As advancements in transportation equipment intelligence continue, the job shop scheduling problem integrating finite transportation resources (JSPIFTR) has attracted considerable attention. Within the domain of shop scheduling, the neighborhood structure serves as a cornerstone for enabling intelligent optimization algorithms to effectively navigate and discover optimal solutions. However, current algorithms for JSPIFTR rely on generalized neighborhood structures, which incorporate operators like insertion and swap. While these structures are tailored to the encoding vectors, their utilization often leads to suboptimal optimization efficacy. To address this limitation, this paper introduces novel neighborhood structures specifically designed to the distinctive properties of JSPIFTR. These innovative structures leverage the intrinsic structural information in integrated scheduling, thereby enhancing the optimization effectiveness of the algorithm. Firstly, two theorems are presented to demonstrate the feasibility of the neighborhood solution. Secondly, different neighborhood structures for critical transportation and processing tasks are subsequently designed based on the analysis of the problem properties and constraints. Thirdly, an efficient fast evaluation method is developed to expediently calculate the objective value of the neighborhood solution. Finally, the novel neighborhood structures are combined with the tabu search (TS_NNS) and compared with other state-of-the-art methods on EX and NEX benchmarks. The comparative results demonstrate the remarkable performance of the neighborhood structure, with the TS_NNS enhancing the best solutions across 23 instances.

中文翻译：

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基于新颖邻域结构的禁忌搜索求解集成有限运输资源的车间调度问题


随着运输设备智能化的不断进步，整合有限运输资源的作业车间调度问题（JSPIFTR）引起了人们的广泛关注。在车间调度领域，邻域结构是智能优化算法有效导航和发现最佳解决方案的基石。然而，当前的 JSPIFTR 算法依赖于广义邻域结构，其中包含插入和交换等运算符​​。虽然这些结构是针对编码向量定制的，但它们的使用通常会导致优化效果不佳。为了解决这一限制，本文引入了专门针对 JSPIFTR 的独特属性而设计的新型邻域结构。这些创新结构利用了集成调度中的内在结构信息，从而增强了算法的优化有效性。首先，提出两个定理来证明邻域解的可行性。其次，根据问题属性和约束的分析，随后设计用于关键运输和加工任务的不同邻域结构。第三，开发了一种高效的快速评估方法，可以方便地计算邻域解的目标值。最后，将新颖的邻域结构与禁忌搜索（TS_NNS）相结合，并在 EX 和 NEX 基准上与其他最先进的方法进行比较。比较结果证明了邻域结构的卓越性能，TS_NNS 增强了 23 个实例的最佳解决方案。