To enhance the operational performance of supply chains under the trends of globalization and customization, integrated multi-factory production and distribution has recently attracted increasing attention. This paper presents a novel integrated multi-factory production scheduling and vehicle routing problem. In this problem, a set of customer orders is first assigned to several distributed factories for production, each of which is arranged as a hybrid flow shop (HFS). Owing to the technical or physical aspects, factory eligibility is considered in the production stage, where some orders can only be processed in a subset of factories. The finished products are then delivered by capacitated vehicles, subject to customer time windows. As a combination of the distributed HFS scheduling problem and the vehicle routing problem, three types of decisions have to be made, namely factory allocation, job scheduling, and vehicle assignment and routing. Considering the NP-hardness of the studied problem, a hybrid algorithm that integrates a distribution estimation algorithm (EDA) with an adaptive large neighborhood search (ALNS) is developed to generate solutions. To improve the local search capability of this algorithm, Q-Learning is employed to dynamically determine the destroy-and-repair operators of ALNS. Computational results on both small-sized and large-sized test problems indicate the superiority of the proposed algorithm.
Wang et al. (Wed,) studied this question.