طراحی مدل آرمانی- فازی برای بهینه‌سازی هزینه‌ و مسافت وسایل نقلیه در زنجیره تامین چهار سطحی حلقه بسته با استفاده از الگوریتم مورچگان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران.

2 استادیار، گروه مدیریت صنعتی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران

3 دانشیار، گروه مدیریت صنعتی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران

چکیده

در دنیای پر رقابت کنونی، زنجیره تامین حلقه بسته در راستای بازیافت محصولات به یک چالش اساسی تبدیل شده است. سیستم بازیافت به دلیل پوشش قوانین و کاهش آلودگی‌ زیست‌محیطی، افزایش توان اقتصادی با ایجاد مشاغل جدید و توانایی بازیابی ارزش محصولات برگشتی، از جایگاه ویژه‌ای در زنجیره تامین برخوردار است. هدف این پژوهش، طراحی مدل آرمانی- فازی برای بهینه‌سازی هزینه‌ و مسافت وسایل نقلیه در زنجیره تامین چهار سطحی حلقه بسته است. در این پژوهش، نخست عوامل موثر در لجستیک معکوس شناسایی و برای طراحی مدل پژوهش چندهدفه به دلیل وجود اهداف مختلف، مدل ریاضی آرمانی- فازی با هدف کمینه‌‌سازی هزینه و مسافت ارائه گردید. مدل لجستیک حلقه بسته به دلیل قرارگیری در گروه مسائل NP-Hard، با روش‌های گرادیان مبنا به‌راحتی قابل‌ حل نبوده، از این‌رو جهت بهینه‌سازی از الگوریتم مورچگان استفاده ‌شده است. درنهایت پس از اجرای مدل پژوهش، هدف به حداقل رساندن هزینه، مقدار عضویت بالاتری نسبت به هدف مسافت داشته و مدیران نیز باید توجه ویژه‌ای به این هدف داشته باشند.

کلیدواژه‌ها


عنوان مقاله [English]

Design a Fuzzy Goal Programming Model for Optimizing the Cost and Distance of Vehicles in the Four-Echelon Closed-Loop Supply Chain by Using Ant Colony Algorithm

نویسندگان [English]

  • Sajjad Jalalifar 1
  • Reza Ehtesham Rasi 2
  • Ali Mohtashami 3
1 ph.D stdent in industrial managemnt
2 Assistant Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 Associate Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
چکیده [English]

In today's highly competitive world, closed-loop supply chains (CLSC) have become a major challenge for product recycling. The recycling system has a special place in the supply chain due to the coverage of laws and the reduction of environmental pollution, increasing economic power by creating new jobs and the ability to recover the value of returned products. The purpose of this study is to design fuzzy- goal model for optimizing the cost and distance of vehicles in the four echelon closed-loop supply chain. The closed-loop logistics model could not be easily solved with the base gradient methods due to its NP-Hard problem group, so the ant algorithm was used for optimization. Finally, after implementing the fuzzy goal model of the research, it was found that the goal of minimizing the cost in the model has a higher membership amount than the distance goal, and managers should pay special attention to this goal.

کلیدواژه‌ها [English]

  • closed-loop supply chain
  • Fuzzy Goal Programming
  • Optimization
  • ant colony algorithm
[1]    Lambert, D. M. Stock, J.R. & Sterling, J.U. (1990). A Gap Analysis of Buyer and Seller Perceptions of the Importance of Marketing Mix Attributes, Educator Conference Proceeding Washington, DC.
[2]    Christopher, M. (2016). Logistics Supply Chain Management. Pearson, UK.
[3] De Boer, L. Labro, E. & Morlacchi, P. (2001). A review of methods supporting supplier selection, European J. Purchasing & Supply management,7(2),pp.75-89.
[4]   Wei, Yin. (2011).Reverse supply chain management. University of Gothenburg.
[5]   Rasi, E.Reza. (2018).A Cuckoo Search Algorithm Approach for Multi- Objective Optimization in Reverse Logistics Network under Uncertainty Condition. International Journal of Supply and Operations Management, 5(1), pp.66-80.
[6]   Mohtashami, Zahra. Aghsami,Amir. Jolai,Fariborz. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of Cleaner Production.242(1),pp. 118-152.
[7]    Aziziankohan, A., Jolai, F., Khalilzadeh, M., Soltani, R., and Tavakkoli-Moghaddam, R. (2017). Green supply chain management using the queuing theory to handle congestion and reduce energy consumption and emissions from supply chain transportation fleet. J. Ind. Eng. Manag., 10,pp. 213-231.
[8]    McKinnon, A. Cullinene, S. Browne, M. Whiteing, A. (2010). Green Logistics, Improving the environmental sustainability of logistics, Kogan page, London Philadelphia New Delhi.
[9]    Mutha, A. & Pokharel, S. (2009). Strategic network design for reverse logistics and remanufacturing using new and old product modules, Computers & Industrial Engineering, Vol. 56, pp. 334-346.
[10] Cruz-Rivera, R. & Ertel, J. (2009). Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico, European Journal of Operational Research, Vol. 196(6), pp. 930-939.
[11] Subramanian, N., Gunasekaran, A. (2015). Cleaner supply-chain management practices for twenty-firstcentury organizational competitiveness: Practice-performance framework and research propositions.International Journal of Production Economics, 164(5),pp. 216-233.
[12] Rasi,E.Ehtesham. Karami Pour, Meysam. Noor Mohammadi,Rahman. (2018). Mathematical modelling of the vehicle hybrid locating-routing problem in three-tier supply chain. Int. J. Modelling in Operations Management,7(1),pp.42-58.
[13] Harrison, A. & Van Hoek, R. (2008).Logistics Management and Strategy competing through the supply chain, Harlow: Pearson Education Limited.
[14] Chiara, Gobbi. (2011). Designing the reverse supply chain: the impact of the product residual value, pp.768-796.
[15] Jayaraman, V. & Pirkul, H. (2001). Planning and coordination of production and distribution facilities for multiple commodities. European Journal of Operational Research, 133, pp.394–408.
[16] Melachrinoudis, E. Messac, A. & Min, H. (2005). Consolidating a warehouse network: a physical Programming approach. International Journal of Production Economics, Vol. 97, pp.1-17.
[17] Listes, O. & Dekker R. (2005). A stochastic approach to a case study for product recovery network design. European Journal of Operational Research, Vol. 160, pp. 268–287.
[18] Amiri, A. (2006). Designing a distribution network in a supply chain system: Formulation and efficient solution procedure. European Journal of Operational Research, Vol. 171, pp. 567–576.
[19] Du, F. & Evans G. W. (2008). A bi-objective reverse logistics network analysis for post-sale service. Computers & Operations Research, 35, pp. 2617 – 2634.
[20] Dat, L.Q. Linh, D.T.T. Chou, Sh.Y & Yu, V.F. (2012). Optimizing reverse logistic costs for recycling end-of-life electrical and electronic products. Expert Systems with Applications, 39,pp.6380–6387.
[21] Ramezani, M. Bashiri, M. Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37,pp.328-344.
[22] Rahimi, M. Baboli, A. Rekik, Y. (2016). Sustainable Inventory Routing Problem for Perishable Products by Considering Reverse Logistic. IFAC-PapersOnLine, 5(3), pp. 949-954.
[23] Dondo, R.G. & Mendez, C.A. (2016). Operational planning of forward and reverse logistic activities on multi-echelon supply chain networks. Computers & Chemical Engineering, 88,pp. 170–184.
[24] Ehtesham Rasi, R. and Sohanian, M. (2020).A multi-objective optimization model for sustainable supply chain network with using genetic algorithm. Management, article in press.
[25] Zhou, Y. Wang, Sh. (2008). Generic Model of Reverse Logistics Network Design. Journal of Transportation Systems. Engineering & Information Technology, 8(3),pp.71-78.
[26]Ahmadiazar,Mahmoud.Dorri,Behroz.Tabriz,Akbar,Alam.Kassai,Massoud. (2019).Modeling and solving problem sustainable closed loop supply chain network design for petrochemical products under uncertainty conditions. Modern Researches in Decision Making (Scientific Research Quarterly), 4(4), pp.1-30.
[27] Khodadadian, Davood.Radfar,Reza.Toloei,Eshlaghi,Abbas. (2020). A multi-objective green supply chain: multi-product model considering uncertainty. Modern Researches in Decision Making (Scientific Research Quarterly), 5(3), pp.1-28.
[28] Khalili Nasr,Arash.,Tavana,Madjid,.Alavi,Behrouz.,Mina,Hassan. (2020). A Novel Fuzzy Multi-Objective Circular Supplier Selection and Order Allocation Model for Sustainable Closed-Loop Supply Chains.Journal of Cleaner production,10, article in press.
[29] Liu,Ying.,Ma, Lin.&Liu,Yankui. (2020). A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity.Applied Mathematical Modeling,92(2),pp.99-135.