ارزیابی یک مدل یکپارچه مکان‌یابی- موجودی تسهیلات با رویکرد آزادسازی لاگرانژ همراه با مطالعه موردی در صنعت کالاهای تندمصرف

نوع مقاله : پژوهشی

نویسندگان

1 گروه مهندسی صنایع- دانشکده فنی و مهندسی - دانشگاه الزهرا

2 پژوهشگاه نیرو ،تهران-ایران

3 دانشکده مهندسی صنایع و سیستم‌های مدیریت، دانشگاه صنعتی امیرکبیر، تهران، ایران

چکیده

مدیریت زنجیره تأمین و طراحی شبکه توزیع در سال­های اخیر مورد توجه بسیاری از محققان قرار گرفته است. این مقاله به مطالعه سیستم چند محصولی مسئله مکان‌یابی تسهیلات در شبکه توزیع می­پردازد که شامل تصمیم­های مربوط به موجودی، برای هر محصول، مکان‌یابی تسهیلات و تخصیص مشتریان است. در ارزیابی مدل یکپارچه از اطلاعات یک شرکت فعال در صنعت کالاهای تندمصرف خانوار نیز استفاده شده است. به‌دلیل پیچیدگی در دستیابی به راه حل بهینه در مسایل با ابعاد واقعی، یک روش حل مبتنی بر الگوریتم آزادسازی لاگرانژ و روش زیر گرادیان پیشنهاد شده است. به منظور نمایش کارائی روش حل پیشنهادی، نتایج حاصله با نرم­افزار بهینه­سازی مقایسه شد. نتایج محاسباتی نشان می­دهد که عملکرد الگوریتم پیشنهادی از نظر شاخص­های مختلف شامل متوسط استفاده از ظرفیت مراکز توزیع  (88/3%)، متوسط شکاف دوگانگی (0/71%) و بدترین شگاف گزارش شده (1/77%) بسیار امیدوارکننده است. همچنین نتایج مطالعه موردی صورت گرفته نیز کاهش تعداد مراکز توزیع از 17 به 12 مرکز را پیشنهاد می­دهد. در پایان به چند نکته مدیریتی نیز اشاره شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of an integrated facility location-inventory model using Lagrangian relaxation approach with a FMCG case study

نویسندگان [English]

  • Gholamreza Nasiri 1
  • Akbar Namazi Tajarogh 2
  • Hamid Davoudpour 3
1 Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
2 Niroo Research Institute, Tehran, Iran
3 3Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
چکیده [English]

Supply chain management and distribution network design have attracted the attention of many researchers during recent years. This paper addresses a multi-product system of location problem in distribution network that incorporates inventory decisions for each product into capacitated facility location models. In the development of mathematical model, the data of real case study of fast moving consumer goods company is used. Due to difficulty of obtaining the optimal solution in real-scaled problems, a heuristic solution approach based on Lagrangian relaxation algorithm and sub-gradient method is presented. The proposed solution method is compared with the optimization software on randomly generated test problems with different size. Computational results show that the performance of proposed solution algorithm is very promising in terms of various indexes including 88.3% of DCs capacity, duality gap average and the worst case 0.71% and 1.77% respectively. The results of considered case study also proposed to reduce the number of distribution centers from 17 to 12 centers. Finally, some managerial insights are mentioned.

کلیدواژه‌ها [English]

  • Facility location
  • Inventory control
  • Lagrangian relaxation
  • Risk pooling effect
  • Case study
[1] M. Najjartabar-Bisheh, G.R. Nasiri, and H. Davoudpour, “Multi Echelon Supply Chain Model with A New Rationing Policy,” Proceedings of the 2019 IISE Conference, Orlando, Florida, USA, pp. 18-21, 2019.##
[2] G. R. Nasiri, N. Ghaffari, and H. Davoudpour, “Location-inventory and shipment decisions in an integrated distribution system: an efficient heuristic solution,” Eur. J. Ind. Eng., vol. 9 , no. 5, pp. 613–637, 2015.##
[3] G. R. Nasiri and F. Jolaie, “Supply Chain Network Design in Uncertain Environment: A review and classification of related models, "Optimization Techniques for Problem Solving in Uncertainty", first edition, book series in IGI Global publisher, 2018.##
[4] E. Ahmadzadeh, and B. Vahdani, “A location-inventorypricing model in a closed loop supply chain network with correlated demands and shortages under a periodic review system,” Comput. Chem. Eng., vol. 101, pp. 148–166, 2017.##
[5] O. Kaya and B. Urek, “A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain,” Comput. Oper. Res., vol. 65, pp. 93–103, 2016.##
[6] I. Correia, and T. Melo, “A multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment,” Comput. Ind. Eng., vol. 110, pp. 307– 321, 2017.##
[7] M. S. Puga, and J. S. Tancrez, “A heuristic algorithm for solving large location–inventory problems with demand uncertainty,” Eur. J. Oper. Res., vol. 259, no. 2, pp. 413–423, 2017.##
[8] M. Seifbarghy and S. Malekpour Kolbadinejhad, “Development of a closed loop supply chain network considering environmental factors and location inventory decisions under uncertainty,” Iran. J. Supply Chain Manag., vol. 22, no. 67, pp. 4-22, 2020 (in Persian).##
[9] M. Biuki, A. Kazemi, and A. Alinezhad, “An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network,” J. Clean. Prod., vol. 260, p. 120842, 2020.##
[10] H. Golpîra, “Optimal integration of the facility location problem into the multi-project multi-supplier multi-resource Construction Supply Chain network design under the vendor managed inventory strategy,” Expert Syst. Appl., vol. 139, p. 112841, 2020.##
[11] Y. Liu, E. Dehghani, M.S. Jabalameli, A. Diabat, and C-C. Lu, “A coordinated location-inventory problem with supply disruptions: A two-phase queuing theory–optimization model approach,” Comput. Ind. Eng., vol. 142, p. 106326, 2020.##
[12] S. M. Mousavi, P. M. Pardalos, S. T. Akhavan Niaki, A. Fügenschuh, and M. Fathi, “Solving a continuous periodic review inventory-location allocation problem in vendor-buyer  supply chain under uncertainty,” Comput. Ind. Eng., vol. 128, pp. 541-552, 2019.##
[13] Z. Dai, F. Aqlan, F. X. Zheng, and K. Gao, “A locationinventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints,” Comput. Ind. Eng., vol. 119, pp. 338-352, 2018.##
[14] F. J. Tapia-Ubeda, P. A. Miranda, and M. Macchi, “A Generalized Benders Decomposition based algorithm for an inventory location problem with stochastic inventory capacity constraints,” Eur. J. Oper. Res., vol. 267, no. 3, pp. 806-817, 2018.##
[15] F. Rayat, M. M. Musavi, and A. Bozorgi-Amiri, “Bi-objective reliable location-inventory-routing problem with partial backordering under disruption risks: A modified AMOSA approach,” Appl. Soft Comput., vol. 59, pp. 622-643, 2017.##
[16] A. Hiassat, A. Diabat, and I. Rahwan, “A genetic algorithm approach for location-inventory-routing problem with perishable products,” J. Manuf. Syst., vol. 42, pp.93-103, 2017.##
[17] N. A. Pujaria, T. S. Hale, and F. Haq, “A continuous approximation procedure for determining inventory distribution schemas within supply chains,” Eur. J. Oper. Res., vol. 186, no. 1, pp. 405-422, 2008.##
[18] P. A. Miranda, and R. A. Garrido, “Valid inequalities for Lagrangian relaxation in an inventory location problem with stochastic capacity,” Transp. Res. E: Logist. Transp. Rev., vol. 44, no. 1, pp. 47-65, 2008.##
[19] P. A. Miranda, R. A. Garrido, “Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand,” Transp. Res. E: Logist. Transp. Rev., vol. 40, no. 3, pp. 183–207, 2004.##
[20] M.S. Puga, and J-S., Tancrez, “A heuristic algorithm for solving large location–inventory problems with demand uncertainty,” Eur. J. Oper. Res., vol. 259, no. 2, pp. 413-423, 2017.##
[21] A. Kumar Saha, A., Paul, A. Azeem and S. Kumar Paul, “Mitigating partial-disruption risk: A joint facility location and inventory model considering customers’ preferences and the role of substitute products and backorder offers,” Comput. Oper. Res., vol. 117, p. 104884, 2020.##
[22] H. Farughi, and M. Ashrafi Fashi, “The multi-echelon supply chain network design subject to multiple reliable strategies in distribution centers level,” J. Ind. Eng. Res. Prod. Syst., vol. 5, no. 10, pp. 53-67, 2017 (in Persian).##
[23] D. Saffari, A. aghaie, E. roghanian, “Multi-layer locationallocation model within queuing networks framework,” J. Ind. Eng. Res. Prod. Syst., vol. 6, no. 12, pp. 49-61, 2018. (in Persian).##
[24] S. Chandra, M. Sarkhel, and A.K. Vatsa, “Capacitated facility location-allocation problem for wastewater treatment in an industrial cluster,” Comput. Oper. Res., p. 105338, 2021.##
[25] G. R. Nasiri, R. Zolfaghari, and H. Davoudpour, “An integrated supply chain production–distribution planning with stochastic demands,” Comput. Ind. Eng., vol. 77, pp. 35–45, 2014.##
[26] G. R. Nasiri, H. Davoudpour, and B. Karimi, “The impact of integrated analysis on supply chain management: a coordinated approach for inventory control policy,” Int. J. Supply Chain Manag., vol. 15, no. 4, pp. 277–289, 2010.##