Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach
Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of t...
| Autores: | , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Colombia |
| Institución: | Corporación Universidad de la Costa |
| Repositorio: | Repositorio REDICUC |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.cuc.edu.co:11323/9430 |
| Acceso en línea: | https://hdl.handle.net/11323/9430 https://doi.org/10.2478/fcds-2022-0010 https://repositorio.cuc.edu.co/ |
| Access Level: | acceso abierto |
| Palabra clave: | Hub Reinforced epsilon constraint method Multilevel services Queue theory Multi-objective optimization Location-assignment |
| Sumario: | Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer. |
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