مکان‌یابی و مسیریابی زنجیره تأمین پایدار در فروشگاه‌های آنلاین
محورهای موضوعی :
مدیریت صنعتی
Hossein Firouzi
1
,
Javad Rezaeian
2
,
Alireza Rashidi Komijan
3
,
Mohammad Mehdi Movahedi
4
1 - Vice President of Automotive Engineering Saipa Citroen
2 - Industrial Engineering, Mazandaran University of Science and Technology
3 - Assistant Professor, Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
4 - Department of Industrial Management, Islamic Azad University, Firoozkuh Branch< Firoozkuh, Iran,
تاریخ دریافت : 1401/03/21
تاریخ پذیرش : 1402/01/19
تاریخ انتشار : 1402/03/30
کلید واژه:
زنجیره تأمین پایدار,
فروشگاه آنلاین,
مسیریابی,
اسلات زمانی,
مکان‌یابی,
چکیده مقاله :
یکی از مهم‌ترین مشکلات شرکت‌های توزیع (بالاخص فروشگاه‌های آنلاین بزرگ)، طراحی موقعیت بازاندازها، مسیریابی وسایل نقلیه و بهینه‌سازی شبکه توزیع تأمین است. در این پژوهش به مکان‌یابی مراکز توزیع فروشگاه آنلاین دیجی‌کالا پرداخته شده است. یک مسأله مکان‌یابی - مسیریابی چندهدفه با در نظر گرفتن اصل پایداری و محدودیت زمانی با روش‌های فراابتکاری توسعه داده شده است. براساس نتایج مدل پیشنهادی اثر تقاضا بر سه هدف زمان، هزینه و مسائل زیست‌محیطی و اجتماعی بررسی گردید و نشان داده شد که اگر تقاضا افزایش یابد اثر بیشتر بر زمان به‌وجود می‌آید. به‌عبارت دیگر افزایش تقاضا می‌تواند خلل‌هایی را ایجاد کند که در نتیجه آن زمان تحویل افزایش یافته و لذا این امر می‌تواند در بلندمدت منجر به نارضایتی مشتری شود. افزایش میزان اشتغال نیز منجر به بهتر شدن مسائل زیست‌محیطی می‌شود اما اثری بر هزینه و زمان ندارد. همچنین هزینه حمل کالا اثری بر مسائل زیست‌محیطی و زمان ندارد اما منجر به افزایش هزینه می‌شود.
چکیده انگلیسی:
One of the most important problems for distribution companies (especially large online stores) is the design of docking stations, vehicle routing, and supply chain optimization. In this research, the distribution centers of Digi kala online store have been located. A multi-objective location-routing problem has been developed by meta-heuristic methods considering the principle of stability and time constraints. Based on the results of the proposed model, the effect of demand on the three objectives of time, cost, and environmental and social issues was investigated and it was shown that if demand increases, there will be a greater effect on time. In other words, increasing demand can create gaps that result in increased delivery time, and therefore this can lead to customer dissatisfaction in the long run. Increasing employment also leads to better environmental issues, but has no effect on cost or time. Also, the cost of transporting goods has no effect on environmental issues and time, but leads to increased costs.
منابع و مأخذ:
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Mousavi, S. M., Vahdani, B., Tavakkoli-Moghaddam, R., & Hashemi, H. (2014). Location of cross-docking centers and vehicle routing scheduling under uncertainty: a fuzzy possibilistic–stochastic programming model. Applied Mathematical Modelling, 38(7-8), 2249-2264.
Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390.
Musa, R., Arnaout, J. P., & Jung, H. (2010). Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers & Industrial Engineering, 59(1), 85-92.
Naderi, B., Rahmani, S., & Rahmani, S. (2014). A multiobjective iterated greedy algorithm for truck scheduling in cross-dock problems. Journal of Industrial Engineering, 2014.
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Vahdani, B., Tavakkoli-Moghaddam, R., & Mousavi, S. M. (2013). Scheduling of trucks in cross-docking systems: a hybrid meta-heuristic algorithm. Lecture Notes in Management Science, 5, 125-132.
Vincent, T., Seipp, F., Ruzika, S., Przybylski, A., & Gandibleux, X. (2013). Multiple objective branch and bound for mixed 0-1 linear programming: Corrections and improvements for the biobjective case. Computers & Operations Research, 40(1), 498-509.
Wu, C. J., & Hamada, M. S. (2011). Experiments: planning, analysis, and optimization. John Wiley & Sons.
Zhang, Z. H., Li, B. F., Qian, X., & Cai, L. N. (2014). An integrated supply chain network design problem for bidirectional flows. Expert Systems with Applications, 41(9), 4298-4308.
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Ali Ahmadi, Alireza & Nahaie, Vahid Saeed. (2007). A comprehensive description of research methods (paradigms, strategies, designs and quantitative, qualitative and hybrid approaches. (2nd edition), Tehran: Tolied Danesh.
Arabani, A. B., Ghomi, S. F., & Zandieh, M. (2011). Meta-heuristics implementation for scheduling of trucks in a cross-docking system with temporary storage. Expert systems with Applications, 38(3), 1964-1979.
Aravendan, M., & Panneerselvam, R. (2014). Literature review on network design problems in closed loop and reverse supply chains. Intelligent Information Management, 2014.
Bouchery, Y., Corbett, C. J., Fransoo, J. C., & Tan, T. (Eds.). (2016). Sustainable supply chains: A research-based textbook on operations and strategy(Vol. 4). Springer.
Boysen, N. (2010). Truck scheduling at zero-inventory cross docking terminals. Computers & Operations Research, 37(1), 32-41.
Boysen, N., Fliedner, M., & Scholl, A. (2010). Scheduling inbound and outbound trucks at cross docking terminals. OR spectrum, 32(1).
Cardona-Valdés, Y., Álvarez, A., & Ozdemir, D. (2011). A bi-objective supply chain design problem with uncertainty. Transportation Research Part C: Emerging Technologies, 19(5), 821-832.
Cardona-Valdés, Y., Álvarez, A., & Pacheco, J. (2014). Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty. Transportation Research Part B: Methodological, 60, 66-84.
Czinkota, M. R., Kotabe, M., Vrontis, D., Shams, S. R. (2021). Distribution and Supply Chain Management. Marketing Management: Past, Present and Future, 499-552.
Formentini, M. (2020). Sustainable Supply Chain Management. In Corporate Sustainability in Practice: A Guide for Strategy Development and Implementation(pp. 207-223). Cham: Springer International Publishing.
Georgiadis, M. C., Tsiakis, P., Longinidis, P., & Sofioglou, M. K. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39(3), 254-272.
Hassini, E., Surti, C., & Searcy, C. (2012)."A literature review and A Case Study of Sustainable Supplu Chains whit a Focus on Metrics." Production Economics, 140, 69-82.
Javid, A. A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582-597.
Jozefowiez, N., Laporte, G., & Semet, F. (2012). A generic branch-and-cut algorithm for multiobjective optimization problems: Application to the multilabel traveling salesman problem. INFORMS Journal on Computing, 24(4), 554-564..
Kara, S. A. I. B., & Melo, I. M. T. (2013). Location and Logistics. Technical Reports on Logistics, Saarl. Bus. Sch. ISSN 2193–7761, 2014.
Küükoğlu, İ., Aksoy, A., Ene, S., & Öztürk, N. (2013). A mathematical model for two dimensional loading problem in cross-docking network design. Mathematical and Computational Applications, 18(3), 273-282.
Larbi, R., Alpan, G., Baptiste, P., & Penz, B. (2011). Scheduling cross docking operations under full, partial and no information on inbound arrivals. Computers & Operations Research, 38(6), 889-900.
Li, L., & Schulze, L. (2010, March). Uncertainty in logistics network design: a review. In World Congress on Engineering 2012. July 4-6, 2012. London, UK.(Vol. 2189, pp. 1466-1471). International Association of Engineers.
Liao, C. J., Lin, Y., & Shih, S. C. (2010). Vehicle routing with cross-docking in the supply chain. Expert systems with applications, 37(10), 6868-6873.
Linton, J. D., Klassen, R., & Jayaraman, V. (2007). Sustainable supply chains: An introduction. Journal of operations management, 25(6), 1075-1082
Ma, H., Miao, Z., Lim, A., & Rodrigues, B. (2011). Crossdocking distribution networks with setup cost and time window constraint. Omega, 39(1), 64-72.
Matinrad, N., Roghanian, E., & Rasi, Z. (2013). Supply chain network optimization: A review of classification, models, solution techniques and future research. Uncertain Supply chain management, 1(1), 1-24.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669.
Mousavi, S. M., Tavakkoli-Moghaddam, R., & Jolai, F. (2013). A possibilistic programming approach for the location problem of multiple cross-docks and vehicle routing scheduling under uncertainty. Engineering Optimization, 45(10), 1223-1249.
Mousavi, S. M., Vahdani, B., & Tavakkoli-Moghaddam, R. (2014). Optimal design of the cross-docking in distribution networks: Heuristic solution approach. International Journal of Engineering, 27(4), 533-544.
Mousavi, S. M., Vahdani, B., Tavakkoli-Moghaddam, R., & Hashemi, H. (2014). Location of cross-docking centers and vehicle routing scheduling under uncertainty: a fuzzy possibilistic–stochastic programming model. Applied Mathematical Modelling, 38(7-8), 2249-2264.
Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390.
Musa, R., Arnaout, J. P., & Jung, H. (2010). Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers & Industrial Engineering, 59(1), 85-92.
Naderi, B., Rahmani, S., & Rahmani, S. (2014). A multiobjective iterated greedy algorithm for truck scheduling in cross-dock problems. Journal of Industrial Engineering, 2014.
Parker, T. (2020). An Expensive Problem for the Online Fashion Industry: Too many returns. https://medium.com/@parker_content/an-expensive-problem-for-the-online-fashion-industry-too-many-returns-abc441ed1b51
Ramezani, M., Kimiagari, A. M., Karimi, B., & Hejazi, T. H. (2014). Closed-loop supply chain network design under a fuzzy environment. Knowledge-Based Systems, 59, 108-120.
Rodriguez, M. A., Vecchietti, A. R., Harjunkoski, I., & Grossmann, I. E. (2014). Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models. Computers & Chemical Engineering, 62, 194-210.
Vahdani, B., Tavakkoli-Moghaddam, R., & Mousavi, S. M. (2013). Scheduling of trucks in cross-docking systems: a hybrid meta-heuristic algorithm. Lecture Notes in Management Science, 5, 125-132.
Vincent, T., Seipp, F., Ruzika, S., Przybylski, A., & Gandibleux, X. (2013). Multiple objective branch and bound for mixed 0-1 linear programming: Corrections and improvements for the biobjective case. Computers & Operations Research, 40(1), 498-509.
Wu, C. J., & Hamada, M. S. (2011). Experiments: planning, analysis, and optimization. John Wiley & Sons.
Zhang, Z. H., Li, B. F., Qian, X., & Cai, L. N. (2014). An integrated supply chain network design problem for bidirectional flows. Expert Systems with Applications, 41(9), 4298-4308.
