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    Resolving forward-reverse logistics multi-period model using evolutionary algorithms

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    Authors
    Kumar, Varun
    Kumar, Vikas
    Brady, Malcolm
    Garza-Reyes, Jose Arturo cc
    Affiliation
    University of Derby
    Issue Date
    2016-05
    
    Metadata
    Show full item record
    Abstract
    In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/ near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management.
    Citation
    Kumar, V.N.S.A., Kumar, V., Brady, M., Garza-Reyes, J.A., Simpson, M. (2016), “Resolving Forward-Reverse Logistics Multi-Period Model Using Evolutionary Algorithms”, International Journal of Production Economics, 183, Part B, pp. 458-469. DOI: 10.1016/j.ijpe.2016.04.026
    Publisher
    Elsevier
    Journal
    International Journal of Production Economics
    URI
    http://hdl.handle.net/10545/610581
    DOI
    10.1016/j.ijpe.2016.04.026
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0925527316300688
    http://www.derby.ac.uk/staff/jose-arturo-garza-reyes/
    http://www.joseagarzareyes.com/
    http://scholar.google.co.uk/citations?user=lwS0V6wAAAAJ&hl=en
    https://www.linkedin.com/in/dr-jose-arturo-garza-reyes-42225323
    Type
    Article
    Language
    en
    ISSN
    09255273
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ijpe.2016.04.026
    Scopus Count
    Collections
    Centre for Supply Chain Improvement

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