amiri, N. )2015(. Supplier Selection Using a Clustering Method Based on a New Distance for Interval Type-2Fuzzy Sets: A Case Study. Applied Soft Computing Journal, 38(c), 213-231.
15
Hou, A. Z. Zeng, and L. Zhao, "Coordination with a backup supplier through buy-back contract under supply disruption," Transportation Research Part E: Logistics and Transportation Review, 46, 881-895.
16
Inderfurth, K., Kelle, P. & Kleber, R. (2013). Dual sourcing using capacity reservation and spot market: Optimal procurement policy and heuristic parameter determination. European Journal of Operational Research, 225(2), 298-309.
17
Inderfurth, K. & Kelle, P. )2011(. Capacity reservation under spot market price uncertainty. International Journal of Production Economics, 133(1), 272-279.
18
Keyvanloo, A., Kimiagari, A.M. & Esfahanipour, A. (2014). Risk analysis of sourcing problem using stochastic programming, Industrial Engineering, 21(3), 1034-1043.
19
Krajewsld, L.J. & Ritzman, L.P. (1996). Operations Management Strategy and Analysis. London, Addison-Wesley Publishing Co.
20
Linthorst, M.M. & Telgen, J. (2007(. Public Purchasing Future: Buying from Multiple Sources. Advancing Public Procurement: Practices, Innovation and Knowledge-Sharing. Academics Press, Boca Raton, FL., 471–482.
21
Manuj, I. & Mentzer, J.T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, 38 (3), 192–223.
22
Meena P.L., Sarmah S.P. & Sarkar S.A. )2011(. Sourcing decisions under risks of catastrophic event disruptions. Transportation Research Part E: Logistics and Transportation Review, 47(6), 1058–1074.
23
Moghaddam, K. )2015(. Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 42, 6237-6254.
24
Moritz, S. & Pibernik, R. (2008). The optimal number of suppliers in the presence of volume discounts and different compensation potentials – an analytical and numerical analysis. Working Paper. European Business School Research.
25
Nam, S.H., Vitton, J. & Kurata, H. (2009). Robust supply base management: determining the optimal number of suppliers utilized by contractors. International Journal of Production Economics, 134(2), 333-343.
26
Neiger, D., Rotaru, K., & Churilov, L. (2009). Supply chain risk identification with value-focused process engineering. Journal of Operations Management, 27, 154-168.
27
Norrman, A. & Jansson, U. (2004). Ericsson’s proactive supply chain risk management approach after a serious sub-supplier accident. International Journal of Physical Distribution & Logistics Management, 34 (5), 434-456.
28
Noyan N. )2012(. Risk-averse two-stage stochastic programming with an application to disaster management. Computers and Operations Research. (39), 541–559.
29
Oke, A. & Gopalakrishnan, M. (2009). Managing disruptions in supply chains: A case study of a retail supply chain. International Journal of Production Economics, 118, 168–174.
30
Pazhani, S., Ventura, J.A. & Mendoza, A. )2015(. A Serial Inventory System with Supplier Selection and Order Quantity Allocation considering Transportation Costs. Applied Mathematical Modelling,40(1), 612-634.
31
Rawls, C.G. & Turnquist, M.A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation Research Part B: Methodological, 44(4), 521–534.
32
Ross, S.M. (2007). Introduction to Probability Models. Academic Press.
33
Ruiz-Torres, A., Mahmoodi, F. & Zeng, A. (2013). Supplier selection model with contingency planning for supplier failures. Computers & Industrial Engineering, 66(2), 374-382.
34
Sawik, T. (2011). Selection of supply portfolio under disruption risks, Omega, vol, 39, 194-208.
35
Sawik, T. (2013). Selection of resilient supply portfolio under disruption risks. Omega, 41, 259–269.
36
Schoenherr, T., Modi, S.B., Benton, W.C., Carter, C.R., Choi, T.Y., Larson, P.D., Leenders, M.R., Mabert, V.A., Narasimhan & R., Wagner, S.M. ( 2012). Research opportunities in purchasing and supply management. International Journal of Production Research, 50, 4556–4579.
37
Shahidehpour, M & Li, T. (2007). Stochastic Security-Constrained Unit Commitment. Ieee Transactions On Power Systems, 22, 800-811.
38
Sonmez, M. (2006). A review and critique of supplier selection process and practices. Working Paper. European Business School Research, Loughborough University.
39
Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103, 451-488.
40
Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52 (5), 639–657.
41
Torabi, S.A., Baghersad, M. & Mansouri, S.A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E, 79, 22–48.
42
Trevelen, M. & Schweikhart, S.B. (1988). A risk/benefit analysis of sourcing strategies: single vs multiple sourcing. Journal of Operations Management, 7 (4), 93–114.
43
Trikman, P. & McCormack, K. (2009). Supply chain risk in turbulent environments – A conceptual model for managing supply chain network risk. International Journal of Production Economics, 119, 247–258.
44
Weber, C.A., Current J.R. & Benton, W.C. (1991). Vendor Selection Criteria and Methods. European Journal of Operational Research, 50(1), 2-18.
45
Yu, H., Zeng, A.Z. & Zhao, L. (2009). Single or dual sourcing: decision-making in the presence of supply chain disruption risks. Omega, 37 (4), 788–800.
46