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. ► 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. ► 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.