Highlights VRPs in distribution centers with cross-docking are more complex than the traditional ones. This paper addresses the VRP of distribution centers with multiple cross-docks for processing multiple products. The formulated model is solved by using PSO with a Self-Learning strategy. The results obtained by SLPSO are compared with a GA based approach.

    Abstract Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks


    Beteiligte:


    Erscheinungsdatum :

    2016-04-12


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Multi-products Location-Routing Problem with Pickup and Delivery

    Rahmani, Younes / Oulamara, Ammar / Cherif, Wahiba Ramdane | IEEE | 2013


    Car handling on ore docks

    Cole, Charles E. | Engineering Index Backfile | 1917


    Handling appliances at railway docks

    Engineering Index Backfile | 1936


    Multiple Object Tracking using Particle Swarm Optimization

    Chen-Chien Hsu / Guo-Tang Dai | BASE | 2012

    Freier Zugriff