A Genetic Algorithm for Reverse Logistics network design

A case study from the GCC

Ahmed Alshamsi, Ali Diabat

    Research output: Contribution to journalArticle

    Abstract

    Reverse logistics (RL) involves a sequence of operations that initiate at the consumer level and terminate at the manufacturer, opposite to the traditional forward approach of the supply chain. Recycling, reuse, and re-processing of products are activities of RL networks, all of which are becoming increasingly prevalent due to growing environmental and socio-economic concerns. Research has begun to study such networks in an effort to maximize efficiency and to improve operations. Previous work focused on developing a Mixed Integer Linear Programming (MILP) with an aim of determining the optimal location and capacity of important nodes of the RL network, such as inspection centers and remanufacturing facilities. Transportation decisions, such as whether to use in-house or outsourced vehicles, are often based on cost effectiveness. The problem is formulated for the case of a household appliance in the Gulf Cooperation Council (GCC) region. Sixty-eight cities are considered, leading to a very large number of variables and constraints; thus, a heuristic approach, namely a Genetic Algorithm (GA), is chosen to solve the problem. The main contribution of this paper is to develop a very efficient GA capable of solving a large scale problem in short time. The developed GA was capable of solving a very large problem (with 656,885 continuous variables, 2040 binary variables, 10 integer variables, and 100,340 constraints) with a gap of 0.3% and about 38.5 times faster than GAMS using a personal computer. The same GA succeeded to solve both large and small problems to optimality or with a gap that didn't exceed 1.5% and faster than GAMS. The technique that we used to code the GA reduced the number of variables and constraints to 92% and 86%, respectively. Furthermore, the reported results provide important insights on practical aspects of the problem, as well as useful points for the evaluation of the heuristic's performance.

    Original languageEnglish (US)
    Pages (from-to)652-669
    Number of pages18
    JournalJournal of Cleaner Production
    Volume151
    DOIs
    StatePublished - May 10 2017

    Fingerprint

    network design
    genetic algorithm
    Logistics
    logistics
    Genetic algorithms
    heuristics
    Domestic appliances
    environmental economics
    linear programing
    Cost effectiveness
    Personal computers
    Linear programming
    Supply chains
    Recycling
    recycling
    Inspection
    gulf
    co-operation
    Network design
    Reverse logistics

    Keywords

    • Genetic Algorithms
    • Metaheuristics
    • Mixed integer programming
    • Remanufacturing
    • Reverse Logistics

    ASJC Scopus subject areas

    • Renewable Energy, Sustainability and the Environment
    • Environmental Science(all)
    • Strategy and Management
    • Industrial and Manufacturing Engineering

    Cite this

    A Genetic Algorithm for Reverse Logistics network design : A case study from the GCC. / Alshamsi, Ahmed; Diabat, Ali.

    In: Journal of Cleaner Production, Vol. 151, 10.05.2017, p. 652-669.

    Research output: Contribution to journalArticle

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