A Hybrid Genetic Algorithm for the Single Machine Scheduling Problem

Authors:
David M. Miller;Hui-Chuan Chen;Jessica Matson;Qiang Liu
Affiliations:
Commerce and Business Administration, University of Alabama, USA;College of Engineering, University of Alabama, USA;College of Engineering, Tennessee Tech, USA;McKesson HBOC Co., Atlanta GA, USA
Venue:
Journal of Heuristics
Year:
1999

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Abstract

A hybrid genetic algorithm (HGA) is proposed for the singlemachine, single stage, scheduling problem in a sequence dependentsetup time environment within a fixed planning horizon (SSSDP). Itincorporates the elitist ranking method, genetic operators, and ahill-climbing technique in each searching area. To improve theperformance and efficiency, hill climbing is performed by uniting theWagner-Whitin Algorithm with the problem-specific knowledge. Theobjective of the HGA is to minimize the sum of setup cost, inventorycost, and backlog cost. The HGA is able to obtain a superiorsolution, if it is not optimal, in a reasonable time. Thecomputational results of this algorithm on real life SSSDP problemsare promising. In our test cases, the HGA performed up to 50%better than the Just-In-Time heuristics and 30% better than thecomplete batching heuristics.