Solving the stochastic dynamic lot‐sizing problem through nature‐inspired heuristics

We investigate the dynamic lot‐size problem under stochastic and non‐stationary demand over the planning horizon. The problem is tackled by using three popular heuristic methods from the fields of evolutionary computation and swarm intelligence, namely particle swarm optimization, differential evolution and harmony search. To the best of the authors' knowledge, this is the first investigation of the specific problem with approaches of this type. The algorithms are properly manipulated to fit the requirements of the problem. Their performance, in terms of run‐time and solution accuracy, is investigated on test cases previously used in relevant works. Specifically, the lot‐size problem with normally distributed demand is considered for different planning horizons, varying from 12 up to 48 periods. The obtained results are analyzed, providing evidence on the efficiency of the employed approaches as promising alternatives to the established Wagner–Whitin algorithm, as well as hints on their proper configuration.