A genetic algorithm for lot sizing optimization with a capacity loading criterion

The lot sizing problems are one of the basic optimization problems, which have to be solved during production planning. Only a few nature-inspired algorithms have been proposed for solving such problems. In this study the authors propose a genetic algorithm for a discrete lot sizing problem with so called small buckets and the criterion of capacity utilization. The results are compared with CPLEX MIP solver and other heuristics. The genetic algorithm proposed here gives solutions, which are 0.4 to 2.9 percent away from the theoretical lower bound.