Multilevel lot sizing with a genetic algorithm under fixed and rolling horizons

This paper proposes a genetic algorithm (GA) as a heuristic for multilevel lot sizing, combined with the Bit_Mod heuristic developed in this context, and invoking adaptive probabilities for crossover and mutation. The influen ce of various parameters under fixed and rolling horizons is detailed. Desi gn of experiments methodology is used in this connection. In the rolling ho rizon, the behaviour of the GA and other rules are compared with and withou t freezing the plan. The performance of the GA is compared with the cost-mo dified Wagner Whitin algorithm and cost-modified silver meal methods. The s uperiority of the proposed method is discussed, and case studies are given.