A learning-and-knowledge-assisted multi-population collaborative evolutionary algorithm for integrated design-production-distribution scheduling problems in mass personalized customization

Recently, new requirements are proposed for the manufacturing industry transitioning to distributed production models due to emergence of mass personalized customization. Integrated scheduling of design, production and distribution, mixed management of batch and flexible manufacturing are becoming the imminent challenges faced by enterprises. This article proposes an integrated design-production-distribution scheduling problem in distributed mixed shops. It considers distributed flow shops for batch manufacturing and distributed flexible job shops for flexible manufacturing. First, a mixed integer linear programming model is formulized to minimize the maximum completion time, total costs, and total tardiness. Second, a learning-and-knowledge-assisted multi-population collaborative evolutionary algorithm is developed to settle the model. Genetic operators are adopted to improve the global and local search abilities. Three subpopulations with adaptive crossover and mutation probabilities are constructed to enhance the convergence and diversity of population. A Q-learning-assisted cooperative approach is adopted to realize the information communication among subpopulations in the genetic operations. The Q-learning method is used to intelligently choose parent individuals from three subpopulations by utilizing its self-learning strategies. A variable neighborhood search approach considering problem-knowledge neighborhood structures is devised to refine the excellent individuals in population. Finally, the presented algorithm is compared against three well-known intelligent optimization methods on a collection of instances. Comparison outcomes verify the superiority of the developed algorithm in handling the considered problem.