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Multi-Level Production and Distribution Planning with Transportation Fleet Optimization

Author

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  • Tony J. Van Roy

    (Bank Brussels Lambert, Berkelareveld 7, 3060 Bertem, Belgium)

Abstract

We present an application of mathematical programming for solving a complex multi-level production and distribution network optimization problem faced by a petrochemical company. Two liquified petroleum gas (LPG) products, propane and butane, are produced at refineries, from where they are shipped in bulk to plants for bottling. Then the company distributes the gas bottles to the customers either directly or via depots and transportation breakpoints. Transportation is carried out by the company's own truck fleet or by contract carriers. The own trucks can operate on different shift systems allowing for overtime. The problem is to find the location of the bottling plants and the depots, the production levels at the refineries and the plants, the stock levels at the plants and the depots, the transportation volumes on each link of the distribution network, the number of trucks and drivers, the transportation shift systems and schedules, and the customer assignments so as to meet LPG demand at minimal cost. The actual problem has 2 commodities, 2 refineries, 10 potential bottling plant locations, 40 potential depot locations, 40 breakpoints (transporters), and 200 customer regions. The problem was solved using a matrix generator designed for network-like problems and MPSARX, a state-of-the-art general-purpose mathematical programming software system augmented with advanced automatic reformulation and cut generation features.

Suggested Citation

  • Tony J. Van Roy, 1989. "Multi-Level Production and Distribution Planning with Transportation Fleet Optimization," Management Science, INFORMS, vol. 35(12), pages 1443-1453, December.
    • Handle: RePEc:inm:ormnsc:v:35:y:1989:i:12:p:1443-1453
    DOI: 10.1287/mnsc.35.12.1443
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    Cited by:

    1. Thomas, Douglas J. & Griffin, Paul M., 1996. "Coordinated supply chain management," European Journal of Operational Research, Elsevier, vol. 94(1), pages 1-15, October.
    2. Eskigun, Erdem & Uzsoy, Reha & Preckel, Paul V. & Beaujon, George & Krishnan, Subramanian & Tew, Jeffrey D., 2005. "Outbound supply chain network design with mode selection, lead times and capacitated vehicle distribution centers," European Journal of Operational Research, Elsevier, vol. 165(1), pages 182-206, August.
    3. Ali, Agha Iqbal & O'Connor, Debra J., 2010. "The impact of distribution system characteristics on computational tractability," European Journal of Operational Research, Elsevier, vol. 200(2), pages 323-333, January.
    4. Lejeune, M.A., 2006. "A variable neighborhood decomposition search method for supply chain management planning problems," European Journal of Operational Research, Elsevier, vol. 175(2), pages 959-976, December.
    5. Oxe, Gerd, 1997. "Reducing overcapacity in chemical plants by linear programming," European Journal of Operational Research, Elsevier, vol. 97(2), pages 337-347, March.

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