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Stochastic joint replenishment problem with quantity discounts and minimum order constraints

Author

Listed:
  • Ji Seong Noh

    (Hanyang University)

  • Jong Soo Kim

    (Hanyang University)

  • Biswajit Sarkar

    (Hanyang University)

Abstract

This paper analyzes a logistics system involving a supplier who produces and delivers multiple types of items and a buyer who receives and sells to end customers. The buyer controls the inventory of each item by ordering at a preset time interval, which is an integer multiple of a base cycle, to meet the stochastic demands of the end customers. The supplier makes contracts with the buyer that specify that the ordered amount is delivered at the start of each period at a unit price determined by a quantity discount schedule. The contract also specifies that a buyer’s order should exceed a minimum order quantity. To analyze the system, a mathematical model describing activities for replenishing a single type of item is developed from the buyer’s perspective. An efficient method to determine the base cycle length and safety factor that minimizes the buyer’s total cost is then proposed. The single item model is extended to a multiple items joint replenishment model, and algorithms for finding a cost-minimizing base cycle, order interval multipliers, and safety factors are proposed. The result of computational experiments shows that the algorithms can find near-optimal solutions to the problem.

Suggested Citation

  • Ji Seong Noh & Jong Soo Kim & Biswajit Sarkar, 2019. "Stochastic joint replenishment problem with quantity discounts and minimum order constraints," Operational Research, Springer, vol. 19(1), pages 151-178, March.
  • Handle: RePEc:spr:operea:v:19:y:2019:i:1:d:10.1007_s12351-016-0281-6
    DOI: 10.1007/s12351-016-0281-6
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    2. Saha, Subrata & Chatterjee, Debajyoti & Sarkar, Biswajit, 2021. "The ramification of dynamic investment on the promotion and preservation technology for inventory management through a modified flower pollination algorithm," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
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    5. Taleizadeh, Ata Allah & Tafakkori, Keivan & Thaichon, Park, 2021. "Resilience toward supply disruptions: A stochastic inventory control model with partial backordering under the base stock policy," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    6. Paulo Afonso & Vishad Vyas & Ana Antunes & Sérgio Silva & Boris P. J. Bret, 2021. "A Stochastic Approach for Product Costing in Manufacturing Processes," Mathematics, MDPI, vol. 9(18), pages 1-23, September.
    7. Mrudul Y. Jani & Manish R. Betheja & Urmila Chaudhari & Biswajit Sarkar, 2021. "Optimal Investment in Preservation Technology for Variable Demand under Trade-Credit and Shortages," Mathematics, MDPI, vol. 9(11), pages 1-12, June.
    8. Mostafa Parsa & Ali Shahandeh Nookabadi & Zümbül Atan & Yaser Malekian, 2022. "An optimal inventory policy for a multi-echelon closed-loop supply chain of postconsumer recycled content products," Operational Research, Springer, vol. 22(3), pages 1887-1938, July.
    9. T. V. S. R. K. Prasad & Kolla Srinivas & C. Srinivas, 2020. "Investigations into control strategies of supply chain planning models: a case study," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 874-907, September.
    10. Goran Avlijas & Vesna Vukanovic Dumanovic & Miljan Radunovic, 2021. "Measuring the Effects of Automatic Replenishment on Product Availability in Retail Stores," Sustainability, MDPI, vol. 13(3), pages 1-14, January.
    11. Muriel, Ana & Chugh, Tammana & Prokle, Michael, 2022. "Efficient algorithms for the joint replenishment problem with minimum order quantities," European Journal of Operational Research, Elsevier, vol. 300(1), pages 137-150.
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    13. Wu, Chengfeng & Liu, Xin & Li, Annan, 2021. "A loss-averse retailer–supplier supply chain model under trade credit in a supplier-Stackelberg game," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 353-365.

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