.طراحی شبکه زنجیره تامین حلقه بسته تحت شرایط اختلال و عدم قطعیت با در نظرگرفتن کیفیت و استراتژی تاب آوری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مهندسی صنایع، فنی و مهندسی، دانشگاه علم و صنعت، تهران، ایران

2 مهندسی صنایع، فنی و مهندسی، دانشگاه تهران، تهران، ایران

چکیده

در سال های اخیر با توجه به افزایش نگرانی های زیست محیطی، قوانین دولتی و محدودیت منابع طبیعی و تاثیر قوانین سبز، زنجیره تامین حلقه بسته توجه فزاینده ای را به خود جلب کرده است. از آنجایی که تامین کننده نقش مهم و اساسی در زنجیره تامین دارد در صورت مواجه شدن با ریسک و اختلال اثرات زیان بار و مهمی برای زنجیره تامین به بار خواهد آورد، بنابراین مطالعه و بررسی این شرایط امری ضروری به نظر می رسد. از این رو در این مقاله به بررسی مسئله طراحی شبکه زنجیره تامین حلقه بسته در شرایط ریسک تامین پرداخته می شود. در کنار در نظر گرفتن اختلال در تأمین، عواملی از قبیل استفاده از موجودی اضافی و همچنین قرار داد با تأمین کنندگان قابل اعتماد در دوره هایی که دچار اختلال نشده ایم در این مقاله به عنوان استراتژی های انعطاف پذیری در نظر گرفته شده است. هدف از این مسئله کمینه کردن هزینه های زنجیره با توجه به تصمیمات مکان یابی، میزان جریان بین سطوح و فروش از دست رفته می باشد. اختلال در تامین کنندگان به صورت سناریوهای مختلف و به صورت جزئی در نظر گرفته شده است. مسئله با استفاده برنامه ریزی عدد صحیح آمیخته مدل شده و از رویکرد دو مرحله ای احتمالی برای در نظر گرفتن عدم قطعیت در مدل پیشنهادی استفاده شده است. در پایان تحلیل حساسیت بر روی مدل پیشنهادی صورت گرفته و پیشنهاداتی به منظور استفاده از این مدل در دنیای واقعی ارائه شده است. 

کلیدواژه‌ها


عنوان مقاله [English]

Closed loop supply chain network design under disturbance and uncertainty conditions considering quality and resilience strategy

نویسندگان [English]

  • Morteza Ghomi 1
  • Sayed Gholamreza jalalinaeeni 1
  • Reza tavakoli moghadam 2
  • Armin jabbarzadeh 1
1 Industrial Engineering, Technical and Engineering, University of Science and Technology, Tehran, Iran
2 Industrial Engineering, Technical and Engineering, University of Tehran, Tehran, Iran
چکیده [English]

In recent years, due to increasing environmental concerns, government regulations and natural resource constraints, and the impact of green laws, the closed-loop supply chain has attracted increasing attention. Since the supplier has an important role in the supply chain, if faced with risk and disruption, it will have detrimental and important effects on the supply chain, so it seems necessary to study these conditions. Therefore, in this paper, the issue of closed-loop supply chain network design in supply risk conditions is investigated. In addition to disruption of supply, factors such as the use of excess inventory as well as contracts with reliable suppliers in periods that we have not disrupted are considered flexibility strategies in this article. The goal is to minimize chain costs with respect to location decisions, flow between levels, and lost sales. Disruption in suppliers is considered in different scenarios and in detail. The problem is modeled using mixed integer programming and a possible two-step approach is used to consider the uncertainties in the proposed model. At the end, sensitivity analysis is performed on the proposed model and suggestions are provided for using this model in the real world.

کلیدواژه‌ها [English]

  • Supply chain network design
  • Closed-loop supply chain
  • Disruption
  • Resilience Strategy
[1] Saghafi, M.M., Optimal pricing to maximize profits and achieve market-share targets for single-product and multiproduct companies, in Issues in Pricing: Theory and Research. 1988, Lexington Books Los Angeles. p. 239-253. [2] Li, J., S. Wang, and T.E. Cheng, Competition and cooperation in a single-retailer two-supplier supply chain with supply disruption. International Journal of Production Economics, 2010. 124(1): p. 137-150. [3] Drezner, Z., Heuristic solution methods for two location problems with unreliable facilities. Journal of the Operational Research Society, 1987. 38(6): p. 509- 514. [4] Fleischmann, M., et al., The impact of product recovery on logistics network design. Production and operations management, 2001. 10(2): p. 156-173. [5] Salema, M.I.G., A.P. Barbosa-Povoa, and A.Q. Novais, An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. European Journal of Operational Research, 2007. 179(3): p. 1063-1077. [6] Church, R. and M.P. Scaparra, Analysis of facility systems’ reliability when subject to attack or a natural disaster, in Critical Infrastructure. 2007, Springer. p. 221-241. [7] Listeş, O. and R. Dekker, A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, 2005. 160(1): p. 268-287. [8] Lu, Z. and N. Bostel, A facility location model for logistics systems including reverse flows: The case of remanufacturing activities. Computers & Operations Research :)2(34 .2007 .p. 299-323. [9] Bollat, R.C.P., Resilient global supply chain network design optimization. 2009, The Pennsylvania State University. [10] Pishvaee, M.S., R.Z. Farahani, and W. Dullaert, A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & operations research, 2010. 37(6): p. 1100-1112. [11] Peng, P., et al., Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological, 2011. 45(8): p. 1190-1211. [12] Jabbarzadeh, A., et al., Designing a supply chain network under the risk of disruptions. Mathematical Problems in Engineering, 2012. [13] Azad, N. and H. Davoudpour, Designing a stochastic distribution network model under risk. The International Journal of Advanced Manufacturing Technology, 2013. 64(1-4): p. 23-40. [14] Pishvaee, M.S. and S.A. Torabi, A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 2010. 161 :)20(p. 2668-2683. [15] Pishvaee, M.S., M. Rabbani, and S.A. Torabi, A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 2011. 35(2): p. 637-649. [16] Chen, Q., X. Li, and Y. Ouyang, Joint inventorylocation problem under the risk of probabilistic facility disruptions. Transportation Research Part B: Methodological, 2011. 45(7): p. 991-1003. [17] O’Hanley, J.R., P. Scaparra, and S. García, A General Linearization Technique for Modeling Reliability in Facility Location: Applications to Problems with Site-Dependent Failure Probabilities. 2012. [18] Wang, H., Increasing supply chain robustness through process flexibility and strategic inventory. 2013, Massachusetts Institute of Technology. [19] Ramezani, M., M. Bashiri, and R. TavakkoliMoghaddam, A robust design for a closed-loop supply chain network under an uncertain environment. The International Journal of Advanced Manufacturing Technology, 2013. 66(5-8): p. 825-843. [20] Qiang, Q., et al., The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega, 2013. 41(2): p. 186-194. [21] Amin, S.H. and G. Zhang, A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 2013. 37(6): p. 4165-4176. [22] Lalmazloumian, M., et al., A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Annals of Operations Research, 2013: p. 1-36. [23] Ramezani, M., et al., Closed-loop supply chain network design under a fuzzy environment. KnowledgeBased Systems, 2014. 59: p. 108-120. [24] Baghalian, A., S. Rezapour, and R.Z. Farahani, Robust supply chain network design with service level against disruptions and demand uncertainties: A reallife case. European Journal of Operational Research, 2013. 227(1): p. 199-215. [25] Özceylan, E., T. Paksoy, and T. Bektaş, Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. Transportation Research Part E: Logistics and Transportation Review, 2014. 61: p. 142-164. [26] Keyvanshokooh, E., et al., A dynamic pricing approach for returned products in integrated forward/reverse logistics network design. Applied Mathematical Modelling, 2013. 37(24): p. 10182- 10202. [27] Qi, L., Z.-J.M. Shen, and L.V. Snyder, The effect of supply disruptions on supply chain design decisions. Transportation Science, 20 :)2(44 .10p. 274-289. [28] Vahdani, B., et al., Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model. Transportation Research Part E: Logistics and Transportation Review, 2012. 48(6): p. 1152-1168. [29] Yadegari, E., et al., A Flexible Integrated Forward/Reverse Logistics Model with Random Pathbased Memetic Algorithm. Iranian Journal of Management Studies, 2015. 8(2): p. 287. [30] Aryanezhad, M.-B., S.G. Jalali, and A. Jabbarzadeh, An integrated supply chain design model with random disruptions consideration. African Journal of Business Management, 2010. 4(12): p. 2393. [31] Esmaeilikia, M., et al., A tactical supply chain planning model with multiple flexibility options: an empirical evaluation. Annals of Operations Research, 2014: p. 1-26. [32] Azad, N., et al., Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Annals of Operations Research, 2013. 210(1 :) p. 125-163. [33] Demirel, N., et al., A genetic algorithm approach for optimising a closed-loop supply chain network with crisp and fuzzy objectives. International Journal of Production Research, 2014. 52(12): p. 3637-3664. [34] Hatefi, S.M., et al ..A credibility-constrained programming for reliable forward–reverse logistics network design under uncertainty and facility disruptions. International Journal of Computer Integrated Manufacturing, 2015. 28(6): p. 664-678. [35] Hasani, A. and A. Khosrojerdi, Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 2016. 87: p.52-20 . [36] Schmitt, A.J., et al., Centralization versus decentralization: Risk pooling, risk diversification, and supply chain disruptions. Omega, 2015. 52: p. 201-212. [37] Khosrojerdi, A., et al., A method for designing power supply chain networks accounting for failure scenarios and preventive maintenance. Engineering Optimization, 2016. 48(1): p. 154-172.