A genetic algorithm for solving economic lot size scheduling problem

Authors:
Ruhul Sarker;Charles Newton
Affiliations:
Operations Research/Management Science Group, School of Computer Science, The University of New South Wales, ADFA Campus, Northcott Drive, Canberra, ACT 2600, Australia;Operations Research/Management Science Group, School of Computer Science, The University of New South Wales, ADFA Campus, Northcott Drive, Canberra, ACT 2600, Australia
Venue:
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Year:
2002

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Abstract

The purpose of this research is to determine an optimal batch size for a product and purchasing policy of associated raw materials. Like most other practical situation, this manufacturing firm has a limited storage space and transportation fleet of known capacity. The mathematical formulation of the problem indicates that the model is a constrained nonlinear integer program. Considering the complexity of solving such model, we investigate the use of genetic algorithms (GAs) for solving this model. We develop GA code with three different penalty functions usually used for constraint optimizations. The model is also solved using an existing commercial optimization package to compare the solution. The detailed computational results are presented.