Two-Stage Stochastic Scheduling of a Multiproduct Pipeline System using Similarity Index Decomposition
[EN] Multiproduct pipelines are crucial for delivering substantial quantities of refined oil products from major supply centers to clients within a nearby geographical area. Despite the significant infrastructure investment, the associated transportation costs are markedly lower than those incurred...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/222485 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/222485 |
| Access Level: | acceso abierto |
| Palabra clave: | Decomposition MILP Uncertainty PlanningOil & Gas 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación |
| Sumario: | [EN] Multiproduct pipelines are crucial for delivering substantial quantities of refined oil products from major supply centers to clients within a nearby geographical area. Despite the significant infrastructure investment, the associated transportation costs are markedly lower than those incurred with traditional delivery trucks. However, the scheduling of these systems presents a formidable challenge, requiring meticulous planning of pumping runs well in advance to meet the anticipated demands of clients. In this work, we enhance an existing literature model of a multiproduct pipeline system by introducing uncertainty in the customer demand. The problem is then addressed via a two-stage stochastic formulation. The typical drawback with stochastic formulations is the high computational burden required. To address this challenge, we adapt the so-called Similarity Index decomposition, resulting in a 28-fold improvement in CPU time while achieving equivalent solutions compared to solving the full-space problem. |
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