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Modelo real de planificación y rutas bi-objetivo: equilibrio entre costes y preferencias de clientes

  • Amaya Martínez Puras [1] ; Joaquín A. Pacheco Bonrostro [1]
    1. [1] Universidad de Burgos

      Universidad de Burgos

      Burgos, España

  • Localización: Rect@: Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA, ISSN-e 1575-605X, Vol. 17, Nº. 1, 2016, págs. 57-80
  • Idioma: español
  • Enlaces
  • Resumen
    • español

      Un modelo bi-objetivo para el diseño de rutas diarias de una empresa a lo largo de un período de planificación es analizado. Este modelo viene motivado por un problema real de diseño de las rutas de una empresa de Análisis Químicos a lo largo de un horizonte de planificación y la asignación de los calendarios de visita a sus clientes. Los dos objetivos bajo consideración son: minimizar el coste del transporte y la reducción de las modificaciones sobre los calendarios actuales de los clientes. Para su resolución, se ha desarrollado una metodología ad hoc basada en búsqueda tabú en el contexto del PVRP (Periodic Vehicle Routing Problem). El método de solución fue desarrollado por aplicación de búsqueda tabú combinada con la estrategia MOAMP (MultiObjective Adaptative Memory Procedure) y los resultados son comparados con una implementación de NSGA-II (Non-dominated Sorting Genetic Algorithm), una reconocida buena estrategia de optimización multi-objetivo.

    • English

      A bi-objective model for the design of daily routes of a company over a planning period is analyzed. This model is motivated by a real design problem routes Chemical Analysis Company over a planning horizon and allocation schedules visit to its customers. The two objectives under consideration are: minimizing transport costs and reducing modifications on current customer schedules. For resolution, it has developed an ad hoc methodology based on tabu search in the context of PVRP (Periodic Vehicle Routing Problem). The solution method was developed by application of combined tabu search with MOAMP (Multiobjective Adaptive Memory Procedure) strategy and the results are compared with an implementation of NSGA-II (Non-dominated Sorting Genetic Algorithm), a well-known approach to multi-objective optimization.

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