A genetic algorithm for solving single level lotsizing problems

The single level lot-sizing problem arises whenever a manufacturing company wishes to translate an aggregate plan for production of an end item into a detailed planning of its production. Although the cost driven problem is widely studied in the literature, only laborious dynamic programming approaches are known to guarantee global minimum. Thus, stochastically-based heuristics that have the mechanism to escape from local minimum are needed. In this paper a genetic algorithm for solving single level lot-sizing problems is proposed and the results of applying the algorithm toexample problems are discussed. In our implementation, a lot-sizing population-generating heuristic is used to feed chromosomes to a genetic algorithm with operators specially designed for lot-sizing problems. The combination of the population-generating heuristic with genetic algorithm results in a faster convergence in finding the optimal lot-sizing scheme due to the guaranteed feasibility of the initial population