Response strategies for coping with imperfect items of a joint replenishment model with the adaptive bare-bone differential evolution

Abstract

In joint replenishment problem with random number of imperfect items (IJRP), it is a great challenge for managers to correctly ascertain the number of imperfect items and take the right response strategy to make rectifications simultaneously. In this paper, the effectiveness of four common response strategies, i.e., scrap, all-unit quantity discount, rework & repair, and buy replacements for coping with imperfect items are investigated based on IJRP for the first time, respectively. Considering the NP-hard nature of the classical JRP and its extensions, as well as enhancing the explorable performance in crossing scale-levels of IJRP models and exploitable performance in diversifying the solution population of bare-bones differential evolutionary (BBDE), a meta-heuristic algorithm named as the adaptive BBDE (ABBDE) is designed by introducing an problem scale related adaptive recombination probability and a generalized opposition-based learning (OBL). Through numerical experiments, it has been found that (1) The adaptive improvements of ABBDE have been verified through obtaining the lower best-found total cost (TC), the lowest mean value, and the smallest standard error comparing to that of genetic evolution algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), bare-bones PSO (BBPSO), BBDE. (2) Parameter sensitivity analyses indicate that the scale of yearly demand, lead-time, and significance of uncertainty have significant impacts on system cost, whilst the parameters pertinent to specific response strategy are main factors causing system cost disturbance. (3) Management insights and limitations on adoption of a right response strategy and the practical usage of ABBDE are obtained.