January  2008, 4(1): 53-66. doi: 10.3934/jimo.2008.4.53

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

1. 

Diocesan Girls' School, Kowloon, Hong Kong, China

2. 

Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China

3. 

Graduate School of Management, Kent State University, Kent, OH 44244, United States

Received  August 2006 Revised  December 2006 Published  January 2008

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.
Citation: T. W. Leung, Chi Kin Chan, Marvin D. Troutt. A mixed simulated annealing-genetic algorithm approach to the multi-buyer multi-item joint replenishment problem: advantages of meta-heuristics. Journal of Industrial & Management Optimization, 2008, 4 (1) : 53-66. doi: 10.3934/jimo.2008.4.53
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