Industrial Engineering and Management Systems

  • 제14권1호
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  • Pages.1-10
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  • 2015
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  • 1598-7248(pISSN)
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  • 2234-6473(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
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Applying Genetic Algorithm for Can-Order Policies in the Joint Replenishment Problem

  • 투고 : 2014.01.05
  • 심사 : 2015.03.09
  • 발행 : 2015.03.30
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초록

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

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참고문헌

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피인용 문헌

  1. A systematic literature review on the joint replenishment problem solutions: 2006-2015 vol.27, pp.0, 2017, https://doi.org/10.1590/0103-6513.222916
  2. Application of the joint replenishment problem in a collaborative Inventory approach to define resupply plans in urban goods distribution contexts vol.85, pp.207, 2018, https://doi.org/10.15446/dyna.v85n207.72546
  3. A Multi-Item Replenishment Problem with Carbon Cap-and-Trade under Uncertainty vol.12, pp.12, 2020, https://doi.org/10.3390/su12124877