A Knowledge-Guided Co-Evolutionary Algorithm for Energy-Efficient Distributed Assembly Welding Shop Scheduling Problem
研究了节能分布式装配焊接车间调度问题，提出混合整数线性规划模型和知识引导协同进化算法，以最小化总能耗和完工时间，实验表明该算法优于其他六种优化算法。
The growing trend toward decentralization within factories has brought attention to distributed welding shop scheduling problem (DWSP) among both practitioners and researchers. However, despite the prevalence of job-to-product assembly process in industrial fields, the investigation of distributed assembly welding shop scheduling problem (DAWSP) remains unexplored. Meanwhile, given the energy-intensive characteristic of welding operations, addressing energy consumption in welding shop is crucial for achieving environmental sustainability. Thus, this study investigates the energy-efficient DAWSP (EDAWSP), focusing on minimizing total energy consumption (TEC) and makespan. The proposed approaches include a mixed integer linear programming (MILP) model and a knowledge-guided co-evolutionary algorithm (KCEA). In KCEA, a knowledge coefficient is defined to build a bridge that connects the welding part and assembly part. By incorporating knowledge coefficient and weight-sum approach, an effective initialization strategy is proposed for producing a superior initial population. To effectively complete evolutionary process, a co-evolutionary operator is devised based on bi-population strategy. To improve KCEA’s exploitation capability, a local search is developed within the variable neighborhood search (VNS) framework, utilizing six critical-path-based neighborhood structures. Besides, an energy-saving strategy is presented to further minimize TEC without increasing makespan. Finally, a series of comparison experiments are executed. The experimental results illustrate that all improved components of KCEA contribute to its performance, and KCEA outperforms other six optimization algorithms in solving EDAWSP.