Conference Proceedings

A hybrid genetic algorithm to solve a lot-sizing and scheduling problem

Andrea Staggemeier, Alistair Clark, Uwe Aickelin, Jim Smith

The 16th triennial conference of the International Federation of Operational Research Societies (IFORS2002) | International Federation of Operational Research Societies (IFORS) | Published : 2002


This paper reports a lot-sizing and scheduling problem, which minimizes inventory and backlog costs of multiple products on M parallel machines with sequence-dependent set-up times over T periods. Problem solutions are represented as product subsets (ordered or unordered) for each machine m at each period t. The optimal lot sizes are then determined applying a linear program. A genetic algorithm searches either over ordered or over unordered subsets (which are implicitly ordered using a fast ATSPtype heuristic) to try to identify an optimal solution. Initial computational results are presented, comparing the speed and solution quality of the ordered and unordered genetic algorithm approaches..

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University of Melbourne Researchers