To improve the reliability of production systems in the plastics industry, researchers are now taking mould maintenance into consideration, besides machine maintenance, in the production scheduling problem. Different strategies and approaches have been proposed to solve the production scheduling with mould maintenance (PS-MM) problem. However, it remains a challenge to provide a satisfactory solution. In this research, a new hybrid metaheuristic algorithm (TLPSO-VNS algorithm) is proposed, which is a combination of the three-level particle swarm optimization (TLPSO) algorithm devised in this study and variable neighbourhood search (VNS). Differing from the joint scheduling strategies used in existing research, this study divides the integrated problem into three sub-problems and solves them through three interrelated PSOs named TLPSO. Then, the solutions obtained by TLPSO are enhanced by VNS. The key characteristics of TLPSO and VNS are employed simultaneously to achieve superior solutions in solving the addressed optimization problem. In the proposed hybrid algorithm, the TLPSO performs a global search whereas the VNS has a local search role. These two techniques complement each other to enhance the search diversification and intensification. Numerical experiments on a variety of simulated scenarios show the efficiency and effectiveness of the proposed algorithm by comparing it with other algorithms.

为了提高塑料行业生产系统的可靠性，研究人员现在在生产调度问题中，除了机器维护之外，还考虑了模具维护。已经提出了不同的策略和方法来解决带有模具维护（PS-MM）问题的生产计划。但是，提供令人满意的解决方案仍然是一个挑战。本研究提出了一种新的混合元启发式算法（TLPSO-VNS算法），该算法是本研究中设计的三级粒子群优化算法（TLPSO）和变量邻域搜索（VNS）的结合。与现有研究中使用的联合调度策略不同，本研究将集成问题分为三个子问题，并通过三个相互关联的PSO（称为TLPSO）解决了这些问题。然后，VLP增强了TLPSO获得的解决方案。TLPSO和VNS的关键特性可同时使用，以实现解决已解决的优化问题的出色解决方案。在提出的混合算法中，TLPSO执行全局搜索，而VNS具有局部搜索角色。这两种技术相辅相成，以增强搜索的多样性和强度。通过与其他算法进行比较，在各种模拟情况下的数值实验表明了该算法的效率和有效性。TLPSO执行全局搜索，而VNS具有本地搜索角色。这两种技术相辅相成，以增强搜索的多样性和强度。通过与其他算法进行比较，在各种模拟情况下的数值实验表明了该算法的效率和有效性。TLPSO执行全局搜索，而VNS具有本地搜索角色。这两种技术相辅相成，以增强搜索的多样性和强度。通过与其他算法进行比较，在各种模拟情况下的数值实验表明了该算法的效率和有效性。