Combining variable neighborhood search with simulation for the inventory routing problem with stochastic demands and stock-outs
Vendor managed inventory aims at reducing supply chain costs by centralizing inventory management and vehicle routing decisions. This integrated supply chain approach results in a complex combinatorial optimization problem known as the inventory routing problem (IRP). This paper presents a variable...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:294240 |
| Acceso en línea: | https://ddd.uab.cat/record/294240 https://dx.doi.org/urn:doi:10.1016/j.cie.2018.06.036 |
| Access Level: | acceso abierto |
| Palabra clave: | Inventory routing Metaheuristics Simheuristics Stochastic demands Variable neighborhood search |
| Sumario: | Vendor managed inventory aims at reducing supply chain costs by centralizing inventory management and vehicle routing decisions. This integrated supply chain approach results in a complex combinatorial optimization problem known as the inventory routing problem (IRP). This paper presents a variable neighborhood search metaheuristic hybridized with simulation to solve the IRP under demand uncertainty. Our simheuristic approach is able to solve large sized instances for the single period IRP with stochastic demands and stock-outs in very short computing times. A range of experiments underline the algorithm's competitiveness compared to previously used heuristic approaches. The results are analyzed in order to provide closer managerial insights. |
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