Iterated greedy for the yard crane scheduling problem with input/output assignment
[EN] 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 optimizati...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:riunet.upv.es:10251/232642 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/232642 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Iterated Greedy Yard crane scheduling problem Heuristics Input/output assignment Terminal planning |
| Resumo: | [EN] 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. |
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