• DocumentCode
    2852735
  • Title

    Multi-heuristics based Genetic Algorithm for solving maritime inventory routing problem

  • Author

    Siswanto, Nurhadi ; Essam, Daryl ; Sarker, Ruhul

  • Author_Institution
    Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia
  • fYear
    2011
  • fDate
    6-9 Dec. 2011
  • Firstpage
    116
  • Lastpage
    120
  • Abstract
    This paper discusses a multi-heuristics based Genetic Algorithm (GA) to solve maritime inventory routing problems (mIRP). The problem is to transport multiple oil products from a production facility to some consumption ports by using several heterogeneous ships with undedicated compartments. The objective of the problem is to find a minimum cost solution, while satisfying a number of technical and physical constraints, within a given planning horizon. The strategies to assign a ship are transformed to a set of heuristic combinations represented by a chromosome. At every iteration a number of chromosomes are evaluated and evolved within a GA framework. The approach has been applied on several test cases. The multi-heuristic results show that the best optimum values of the case problems are not different from the ones from a MILP method solved using Lingo, but they do so with a significant decrease in computation time.
  • Keywords
    genetic algorithms; goods distribution; inventory management; ships; strategic planning; Lingo; MILP method; heterogeneous ships; maritime inventory routing problem; multiheuristics based genetic algorithm; oil products transport; production facility; Biological cells; Genetic algorithms; Loading; Marine vehicles; Mathematical model; Planning; Routing; Heuristics; inventory routing; scheduling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
  • Conference_Location
    Singapore
  • ISSN
    2157-3611
  • Print_ISBN
    978-1-4577-0740-7
  • Electronic_ISBN
    2157-3611
  • Type

    conf

  • DOI
    10.1109/IEEM.2011.6117890
  • Filename
    6117890