ارائه یک مدل چندهدفه فازی جهت طراحی شبکه چندسطـحی تأمین با درنظرگرفتن وقوع اختلال در سطــوح مختلف و حــل آن با رویکرد ε-Constraint

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشگاه آزاد اسلامی، واحد بین‌المللی خلیج فارس، خرمشهر.

2 استادیار، دانشگاه ایلام.

چکیده

امروزه به دلیل اثرات بسیار زیاد طراحی شبکه­ ی تأمین در منافع اقتصادی سازمان ‏ها، کیفیت ارائه خدمات مناسب و رضایت مشتریان، مسئله طراحی شبکه یکی از مسائل مورد­علاقه و جذاب پژوهشگران حوزه تحقیق در عملیات و علوم مدیریت محسوب می ­شود که پیشرفت‏ های زیادی نیز در این زمینه حاصل شده است. در این پژوهش، یک شبکه تأمین تولیدی مطابق دنیای واقعی طراحی شده است. ساختار این زنجیره تأمین بدین صورت است که محصول بعد از تولید در کارخانه­ های تولیدی در انبارهای مربوطه نگهداری شده و سپس به خرده‌فروش‌ها منتقل می‌شود. در­نهایت مشتریان با مراجعه به خرده‌فروشی‌ها نیاز خود را برطرف می­ کنند. برای نزدیک­ شدن به واقعیت، عدم ­قطعیت فازی در پارامترهای مدل در نظر گرفته شده و اختلالاتی از قبیل آتش‌سوزی یا قطع برق و غیره در کارخانه ­ها و انبارها به ­صورت همزمان لحاظ شده است؛ همچنین مسئله دارای افق زمانی چند­دوره‌ای و دو هدف حداقل­ کردن هزینه و حداکثر­کردن سطح پوشش مناطق مشتریان است که برای حل آن از حل­کننده CPLEX نرم‌افزار GAMS و رویکرد ε-Constraint استفاده شده است.

کلیدواژه‌ها


عنوان مقاله [English]

in Different Levels and Solving by ε-Constraint Approach

نویسندگان [English]

  • Fatemeh Fazeli 1
  • Masoud Seidi 2
1 Msc. Student, Islamic Azad University, Branch of Khalij Fars, Khoramshahr.
2 Assistant Professor, Ilam University.
چکیده [English]

Nowadays, due to the huge effects of designing the supply network on the economic interests of organizations, the quality of providing appropriate services and customer satisfaction, the problem of network design is one of the most interesting and attractive issues of research in operations and management science. In this paper, a real-world manufacturing network is designed. The structure of this supply chain is such that the product is stored in the production facilities after production in the manufacturing plants and then transported to the retailers. Ultimately, customers will be able to address their needs by visiting retailers. In order to be real, fuzzy uncertainty is considered in the model parameters, and disruptions such as fire or power failure, etc. in factories and warehouses are considered simultaneously. Also, the problem has a multi-period time horizon and two goals are to minimize cost and maximize the coverage level of customers. To solve it, using CPLEX (GAMS) by ε-Constraint approach.

کلیدواژه‌ها [English]

  • Network Design
  • Multi: Objective
  • Fuzzy
  • Disruption
  • ε-Constraint
1. Adeli, M., & Zandieh, M. (2013). Presenting a simulation of optimization multiobjective approach for sourcing model and decision of integrating inventories. Journal of Industrial Management Perspective, 11, 89-110 (In Persian).
2. Aghezzaf, E.H. , Sitompul, C. , & Najid, N. M. (2010). Models for robust tactical planning in multi-stage production systems with uncertain demands. Computers & Operations Research, 37, 880-889.
3. Alizadeh, M. , Mahdavi, I. , Mahdavi-Amiri, N. , & Shiripour, S. (2015). A capacitated location-allocation problem with stochastic demands using sub-sources: An empirical study. Applied Soft Computing, 34, 551-571.
4. Ara,  A. L. , Kazemi, A. ,  Gahramani, S. , &  Behshad, M. (2012). Optimal reactive power flow using multi-objective mathematical programming. Scientia Iranica, 19, 1829-1836.
5. Avriel, M. (1980). A geometric programming approach to the solution of location problems. Journal of Regional Science, 20, 239-246.
6. Berman, O.,  Krass, D., & Menezes, V. (2007). Facility reliability issues in network p-median problems: strategic centralization and co-location effects. Operations Research, 55, 332-350.
7. Canel, C., & Das, S. R. (2002). Modeling global facility location decisions: integrating marketing and manufacturing decisions. Industrial Management & Data Systems, 102, 110-118.
8. Chopra, S. & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. Das summa summarum des management, 265-275.
9. Cooper, L. (1963). Location-allocation problems. Operations research, 11, 331-343.
10. Daskin, M. S., Snyder, L. V., & Berger, R. T. (2005). Facility location in supply chain design. Logistics systems: Design and optimization, 39-65.
11. Erengüç, Ş. S., Simpson, N. C. & Vakharia, A. J. (1999). Integrated production/distribution planning in supply chains: An invited review. European journal of operational research, 115, 219-236.
12.  Forghani,  A. & Pourebrahim, A. (2008).Industrial center location problemes. Tadbir, 49-52 (In Persian).
13. Hajipour, V.,  Rahmati, S. H. A. , Pasandideh, S. H. R. , & Niaki, S. T. A. (2014). A multi-objective harmony search algorithm to optimize multi-server location–allocation problem in congested systems. Computers & Industrial Engineering, 72, 87-197.
14.  Hosseininezhad, S. J., Jabalameli, M. S., & Naini, S. G. J. (2014). A fuzzy algorithm for continuous capacitated location allocation model with risk consideration. Applied Mathematical Modelling, 38, 983-1000.
15. Jabalameli, M.S. & Ghaderi, A. (2005) Solving large-scale location-allocation problems using a mixed-neighborhood search algorithm. Second International Logistics Conference, Iran(Tehran)(In Persian).
16. Jiménez, M. , Arenas, M. , Bilbao, A. , &  Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 1599-1609
17. Love, R. F., & Morris, J. G. (1988). Facilities Layout and Location:Models & Methods vol. 3. North-Holland, New York.
18. Marín, A. (2011). The discrete facility location problem with balanced allocation of customers. European Journal of Operational Research, 210, 27-38.
19. Mestre, A. M., Oliveira, M. D., & Barbosa-Póvoa, A. P. (2015). Location–allocation approaches for hospital network planning under uncertainty. European Journal of Operational Research, 240, 791-806.
20. Mousavi, V., & Niaki, S. T. A.(2013). Capacitated location allocation problem with stochastic location and fuzzy demand: a hybrid algorithm. Applied Mathematical Modelling, 37, 5109-5119.
21. Park, G. , Lee, Y. , & Han, J. (2014). A two-level location–allocation problem in designing local access fiber optic networks. Computers & Operations Research, 51, 52-63.
22. Qi, L., Shen, Z.J. M., & Snyder, L. V. (2010). The effect of supply disruptions on supply chain design decisions. Transportation Science, 44, 274-289.
23. Rabieh, M., Azar, A., Modarres Yazdi, M., Fetanatfard Haghighi, M. (2011). Designing a robust multi–objective mathematical model of sourcing: an approach to reducing supply chain risks (Case study: Iran khodro supply chain). Journal of Industrial Management Perspective, 1, 57-77 (In Persian).
24. Rahmati, S. H. A., Ahmadi, A., Sharifi, M. , & Chambari, A.  (2014). A multi-objective model for facility location–allocation problem with immobile servers within queuing framework. Computers & Industrial Engineering, 74, 1-10.
25. Simchi-Levi, D.,  Simchi-Levi, E., & Kaminsky, P. (1999) Designing and managing the supply chain: Concepts, strategies, and cases.McGraw-Hill New York.
26. Syam, S. S., & Côté, M. J. (2012). A comprehensive location-allocation method for specialized healthcare services. Operations Research for Health Care, 1, 73-83.
27. Talebi, D., Ayron, F. (2015). Identifying supply chain risks and selecting suppliers using the network analysis process (The case study: Automobile industry). Journal of Industrial Management Perspective, 17, 31-43 (In Persian).
28. Vidyarthi, N., & Jayaswal, S. (2014). Efficient solution of a class of location–allocation problems with stochastic demand and congestion. Computers & Operations Research, 48, 20-30.
29. Yan, S. , Lin, J. R., Chen, Y.C., & Xie, F.R. (2017). Rental bike location and allocation under stochastic demands. Computers & Industrial Engineering, 107, 1-11.
30. Zahiri, B., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2014). A robust possibilistic programming approach to multi-period location–allocation of organ transplant centers under uncertainty. Computers & Industrial Engineering, 74, 139-148.
31. Zeinal Hamadani, A., Ardakan, M. A. , Rezvan, V., & Honarmandian, V. (2013). Location-allocation problem for intra-transportation system in a big company by using meta-heuristic algorithm. Socio-Economic Planning Sciences, 47, 309-317.