Optimizing replenishment polices using Genetic Algorithm for single-warehouse multi-retailer system

W. Yang, Tung Sun Chan, V. Kumar

Research output: Journal article publicationJournal articleAcademic researchpeer-review

54 Citations (Scopus)

Abstract

Coordinating inventory and transportation policies can lead to substantial cost savings and improved service levels especially when the companies relay on third-party logistics providers to transport the products across the supply chain. In this paper, therefore focus has been given on a supply chain system of multi-supplier, single warehouse and multi-retailer with backlogging and transportation capacity. The paper aims to suggest replenishment policies that can minimize system-wide cost by taking advantage of quantity discounts in the transportation cost structures. The problem considered in this paper has been formulated as an integer programming model. The supply chain problem is usually complex and involves massive calculations hence it is difficult to obtain an optimal solution. Therefore, to overcome this issue a Genetic Algorithm (GA) based approach has been suggested to resolve the problem. The computational results demonstrate the robustness and efficacy of the GA in optimizing replenishment policies.
Original languageEnglish
Pages (from-to)3081-3086
Number of pages6
JournalExpert Systems with Applications
Volume39
Issue number3
DOIs
Publication statusPublished - 15 Feb 2012

Keywords

  • Genetic Algorithms
  • Multi-retailer
  • Replenishment policy
  • Supply chain management
  • Warehouse

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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