This paper addresses the customer order scheduling problem in parallel production environment commonly appearing in the pharmaceutical and paper industries. The problem aims to minimize the total completion time of the orders with their jobs processed on dedicated machines in parallel. To deal with the computational challenge of large-scale problems, we propose a learning-based two-stage optimization method consisting of a learned dispatching rule in the first stage and an adaptive local search in the second stage. The new dispatching rules are automatically generated by the proposed feature-enhanced genetic programming method in an off-line learning manner. Based on the high-quality initial solutions provided by the learned dispatching rule, we develop an adaptive local search to further improve the solution quality. Numerical results indicate the superiority of the learned dispatching rule and show the proposed two-stage optimization method significantly outperforms state-of-the-art methods in the literature.

本文解决了制药和造纸行业常见的并行生产环境中的客户订单调度问题。该问题旨在最小化在专用机器上并行处理订单的总完成时间。为了应对大规模问题的计算挑战，我们提出了一种基于学习的两阶段优化方法，包括第一阶段的学习调度规则和第二阶段的自适应局部搜索。新的调度规则是由所提出的特征增强遗传编程方法以离线学习的方式自动生成的。基于学习的调度规则提供的高质量初始解决方案，我们开发了自适应局部搜索以进一步提高解决方案质量。