A Knowledge-Driven Cooperative Coevolutionary Algorithm for Integrated Distributed Production and Transportation Scheduling Problem

With increasing market competition, integration between production and transportation in supply chain has been valued to improve the operational performance. Meanwhile, the distributed manufacturing has emerged as a modern paradigm, fostering flexible and intelligent development. In this paper, we address an integrated distributed production and transportation scheduling problem (IDPTSP), taking into account the production plan in distributed heterogeneous hybrid flow-shops, as well as the transportation decisions made by third-party logistics provider. First, the objective calculation is presented to minimize total costs and energy consumption during production and transportation process, and problem property is analyzed to obtain an optimal pickup time strategy. Second, a knowledge-driven cooperative coevolutionary algorithm (KCCA) is proposed, incorporating multiple problem-specific heuristics and operators based on the characteristics of the subproblems in IDPTSP. Third, Q learning assisted cooperative coevolutionary search is proposed via analyzing the interconnections among different subproblems to effectively and efficiently explore their search space. Fourth, local intensification search with multiple operators fusing prior knowledge is incorporated for low-density regions in objective space to enhance exploitation ability. Extensive experiments are carried out to test performances of the KCCA and the numerical comparisons demonstrate effectiveness of the specific designs and superiority of the KCCA over state-of-the-art algorithms in solving the IDPTSP. Note to Practitioners—Drawing inspiration from a real-life case in electronics supply, the focus on distributed manufacturing and the integration of manufacturing and logistics has garnered significant attention from managers due to the enterprises’ need to reduce intermediate stocks and enhance operational performance. The primary focus of enterprises is on reducing costs during production and transportation. Additionally, energy consumption has become a crucial consideration due to rising energy costs and limited energy supplies. However, the complexity of this practical scenario has significantly increased due to the presence of multiple subproblems and objectives. Traditional scheduling methods are unable to effectively and efficiently obtain a Pareto front with convergence and diversity. Based on the characterises of the scheduling problem, we provided a knowledge-driven cooperative coevolutionary algorithm to minimize total costs and energy consumption during production and transportation process. At production stage, factory assignment and job sequence are determined in distributed heterogeneous hybrid flow-shops, while job batching and vehicle routing are determined at transportation stage. Through the analysis of problem properties, an optimal pickup time strategy is introduced and multiple rules and search operators are provided to enhance optimization efficiency. Meanwhile, the framework of cooperative coevolutionary search offers an effective approach to addressing such complex integrated scheduling problems. From experimental comparisons, the effectiveness of the proposed algorithm is verified surpassing state-of-the-art algorithms. Therefore, practitioners can benefit from non-dominated schedules characterized by reduced costs and enhanced energy efficiency.