تعیین سیاست بازپرسازی موجودی و انتخاب تأمین‌کننده در زنجیره‌‌تأمین یکپارچه کالای فاسدشدنی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی صنایع، دانشکدۀ مهندسی، دانشگاه کردستان، سنندج، ایران

2 استادیار گروه مهندسی صنایع، دانشکدۀ مهندسی، دانشگاه کردستان، سنندج، ایران

3 دانشجوی دکتری مهندسی صنایع، دانشکدۀ مهندسی، دانشگاه کردستان، سنندج، ایران

چکیده

انتخاب تأمین­کننده و همچنین تعیین سیاست مناسب بازپرسازی، همواره یکی از دغدغه­های اصلی در سیستم­های تولیدی و زنجیره‌تأمین است. در این مقاله یک زنجیره‌تأمین یکپارچه خریدار –تأمین‌کننده برای کالای فاسدشدنی مورد بررسی قرار می‌گیرد. خرده‌فروش، محصول موردنیاز خود از تولیدکنندگان مختلف تأمین می­کند. محصول خریداری شده از تولیدکنندگان از نوع محصولات فسادپذیر است و نرخ فساد آن به‌صورت درصد ثابتی از سطح موجودی خرده‌فروش و تولیدکنندگان بیان می‌شود. در چنین شرایطی، انتخاب تأمین‌کنندگان و تعیین میزان خرید از هر تأمین‌کننده تأثیر به‌سزایی در بهینه‌سازی زنجیره­تأمین دارد. خرده‌فروش بر اساس تقاضای مشخص و ثابت مشتریان نهایی سعی می‌کند سیاست بهینه‌ی بازپرسازی خود را تعیین کند و بر اساس این سیاست بهینه، فرآیند خرید مواد اولیه از تولیدکنندگان و همچنین زمان خرید و حجم خرید از هر تولیدکننده مشخص خواهد شد. در این میان آن دسته از تأمین‌کنندگانی که توانایی تأمین محصول با کمترین هزینه در زمان مقرر را دارند، انتخاب می‌شوند. هدف اصلی در این مقاله انتخاب تأمین­کنندگان و تعیین سیاست بازپرسازی به‌گونه­ای‌که کل هزینه‌های زنجیره­­تأمین شامل هزینه­های خرده‌فروش و تأمین­کنندگان حداقل شود. در این مدل از رویکرد برنامه­ریزی خطی مختلط عدد صحیح استفاده شده است. درنهایت این مدل با یک مثال عددی بررسی شد.

کلیدواژه‌ها


عنوان مقاله [English]

Determining the Replenishment Policy and Supplier Selection in Integrated Supply Chain for Deteriorating Products

نویسندگان [English]

  • Aryan Mozdgir Mobbarhan 1
  • Heybat Ollah Sadeghi 2
  • Simin Arbabi 3
1 M.A. Master's student in Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
2 Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
3 Ph.D. Student, Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
چکیده [English]

Supplier selection and determining appropriate replenishment policies is one of the major concerns in production systems and supply chains. This paper describes the supply chain of integrated buyers and suppliers of perishable product. The retailer supplies the product it needs from different manufacturers. Products purchased by manufacturers are perishable products, and their Perishable rate is expressed as a fixed percentage of retailers' and manufacturers' inventories. In this situation, the selection of suppliers and the determination of the quantity to purchase from each supplier have a great impact on the optimization of the supply chain. Based on the fixed demand of the end customers, the retailer tries to determine its optimal replenishment policy, and based on this optimal policy, the process of purchasing raw materials from manufacturers, as well as the time and amount of purchase from each manufacturer will be determined. Among these, the supplier that can deliver the product within the specified time and at the lowest cost will be selected. The main purpose of this paper is to select suppliers and determine replenishment policies in a way that minimizes the total cost of this chain of supply, including retailer and supplier costs. This model uses a mixed-integer linear programming approach. Finally, this model was checked with a numerical example.

کلیدواژه‌ها [English]

  • Supplier Selection
  • Deteriorating Products
  • Integrated Supply Chain
  • Replenishment Policy
  • C. Ho, M.K. Shalishali, T.-L.B. Tseng, D.S. Ang,(2022), “Opportunities in green supply chain management”, The Coastal Business Journal, 8: 2.
  • J. Deshmukh, H. Vasudevan,(2014), “Emerging supplier selection criterion in the context of traditional vs green supply chain management”, International Journal of Managing Value and Supply Chains, 5: 19.
  • Aissaoui, M. Haouari, E. Hassini,(2007), “Supplier selection and order lot sizing modeling: A review”, Computers & operations research, 34: 3516-3540.
  • S. Pishvaee, M. Rabbani,(2011), “A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment”, Advances in Engineering Software, 42: 57-63.
  • Morganti, J. Gonzalez-Feliu,(2015), “City logistics for perishable products. The case of the Parma's Food Hub”, Case Studies on Transport Policy, 3: 120-128.
  • Khodaparasti, M.E. Bruni, P. Beraldi, H. Maleki, S. Jahedi,(2018), “A multi-period location-allocation model for nursing home network planning under uncertainty”, Operations Research for Health Care, 18: 4-15.
  • Khanlarzade, B. Yegane, I. Kamalabadi, H. Farughi,(2014), “Inventory control with deteriorating items: A state-of-the-art literature review”, International journal of industrial engineering computations, 5: 179-198.
  • Musavi, A. Bozorgi-Amiri,(2017), “A multi-objective sustainable hub location-scheduling problem for perishable food supply chain”, Computers & Industrial Engineering, 113: 766-778.
  • -J. Chung, C.-N. Lin,(2001), “Optimal inventory replenishment models for deteriorating items taking account of time discounting”, Computers & Operations Research, 28: 67-83.
  • -T. Teng, C.-T. Chang,(2005), “Economic production quantity models for deteriorating items with price-and stock-dependent demand”, Computers & Operations Research, 32: 297-308.
  • T. Balkhi,(2011), “Optimal economic ordering policy with deteriorating items under different supplier trade credits for finite horizon case”, International Journal of Production Economics, 133: 216-223.
  • Sadeghi, H. Golpîra, S.A.R. Khan,(2021), “Optimal integrated production-inventory system considering shortages and discrete delivery orders”, Computers & Industrial Engineering, 156: 107233.
  • Tayal, S. Singh, R. Sharma, A.P. Singh,(2015), “An EPQ model for non-instantaneous deteriorating item with time dependent holding cost and exponential demand rate”, International Journal of Operational Research, 23: 145-162.
  • K. Chan, W.H. Wong, A. Langevin, Y. Lee,(2017), “An integrated production-inventory model for deteriorating items with consideration of optimal production rate and deterioration during delivery”, International Journal of Production Economics, 189: 1-13.
  • Dobson, E.J. Pinker, O. Yildiz,(2017), “An EOQ model for perishable goods with age-dependent demand rate”, European Journal of Operational Research, 257: 84-88.
  • Janssen, A. Diabat, J. Sauer, F. Herrmann,(2018), “A stochastic micro-periodic age-based inventory replenishment policy for perishable goods”, Transportation Research Part E: Logistics and Transportation Review, 118: 445-465.
  • Tiwari, L.E. Cárdenas-Barrón, M. Goh, A.A. Shaikh,(2018), “Joint pricing and inventory model for deteriorating items with expiration dates and partial backlogging under two-level partial trade credits in supply chain”, International Journal of Production Economics, 200: 16-36.
  • A.-A. Khan, A.A. Shaikh, G.C. Panda, I. Konstantaras, A.A. Taleizadeh,(2019), “Inventory system with expiration date: Pricing and replenishment decisions”, Computers & Industrial Engineering, 132: 232-247.
  • Chen, X. Chen, M.F. Keblis, G. Li,(2019), “Optimal pricing and replenishment policy for deteriorating inventory under stock-level-dependent, time-varying and price-dependent demand”, Computers & Industrial Engineering, 135: 1294-1299.
  • Azadi, S.D. Eksioglu, B. Eksioglu, G. Palak,(2019), “Stochastic optimization models for joint pricing and inventory replenishment of perishable products”, Computers & industrial engineering, 127: 625-642.
  • Lashgari, S.J. Sadjadi, M. Sahihi,(2019), “A multi-product, multi-period model to select supplier for deteriorating products while considering uncertainty as well as backorder”, Journal of Industrial Engineering International, 15: 93-101.
  • Yang, H. Chi, W. Zhou, T. Fan, S. Piramuthu,(2020), “Deterioration control decision support for perishable inventory management”, Decision Support Systems, 134: 113308.
  • رجبی, زهرا, صادقی, هیبت اله, و محمودی, انور. (1399). تعیین سیاست بهینه سفارش‌دهی در مدل چنددوره‌ای احتمالی با درنظر گرفتن قیمت خرید تصادفی. نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید, 8(16), 95-111. DOI 10.22084/ier.2020.20328.1909.
  • A.H.S. Amiri, A. Zahedi, M. Kazemi, J. Soroor, M. Hajiaghaei-Keshteli,(2020), “Determination of the optimal sales level of perishable goods in a two-echelon supply chain network”, Computers & Industrial Engineering, 139: 106156.
  • عبدی, فرید, فاروقی, هیوا, صادقی, هیبت اله, و ارکات, جمال. (1399). مکان‌یابی- موجودی- تخصیص افزونگی چندهدفه در زنجیره‌تأمین تک‌دوره‌ای با تقاضای احتمالی. نشریه پژوهش های مهندسی صنایع در سیستم های تولید DOI 10.22084/ier.2021.3925.
  • Sadeghi,(2019), “A forecasting system by considering product reliability, POQ policy, and periodic demand”, Journal of Quality Engineering and Production Optimization, 4: 133-148.
  • Rajabi, H. Sadeghi, A. Mahmoodi,(2020), “Determining The Optimal Ordering Policy In A Multi-Period Stochastic Model With The Uncertainty Purchase Price”, Journal of Industrial Engineering Research in Production Systems, 8: 95-111.
  • H. Ghodsypour, C. O’brien,(2001), “The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint”, International journal of production economics, 73: 15-27.
  • Xia, Z. Wu,(2007), “Supplier selection with multiple criteria in volume discount environments”, Omega, 35: 494-504.
  • Rezaei, M. Davoodi,(2008), “A deterministic, multi-item inventory model with supplier selection and imperfect quality”, Applied Mathematical Modelling, 32: 2106-2116.
  • G. Kheljani, S. Ghodsypour, C. O’Brien,(2009), “Optimizing whole supply chain benefit versus buyer's benefit through supplier selection”, International Journal of Production Economics, 121: 482-493.
  • Hajian Heidary, S. Fatemi Ghomi, B. Karimi,(2015), “Supply chain network design for deteriorating items with discount on transportation cost”, Scientia Iranica, 22: 2634-2643.
  • S. Atabaki, M. Mohammadi,(2017), “A genetic algorithm for integrated lot sizing and supplier selection with defective items and storage and supplier capacity constraints”, International Journal of Operational Research, 28: 183-200.
  • K. Jauhar, M. Pant,(2017), “Integrating DEA with DE and MODE for sustainable supplier selection”, Journal of computational science, 21: 299-306.
  • K. Alfares, R. Turnadi,(2018), “Lot sizing and supplier selection with multiple items, multiple periods, quantity discounts, and backordering”, Computers & Industrial Engineering, 116: 59-71.
  • Sarkar, D.K. Pratihar, B. Sarkar,(2018), “An integrated fuzzy multiple criteria supplier selection approach and its application in a welding company”, Journal of Manufacturing Systems, 46: 163-178.
  • Esmaeili, S.N. Ghobadi,(2018), “A game theory model for pricing and supplier selection in a closed-loop supply chain”, International Journal of Procurement Management, 11: 472-494.
  • G. Gören,(2018), “A decision framework for sustainable supplier selection and order allocation with lost sales”, Journal of Cleaner Production, 183: 1156-1169.
  • Yousefi, M.J. Rezaee, M. Solimanpur,(2019), “Supplier selection and order allocation using two-stage hybrid supply chain model and game-based order price”, Operational Research, DOI: 1-36.
  • Dobos, G. Vörösmarty,(2019), “Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA)”, International Journal of Production Economics, 209: 374-380.
  • Govindan, H. Mina, A. Esmaeili, S.M. Gholami-Zanjani,(2020), “An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty”, Journal of Cleaner Production, 242: 118317.
  • Alejo-Reyes, A. Mendoza, E. Olivares-Benitez,(2021), “A heuristic method for the supplier selection and order quantity allocation problem”, Applied Mathematical Modelling, 90: 1130-1142.
  • Golpîra, H. Sadeghi, S. Bahramara,(2021), “Electricity supply chain coordination: Newsvendor model for optimal contract design”, Journal of Cleaner Production, 278: 123368.