An optimization model for product returns using genetic algorithms and artificial immune system

Ali Diabat, Devika Kannan, Mathiyazhagan Kaliyan, Davor Svetinovic

    Research output: Contribution to journalArticle

    Abstract

    Current environmental issues emerging in the world are reflected in the environmental legislation of several countries. Because environmental issues are important, industries actively seek ways in which to reduce their environmental footprint. One effective method is through the use of reverse logistics. Reverse logistics is the concept of reusing used products in order to reduce wastes and to increase an industry's environmental performance and resulting profits. Stock selection, transportation, centralized collection, data collection, refurbishing, and remanufacturing are some of the more commonly utilized reverse logistic operations. An effective reverse logistics network is essential for increasing the flow of goods from customers to producers. The objective of this paper is to develop a multi-echelon reverse logistics network for product returns to minimize the total reverse logistics cost, which consists of renting, inventory carrying, material handling, setup, and shipping costs. Industries need to give more attention to the task of collecting used products from customers and establishing collection facilities. In this study, a mixed integer non-linear programming (MINLP) model is developed to find out the number and location of initial collection points and centralized return centers required for an effective return and collection system, and also the maximum holding time (collection frequency) for aggregation of small volumes of returned products into large shipments. Two solution approaches, namely genetic algorithm and artificial immune system, are implemented and compared. The usefulness of the proposed model and algorithm are demonstrated via an illustrative example.

    Original languageEnglish (US)
    Pages (from-to)156-169
    Number of pages14
    JournalResources, Conservation and Recycling
    Volume74
    DOIs
    StatePublished - Jan 1 2013

    Fingerprint

    immune system
    genetic algorithm
    logistics
    environmental issue
    industry
    environmental legislation
    shipping
    cost
    footprint
    product
    Optimization model
    Reverse logistics
    Genetic algorithm
    Artificial immune system
    Product returns
    Industry

    Keywords

    • Artificial immune system (AIS)
    • Genetic algorithm (GA)
    • Location-allocation
    • Reverse logistics

    ASJC Scopus subject areas

    • Waste Management and Disposal
    • Economics and Econometrics

    Cite this

    An optimization model for product returns using genetic algorithms and artificial immune system. / Diabat, Ali; Kannan, Devika; Kaliyan, Mathiyazhagan; Svetinovic, Davor.

    In: Resources, Conservation and Recycling, Vol. 74, 01.01.2013, p. 156-169.

    Research output: Contribution to journalArticle

    Diabat, Ali ; Kannan, Devika ; Kaliyan, Mathiyazhagan ; Svetinovic, Davor. / An optimization model for product returns using genetic algorithms and artificial immune system. In: Resources, Conservation and Recycling. 2013 ; Vol. 74. pp. 156-169.
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