A genetic algorithm for solving economic lot size scheduling problem (original) (raw)

The purpose of this research is to determine an optimal batch size for a product, and purchasing policy of associated raw materials. The mathematical model for this problem 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 genetic algorithm 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 detail computational experiences are presented.