The competition among companies required that the companies have to strengthen their supply chain managements. Consignment Stock (CS) represents an interesting strategy to stock monitoring and control for both the buyer and the supplier, and it has been progressively considered and introduced in several companies. CS has been previously analyzed for single vendor single buyer case (1986). The main aim of this paper is to find out the most desirable values of various variables, involved in different CSmodels that lead to incur the minimum cost of supply chain for vendor as well as buyer. The analytical model for single vendor multi buyer CS policy has been analyzed out of four types of models i.e. basic CS model, CS with delay, CS with delay with information sharing; CS with crashing lead-time. The Joint Total Economic cost of each model is optimized. Analytical model is solved with enumeration technique up to five buyers, solving analytical model for multiple buyer with complete enumeration becomes computationally expensive. To overcome this problem Particle Swarm Algorithm (PSA) is proposed for finding optimum for the case of more than five buyers. PSA model is developed and can solve more than seven buyers. So PSA is used for the optimization of the above four models. A generalized C program has been written to implement the above problem using Particle Swarm Algorithm (PSA).


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    Titel :

    AN APPROACH OF SWARM INTELLIGENCE FOR VARIOUS CONSIGNMENT STOCK MODELS


    Beteiligte:

    Erscheinungsdatum :

    2016-03-30


    Anmerkungen:

    IJITR; Vol 4, No 2 (2016): February - March 2016; 2864-2870


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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