This paper examines flexible job shop scheduling problems with multiple resource constraints. A unified solution framework is presented for modelling various types of non-renewable, renewable and cumulative resources, such as limited capacity machine buffers, tools, utilities and work in progress buffers. We propose a Constraint Programming (CP) model and a CP-based Adaptive Large Neighbourhood Search (ALNS-CP) algorithm. The ALNS-CP uses long-term memory structures to store information about the assignment to machines of both individual operations and pairs of operations, as encountered in high-quality and diverse solutions during the search process. This information is used to create additional constraints for the CP solver, which guide the search towards promising regions of the solution space. Numerous experiments are conducted on well-known benchmark sets to assess the performance of ALNS-CP against the current state-of-the-art. Additional experiments are conducted on new instances of various sizes to study the impact of different resource types on the makespan. The computational results show that the proposed solution framework is highly competitive, while it was able to produce 39 new best solutions on well-known problem instances of the literature.

中文翻译：

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针对具有多个资源约束的灵活作业车间调度问题的统一解决方案框架


本文研究了具有多个资源约束的灵活作业车间调度问题。提出了一个统一的解决方案框架，用于对各种类型的不可再生、可再生和累积资源进行建模，例如有限容量的机器缓冲区、工具、实用程序和正在进行的缓冲区。我们提出了一种约束规划 （CP） 模型和一种基于 CP 的自适应大邻域搜索 （ALNS-CP） 算法。ALNS-CP 使用长期记忆结构来存储有关单个操作和成对操作分配给机器的信息，就像在搜索过程中的高质量和多样化解决方案中遇到的那样。此信息用于为 CP 求解器创建其他约束，从而引导搜索到解决方案空间的有前途的区域。在众所周知的基准集上进行了大量实验，以评估 ALNS-CP 与当前最先进的技术相比的性能。对各种大小的新实例进行其他实验，以研究不同资源类型对 makespan 的影响。计算结果表明，所提出的解决方案框架具有很强的竞争力，同时它能够在文献中众所周知的问题实例上产生 39 个新的最佳解决方案。