طراحی شبکه زنجیره‌تأمین چندسطحی برمبنای اهداف چندگانه قابلیت اطمینان، هزینه و زمان تحویل با استفاده از روش حل فراابتکاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه مهندسی صنایع، دانشگاه پیام نور، تهران، ایران

2 دانشجوی دکترای گروه مهندسی صنایع، دانشگاه پیام نور، تهران، ایران

چکیده

قابلیت اطمینان شاخص مهمی است که در هنگام اخذ تصمیمات استراتژیک طراحی زنجیره‌تأمین می‌بایست آن را مدنظر قرار داد تا در هنگام ایجاد اختلال و عدم کارکرد یا خرابی هریک از اعضا، سیستم با کمترین زیان به‌کارکرد خود ادامه دهد. در این مقاله تلاش می‌شود تا با افزایش قابلیت اطمینان، هزینه‌های بلندمدت زنجیره‌تأمین کاهش و سطح سرویس‌دهی بالا رود. از نوآوری‌های این مقاله، ارائه روشی جدید برای محاسبه شاخص قابلیت اطمینان زنجیره‌تأمین باتوجه به تعریف و ماهیت قابلیت اطمینان می‌باشد. محاسبه قابلیت اطمینان هریک از سطوح با روش محاسباتی سیستم‌های موازی، باتوجه به اثبات در متن مقاله، صحیح نمی‌باشد و می‌بایست قابلیت اطمینان هریک از اعضا باتوجه به میزان محصول یا مواد اولیه‌ای که هر عضو از آن سطح تأمین، تولید و یا نگهداری می‌کند، متفاوت باشد که این موارد در تحقیقات گذشته درنظر گرفته نشده است. ارزش، شاخص نهایی موفقیت یک سیستم می‌باشد که در این مقاله ارزش زنجیره‌تأمین با استفاده از سه شاخص مدل SCOR شامل هزینه، پاسخ‌گویی (زمان تحویل) و قابلیت اطمینان تعریف شده است. مدل پیشنهاد شده از دسته مسائل برنامه‌ریزی غیرخطی عدد صحیح (MINLP) می‌باشد که با هدف ماکزیمم کردن ارزش زنجیره، طراحی زنجیره‌تأمین انجام می‌شود. به‌دلیل پیچیدگی مسأله در ابعاد بزرگ و اثبات NP-Hard بودن آن، به‌منظور حل از الگوریتم فراابتکاری توسعه داده شده ژنتیک استفاده شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Multi-level supply chain network Design based on the multiple objectives of reliability, cost and delivery time using a meta-heuristic solution method

نویسندگان [English]

  • Reza Esfandiyari 1
  • Ramin Sadeghian 2
1 Ph.D. student, Department of Industrial Engineering, Payam Noor University, Tehran, Iran
2 Associate Professor, Department of Industrial Engineering, Payam Noor University, Tehran, Iran
چکیده [English]

Reliability is an important indicator that should be taken into account when making strategic decisions in the design of the supply chain so that the system continues to function with the least loss when a member malfunctions or fails. In this article, an attempt is made to reduce the long-term costs of the chain and increase the service level by increasing the reliability. One of the innovations of this article is to present a new method for calculating the supply chain reliability index according to the definition and nature of reliability. Calculating the reliability of each of the levels with the calculation method of parallel systems is not correct, according to the proof in the text of the article, and the reliability of each member should be based on the amount of product or raw materials that each member supplies from that level. Produce or maintain, be different that these cases were not considered in the previous research. Value is the final indicator of the success of a system. In this article, the value of the supply chain is defined using three indicators of the SCOR model, including cost, responsiveness (delivery time) and reliability. The proposed model belongs to the category of non-linear integer programming problems (MINLP), which is designed with the aim of maximizing the value of the supply chain. Due to the complexity of the problem in large dimensions and proving that it is NP-hard, the meta-heuristic algorithm developed by genetics has been used to solve it.

کلیدواژه‌ها [English]

  • Supply Chain Network Design
  • Reliability
  • Value
  • Delivery Time
  • Genetic Algorithm
  • SCOR Model
  • Eleonora Bottania, Teresa Murinob, Massimo Schiavob, Renzo Akkerman (2019). Resilient food supply chain design: Modelling framework and metaheuristic solution approach, Computers & Industrial Engineering 135, 177-198.
  • نجمه بهرامپور، رضا توکلی مقدم، ناصر شهسواری پور (1395) بهینه‌سازی دوهدفه برای مساله مکانیابی-مسیریابی با درنظر گرفتن قابلیت اطمینان و هزینه‌های فازی. نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید. سال چهارم، شماره4، 133-145.
  • Snyder, L. V. (2003). Supply chain robustness and reliability: Models and algorithms. Ph.D. dissertation, Northwestern University, Department of Industrial Engineering & Management Sciences.
  • Snyder, L. V., Daskin, M. S. (2005). Reliability models for facility location: the expected failure cost case. Transportation Science, 39(3): 400-416.
  • Madjid Tavana, Hadi Kian, Arash Khalili Nasr, Arash Khalili Nasr, Hassan Mina (2022). A Comprehensive Framework for Sustainable Closed-Loop Supply Chain Network Design. Clean Products and Processes, 12, 13.
  • ReVelle, C., Hogan. K. (1989). The maximum availability location problem. Transportation Science, 23(3): 192-200
  • Diabat, A., Jabbarzadeh, A., & Khosrojerdi, A. (2019). A perishable product supply chain network design problem with reliability and disruption considerations. International Journal of Production Economics, 212, 125–138.
  • Ali Tolooie, Meghna Maity, Ashesh Kumar Sinha, A. (2020). A two-stage stochastic mixed integer program for reliable supply chain network design under uncertain disruptions and demand. Computers & Industrial Engineering, 148.
  • Anastasia Chatzikontidoua Pantelis Longinidisa Panagiotis Tsiakisb Michael C. Georgiadis (2017). Flexible supply chain network design under uncertainty, Chemical Engineering Research and Design 128, 290–305.
  • Helander, M.E., Melachrinoudis, E. (1997). Facility location and reliable route planning in hazardous material transportation. Transportation science, 31(3): 216-226.
  • ابوالفضل کاظمی، امیر حسین نوبیل، علیرضا علی نژاد (1395)، اراته یک مدل دوهدفه برای تصمیم‌های مکان‌یابی و تخصیص دریک زنجیره‌تأمین سه‌سطحی. مدیریت تولید و عملیات. دوره هفتم، شماره2، 153-172.
  • Sansó, B., Soumis, F. (1991). Communication and transportation network reliability using routing models. Reliability, IEEE Transactions on, 40(1): 29-38.
  • محمد امیرخان، احمد نورنگ، رضا توکلی مقدم (1394) ، یک رویکرد برنامه‌ریزی تعاملی فازی برای طراحی شبکه زنجیره‌تأمین چندسطحی، چندکالایی و چنددوره‌ای تحت شرایط عدم قطعیت با درنظر گرفتن هزینه و زمان. مدیریت تولید و عملیات. دوره ششم، شماره 1. 127-148.
  • Li, J.-Q., Mirchandani, P.B., Borenstein, D. (2009). Real-time vehicle rerouting problems with time European Journal of Operational Research, 194(3): 711-727.
  • Hatefi, S. M., Jolai, F., Torabi, S. A., & TavakkoliMoghaddam, R. (2015). A credibility constrained programming for reliable forward&; reverse logistics network design under uncertainty and facility Int. J. Comput. Integr.
  • Chen, G., Sun, X., Hu, H., & Hu, Y. (2015). Research on Modeling and Algorithm of Supply Chain's Reliability Based on CCFSM. Journal of Coastal Research, 73(sp1), 99-103.
  • L& Gao. J. (2013). MAS-based reliability evaluation model in general equipment supply chain. International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 1387–1392.
  • J & Ren. F (2013). Reliability Evaluation of Processed Oil Supply Chain under Emergency. Applied Mechanics and Materials Vol. 339 (2013) pp 793-799
  • Manuel Taifouris a, Mariano Martín, Alberto Martínez, Nats Esquejo, “Simultaneous optimization of the design of the product, process, and supply chain for formulated product,” Computers and Chemical Engineering. Vol. 152, may 2021.
  • Jabbarzadeh, A., Jalali Naini, A., Davoudpour, H., Azad, N., Designing a supply chain network under the risk of disruptions. Mathematical Problems in Engineering, 2012.
  • Azad, N., Davoudpour, H., Malekly, H., Yektamaram, A., Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Annals of Operations Research. 210(1): pp. 125-163.
  • Seyyed Hossein Alavi, ArminJ abbarzadeh. (2018). Supply chain network design using trade credit and bank credit: A robust optimization model with real world application. Computers & Industrial Engineering, 125, 169–86.
  • Faezeh Gholamia, Mohammad Mahdi Paydarb, Mostafa Hajiaghaei-Keshteli an and Armin Cheraghalipour, (2019). A multi-objective robust supply chain design considering reliability. Journal of Industrial and Production Engineering 34, 3506-3590.
  • Fatemeh Delfania, Hamed Samanipourb, Hossein Beikib, Alexei Valerievich Yumashevc and Elvir Munirovich Akhmetshin, (2020). A robust fuzzy optimisation for a multi-objective pharmaceutical supply chain network design problem considering reliability and delivery time” International Journal of Systems Science: Operations & Logistics. 32.
  • Manuel Taifouris a, Mariano Martín, Alberto Martínez, Nats Esquejo, (2021) “Simultaneous optimization of the design of the product, process, and supply chain for formulated product,” Computers and Chemical Engineering. Vol. 152.
  • Dennis Kallina, Prof. Dr. Dr. Patrick Siegfried, (2021) “Optimization of Supply Chain Network using Genetic Algorithms based on Bill of materials,” The International Journal of Engineering and Science (JIES). Volume 10, Issue 7, Series I, Page, PP 37-47.
  • Jesus OchoaRobles, CatherineAzzaro-Pantel, AlbertoAguilar-Lasserre, (2020) “Optimization of a hydrogen supply chain network design under demand uncertainty by multi-objective genetic algorithms,” Computers & Chemical Engineering “. Volume 140, 2 September 2020, 106853.
  • MahdiFathi, MarziehKhakifirooz, AliDiabat, HuangenChen, (2021) “An integrated queuing-stochastic optimization hybrid Genetic Algorithm for a location-inventory supply chain network” International Journal of Production Economics Volume 237, July 2021, 108139.
  • MinHuang, PengxingYi, LjiunGuo, TielinShi, (2016) “A Modal Interval Based Genetic Algorithm for Closed-loop Supply Chain Network Design under Uncertainty” IFAC-PapersOnLin Volume 49, Issue 12, 2016, Pages 616-621.
  • Zahra Firoozim, Napsiah Ismail, Shahram Ariafar (2013) “A Genetic Algorithm for Solving Supply Chain Network Design Model,” AIP Conference Proceedings 1557, 211 (2013).
  • Fulya Altiparmak, Mitsuo Genb, Lin Lin, Ismail Karaoglan, (2009) “A steady-state genetic algorithm for multi-product supply chain network design,” Computers & Industrial Engineering. Volume 56, Issue 2, March 2009, Pages 521-537.
  • حسین شورورزی، محمد سعدی مسگری، عباس علیمحمدی، حسین آقا محمدی. "مقایسه الگوریتم‌های فراابتکاری در حل مسأله مکان‎یابی". نشریه مدرس علوم انسانی (برنامه‌ریزی و آمایش فضا)،1391 شماره3.
  • Ozkan, S. Kilic, (2019) “A Monte Carlo Simulation for Reliability Estimation of logistics and Supply Chain Network” IFAC PapersOnLine 52-13. 2080–2085.
  • Valery Lukinskiy, Vladislav Lukinskiy, Rostislav Churilov (2014) “Problems of The Supply Chain Reliability Evaluation”. Transport and Telecommunication, 2014, volume 15, no. 2, 120–129.
  • Thomas, M.U. (2002), “Supply chain reliability for contingency operations”, Annual Reliability and Maintainability Symposium, pp. 61-67, IEEE, Seattle, WA.
  • Liu, Y.-H. and Luo, M. (2007), “Reliability evaluation index system on member enterprise of supply chain”, Commercial Research, Vol. 360, pp. 120-123.
  • Mu, D. (2010), Research of Complexity and Evaluation Methods of Supply Chain System, Tsinghua University Press, Beijing.
  • Zhao, H. and Yang, J. (2007), “Supply chain reliability management research”, Modern Management Science, Vol. 4, pp. 55-57.
  • Zhang, Y.-Y. (2012), “Reliability evaluation of fresh agriculture products supply chain based on the GO methodology”, Logistics Engineering and Management, Vol. 34, pp. 65-6
  • Ha, C., Jun, H.-B. and Ok, C. (2018), “A mathematical definition and basic structures for supply chain reliability: a procurement capability perspective”, Computers and Industrial Engineering, Vol. 120, pp. 334-345.
  • Ghaffari-Nasab, N., Ahari, S.G., Ghazanfari, M. (2013). A hybrid simulated annealing based heuristic for solving the location-routing problem with fuzzy Scientia Iranica, 20(3): 919-930.
  • شکراله ،ح، رستمی مهر، م. (1386). "مدیریت ریسک زنجیره‌های تأمین بر پایه قابلیت اطمینان"، نخستین کنگره بین‌المللی مدیریت ریسک.
  • Miller, W., Leung, L., Azhar, T., Sargent, S. (1997). “Fuzzy production planning model for fresh tomato packing”, International Journal of Production Economics, 53: 227-238.
  • Caixeta-Filho, J.V. (2006). “Orange harvesting scheduling management: a case study”, Journal of the Operational Research Society, 57: 637-642.
  • Apaiah, R. K., Hendrix, E. M. (2005). “Design of a supply chain network for pea-based novel protein foods”, Journal of Food Engineering. 55: 199-220.
  • Ferrer, J.C., Mac Cawley, A., Maturana, S., Toloza, S., Vera, J. (2008). “An optimization approach for scheduling wine grape harvest operations”, International Journal of Production Economics, 112: 985-999.
  • Arnaout, J.P.M., Maatouk, M. (2010). “Optimization of quality and operational costs through improved scheduling of harvest operations”, International Transactions in operational research, 17: 595-605.
  • Ahumada, O., Villalobos, J. R. (2011). “Operational model for planning the harvest and distribution of perishable agricultural products”, International Journal of Production Economics, 133: 677-687.
  • Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., Baboli, A. (2012). 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, 48, 1152-1168.
  • Tan, B., Çömden, N. (2012). “Agricultural planning of annual plants under demand, maturation, harvest, and yield risk”, European Journal of Operational Research, 220: 539-549.
  • [Rocco, C. D., Morabito, R. (2016). “Production and logistics planning in the tomato processing industry: A conceptual scheme and mathematical model”, Computers and Electronics in Agriculture, 127: 763-774.
  • Teimoury, E., Nedaei, H., Ansari, S., Sabbaghi, M. (2013). “A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: A system dynamics approach”, Computers and Electronics in Agriculture, 93: 37-45.
  • [Agustina, D., Lee, C., Piplani, R. (2014). “Vehicle scheduling and routing at a cross docking center for food supply chains”, International Journal of Production Economics, 152: 29-41.
  • Pasandideh, H.R, Akhavan Niaki, S.T., Asadi, K. (2015). Optimizing a bi-objective multiproduct multi-period three echelon supply chain network with warehouse reliability. Expert Systems with Applications, 42, 2615-2623.
  • González-Araya, M. C., Soto-Silva, W. E., Espejo, L.G.A. (2015). “Harvest planning in apple orchards using an optimization model”, In Handbook of operations research in agriculture and the agri-food industry. 133:79-105.
  • Soto-Silva, W. E., González-Araya, M. C., OlivaFernández, M. A., Plà-Aragonés, L. M. (2017). “Optimizing fresh food logistics for processing: Application for a large Chilean apple supply chain”, Computers and Electronics in Agriculture. 53: 227-238.
  • Li, G., Zhang, L., Guan, X., Zheng, J. (2016). Impact of decision sequence on reliability enhancement with supply disruption risks. Transportation Research Part E: Logistics andTransportation Review, 90, 25-38.
  • Ghezavati, V., Hooshyar, S., Tavakkoli-Moghaddam, R. (2017). “A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato”, Central European Journal of
  • Ma, X., Wang, S., Islam, S.M., Liu, X. (2019). “Coordinating a three-echelon fresh agricultural products supply chain considering freshness-keeping effort with asymmetric information”, Applied Mathematical Modelling, 67: 337-356.
  • Cheraghalipour, A., Paydar, M. M., Hajiaghaei-Keshteli, M. (2018). “A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms”, Applied Soft Computing, 33: 59-63.
  • Roghanian, E., Cheraghalipour, A. (2019). “Addressing a set of meta-heuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions”, Journal of Cleaner Production, 239: 81-118.
  • Faezeh Gholami, Mohammad Mahdi Paydar, Mostafa Hajiaghaei-Keshteli & Armin Cheraghalipour, A multi-objective robust supply chain design considering, reliability, Journal of Industrial and Production Engineering, 276: 123-305.
  • Cheraghalipour, A., Paydar, M.M., Hajiaghaei-Keshteli, M. (2019). “Designing and solving a bi-level model for rice supply chain using the evolutionary algorithms”, Computers and Electronics in Agriculture, 162: 651-668.
  • Roghanian, E., Cheraghalipour, A. (2019). “Addressing a set of meta-heuristics to solve a multi-objective model for closed-loop citrus supply chain considering CO2 emissions”, Journal of Cleaner Production, 239: 81-118.
  • Jifroudi, S., Teimoury, E., Barzinpour, F. (2020). “Designing and planning a rice supply chain: a case study for Iran farmlands”, Decision Science Letters, 9: 163-180
  • Yan, B., Chen, X., Cai, C., Guan, S. (2020). “Supply chain coordination of fresh agricultural products based on consumer behavior”, Computers & Operations Research, 123: 105-138.
  • Chavez, M. M. M., Sarache, W., Costa, Y., Soto, J. (2020). Multiobjective stochastic scheduling of upstream operations in a sustainable sugarcane supply chain”,
  • Journal of Cleaner Production, 276: 123-305."
  • R. Vishnu, Sangeeth P. Das, R. Sridharan, P. N. Ram Kumar & N. S. Narahari, Development of a reliable and flexible supply chain network design model, a genetic algorithm based approach, International Journal of Production Research, 2021, Volume 59, Issue 20.
  • [Mahmood Nosrati and Alireza Arshadi Khamseh, Reliability optimization in a four-echelon green closed-loop supply chain network considering stochastic demand and carbon price, Uncertain Supply Chain Management 8 (2020) 457–472.
  • Salehi-Amiri, A., Zahedi, A., Gholian-Jouybari, F., Calvo, E. Z. R., & Hajiaghaei-Keshteli, M. (2022). Designing a closed-loop supply chain network considering social factors; a case study on avocado industry. Applied Mathematical Modelling, 101: 600-631.
  • رضا توکلی مقدم، جاوید قهرمانی نهر، علی قدرت نما، حمید رضا ایزد بخش(1397). "طراحی یک شبکه زنجیره‌تأمین سبز چندهدفه چندمحصولی و چنددوره‌ای با درنظر گرفتن تخفیف در شرایط عدم قطعیت"، نشـریه پـژوهـش‌های مهنـدسـی صنــایع در سیستـم‌هـای تولیــد. سال ششم، شماره سیزدهم، پاییز و زمستان 1397 ،صفحـــه 119-137.