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A hybrid genetic algorithm model for transshipment management decisions

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  • Sharma, Dinesh K.
  • Jana, R.K.

Abstract

Transshipment is a critical area of supply chain management that may lead to cost reductions and improved services for companies to make greater profits and to become more competitive. In this study, we present a transshipment planning model for the petroleum refinery industry. The main objective of the model is to minimize the total transshipment cost, maximize production, satisfy storage requirements at depots and meet the demand for oil in these sales areas. To accommodate imprecision, the goals are defined in a fuzzy sense and a fuzzy goal programming (FGP) model is developed. To provide flexibility to the decision-maker, we integrate a genetic algorithm (GA) within the FGP framework in such a way that it can find solutions for different sets of target and tolerance values of the goals in a single run. A case example is presented to demonstrate the usefulness of the integrated technique.

Suggested Citation

  • Sharma, Dinesh K. & Jana, R.K., 2009. "A hybrid genetic algorithm model for transshipment management decisions," International Journal of Production Economics, Elsevier, vol. 122(2), pages 703-713, December.
    • Handle: RePEc:eee:proeco:v:122:y:2009:i:2:p:703-713
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    References listed on IDEAS

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    1. Liu, Shiang-Tai & Kao, Chiang, 2004. "Solving fuzzy transportation problems based on extension principle," European Journal of Operational Research, Elsevier, vol. 153(3), pages 661-674, March.
    2. Yaghoobi, M.A. & Tamiz, M., 2007. "A method for solving fuzzy goal programming problems based on MINMAX approach," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1580-1590, March.
    3. Chen, Liang-Hsuan & Tsai, Feng-Chou, 2001. "Fuzzy goal programming with different importance and priorities," European Journal of Operational Research, Elsevier, vol. 133(3), pages 548-556, September.
    4. Tamiz, Mehrdad & Jones, Dylan & Romero, Carlos, 1998. "Goal programming for decision making: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 111(3), pages 569-581, December.
    5. Abd El-Wahed, Waiel F. & Lee, Sang M., 2006. "Interactive fuzzy goal programming for multi-objective transportation problems," Omega, Elsevier, vol. 34(2), pages 158-166, April.
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    1. Jana, R.K. & Mitra, Subrata K. & Sharma, Dinesh K., 2018. "Software vendors travel management decisions using an elitist nonhomogeneous genetic algorithm," International Journal of Production Economics, Elsevier, vol. 202(C), pages 123-131.
    2. Li, Feng & Yang, Dong & Wang, Shuaian & Weng, Jinxian, 2019. "Ship routing and scheduling problem for steel plants cluster alongside the Yangtze River," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 198-210.
    3. V M Miori, 2011. "A multiple objective goal programming approach to the truckload routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(8), pages 1524-1532, August.

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