An evolutionary approach for the offsetting inventory cycle problem

In inventory management, a fundamental issue is the rational use of required space. Among the numerous techniques adopted, an important role is played by the determination of the replenishment cycle o setting which minimizes the warehouse space within a considered time horizon. The NP-completeness of the O setting Inventory Cycle Problem (OICP) has led the researchers towards the development and the comparison of speci c heuristics. We propose and implement a genetic algorithm for the OICP, whose e ectiveness is validated by comparing its solutions with those found by a mixed integer programming model. The algorithm, tested on realistic instances, shows a high reduction of the maximum space and a more regular warehouse saturation with negligible increase of the total cost. This paper, unlike other papers currently available in literature, provides instances data and results necessary for reproducibility, aiming to become a benchmark for future comparisons with other OICP algorithms.