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    A combined tactical and operational deterministic food grain transportation model: particle swarm based optimization approach

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    Authors
    Maiyar, Lohithaksha M.
    Thakkar, Jitesh J.
    Affiliation
    Indian Institute of Technology Kharagpur
    Issue Date
    2017-05-22
    Subjects
    particle swarm optimisation
    
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    Abstract
    This paper proposes a combined tactical and operational two stage food grain transportation model with linear formulation in the first stage and a mixed-integer non-linear problem (MINLP) in the second stage taking the case of India. Transportation cost is minimized in both stages to fulfil a deterministic demand. First and the second stages correspond to the movement of food grains in between state and central level warehouses respectively. A novel k-parameter based method of constraint handling has been proposed. Further, the two stage MINLP formulation newly incorporates vehicle capacity constraints and proposes a generic metric for measuring vehicle utilization. First stage is solved by CPLEX and for the second stage, two population based random search techniques: Particle swarm optimization-composite particle (PSOCP) and PSO, have been employed. Experimentations on 10 different problem sets reveal that PSOCP performs marginally better than PSO with lesser standard deviation of global fitness and better solution quality with slightly higher CPU time. Later, sensitivity analysis is conducted on all ten problem sets and a decision support framework is proposed to assist potential stakeholders.
    Citation
    Maiyar LM, Thakkar JJ (2017) 'A combined tactical and operational deterministic food grain transportation model: particle swarm based optimization approach', Computers and Industrial Engineering, 110, pp.30-42.
    Publisher
    Elsevier
    Journal
    Computers and Industrial Engineering
    URI
    http://hdl.handle.net/10547/624668
    DOI
    10.1016/j.cie.2017.05.023
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0360835217302279
    Type
    Article
    Language
    en
    ISSN
    0360-8352
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.cie.2017.05.023
    Scopus Count
    Collections
    Business and management

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