Designing e-commerce supply chains: a stochastic facility–location approach
e-Commerce activity has been increasing during recent years, and this trend is expected to continue in thenear future. e-Commerce practices are subject to uncertainty conditions and high variability in customers’demands. Considering these characteristics, we propose two facility–location models that...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10459.1/71763 |
| Acceso en línea: | https://doi.org/10.1111/itor.12433 http://hdl.handle.net/10459.1/71763 |
| Access Level: | acceso abierto |
| Palabra clave: | e-commerce Supply-chain management Capacitated facility–location problem Stochastic combinatorial optimization Simheuristics Stochastic programming |
| Sumario: | e-Commerce activity has been increasing during recent years, and this trend is expected to continue in thenear future. e-Commerce practices are subject to uncertainty conditions and high variability in customers’demands. Considering these characteristics, we propose two facility–location models that represent alternativedistribution policies in e-commerce (one based on outsourcing and another based on in-house distribution).These models take into account stochastic demands as well as more than one regular supplier per customer.Two methodologies are then introduced to solve these stochastic versions of the well-known capacitatedfacility–location problem. The first is a two-stage stochastic-programming approach that uses an exact solver.However, we show that this approach is not appropriate for tackle large-scale instances due to the compu-tational effort required. Accordingly, we also introduce a “simheuristic” approach that is able to deal withlarge-scale instances in short computing times. An extensive set of benchmark instances contribute to illustratethe efficiency of our approach, as well as its potential utility in modern e-commerce practices. |
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