A Discrete Artificial Bee Colony Algorithm for the Economic Lot Scheduling Problem with Returns

Abstract

In this study we model the Economic Lot Scheduling problem with returns (ELSPR) under the basic period (BP) policy with power-of-two (PoT) multipliers and solve it with a discrete artificial bee colony (DABC) algorithm. Tang and Teunter [1] is the first to consider the well-known economic lot scheduling problem (ELSP) with return flows and remanufacturing opportunities. Teunter et al. [2] and Zanoni et al. [3] recently extended this first study by proposing heuristics for the common cycle policy and for a modified basic period policy respectively. As Zanoni et al. [3] we restrict the study to consider independently managed serviceable inventory to test the performance of the proposed algorithm. Our study to the best of our knowledge is the first to solve ELSPR using a meta-heuristic. ABC is a swarm-intelligence-based meta-heuristic inspired by the intelligent foraging behaviors of honeybee swarms. In this study we implement the ABC algorithm with some modifications to handle the discrete decision variables. In the algorithm we employ two different constraint handling methods in order to have both feasible and infeasible solutions within the population. Our DABC is also enriched with a variable neighborhood search (VNS) algorithm to further improve the solutions. We test the performance of our algorithm on the two problem instances used in Zanoni et al. [3]. The numerical study depicts that the proposed algorithm performs well under the BP-PoT policy and it has the potential of improving the best known solutions when we relax BP PoT and independently managed serviceable inventory restrictions in the future.