A mixed simulated annealing-genetic algorithm approach to the multi-buyer multi-item joint replenishment problem: Advantages of meta-heuristics

T. W. Leung, Chi Kin Chan, Marvin D. Troutt

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


The Joint Replenishment Problem (JRP) is a multi-item inventory problem. The objective is to develop inventory policies that minimize total cost (comprised of holding and setup costs) over the planning horizon. In this paper we consider the extension of this problem to the multi-buyer, multi-item version of the JRP. We propose and test a mixed simulated annealing-genetic algorithm (SAGA) for the extended problem. Tests are conducted on problems from a leading bank in Hong Kong. Results are also compared to a pure GA approach and several interesting observations are made on the value of such meta-heuristics.
Original languageEnglish
Pages (from-to)53-66
Number of pages14
JournalJournal of Industrial and Management Optimization
Issue number1
Publication statusPublished - 28 Nov 2008


  • Genetic algorithm
  • Joint Replenishment Inventory Problems
  • Meta-heuristics
  • Multi-buyer extension
  • Simulated annealing

ASJC Scopus subject areas

  • Business and International Management
  • Strategy and Management
  • Control and Optimization
  • Applied Mathematics

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