Solving the economic lot scheduling problem with deteriorating items using genetic algorithms

The focus of this study is to solve the Economic Lot Scheduling Problem (ELSP) with deteriorating items using the extended basic period approach under Power-of-Two (PoT) policy. The ELSP is concerned with the lot sizing and scheduling decision of n items, and PoT policy requires the replenishment frequency of each item to be a PoT integer. Since the conventional ELSP is shown as an NP-hard problem, we propose a hybrid genetic algorithm (HGA) that is equipped with a feasibility testing procedure, namely, Proc FT, and a binary search heuristic to efficiently solve the ELSP with deteriorating items. We use Proc FT to test the feasibility of the local optimum for the set of replenishment frequencies obtained by the evolutionary process of genetic algorithm. When the local optimum for the set of replenishment frequencies is infeasible, we employ the proposed binary search heuristic to search for a feasible solution with the minimal objective value. We use six benchmark examples to evaluate the performance of the proposed hybrid GA. Our numerical results demonstrate that the proposed hybrid GA is an efficient approach in solving the ELSP with deteriorating items. Therefore, we conclude that the proposed HGA could be a favorable decision support tool for managers in the food industry to efficiently derive their production scheduling and lot-sizing strategies for deteriorating items.