The yard crane scheduling problem (YCSP) consists of optimizing container loading for storage and retrieval requests from yard cranes at port terminals. This paper studies a realistic generalization of the YCSP that incorporates the assignments of input/output (I/O) points during the optimization stage. I/O points serve as buffers between the different transportation modes in the port terminal. These are limited in number and unproductive idle times might result if a container schedule exhausts I/O point availability. The resulting problem entails not only scheduling container storage and retrieval requests, but also the assignment of the I/O points. We introduce a series of simple, yet powerful, Iterated Greedy (IG) methods. These include variations of the destruction and reconstruction operators, coordination with novel local search procedures and problem-specific knowledge speed-ups. The proposed IG methods are carefully calibrated and evaluated using comprehensive computational experiments. The results indicate that small changes in the features of the algorithm have a profound impact on performance. Comparisons against the state-of-the-art approaches for this particular problem result in a strong, and statistically significant performance advantage for the proposed IG procedures.

中文翻译：

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迭代了具有输入/输出分配的堆场起重机调度问题的贪婪

堆场起重机调度问题 （YCSP） 包括优化集装箱装载，以满足港口码头堆场起重机的存储和检索请求。本文研究了 YCSP 的现实泛化，它结合了优化阶段输入/输出 （I/O） 点的分配。I/O 点用作港口码头中不同运输方式之间的缓冲区。这些 I/O 点的数量是有限的，如果容器计划耗尽了 I/O 点可用性，则可能会导致非生产性空闲时间。由此产生的问题不仅涉及调度容器存储和检索请求，还涉及 I/O 点的分配。我们介绍了一系列简单但功能强大的迭代贪婪 （IG） 方法。其中包括破坏和重建运算符的变体、与新颖的本地搜索程序的协调以及特定问题的知识加速。所提出的 IG 方法使用全面的计算实验进行了仔细校准和评估。结果表明，算法特征的微小变化会对性能产生深远的影响。与针对此特定问题的最新方法进行比较，为拟议的 IG 程序提供了强大且具有统计学意义的性能优势。