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OR PRACTICE---The Dance of the Thirty-Ton Trucks: Dispatching and Scheduling in a Dynamic Environment

Author

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  • Martin Durbin

    (Optimization Solutions Group, Decisive Analytics Corporation, Arlington, Virginia 22202)

  • Karla Hoffman

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia 22030)

Abstract

We report on the application of operations research to a very complex scheduling and dispatching problem. Scheduling and dispatching are never easy, but the scheduling of concrete deliveries is particularly difficult for several reasons: (1) concrete is an extremely perishable product---it can solidify in the truck if offloading is delayed by a few hours; (2) customer orders are extremely unpredictable and volatile---orders are often canceled or drastically changed at the last minute; (3) the concrete company overbooks by as much as 20% to compensate for customer unpredictability; (4) many orders require synchronized deliveries by multiple trucks; (5) when a truck arrives at a customer site, the customer may not be ready for the delivery, or a storm may negate the ability to use the concrete; and (6) most of the travel takes place in highly congested urban areas, making travel times highly variable.To assist the dispatchers, schedulers, and order takers at this company, we designed and implemented a decision-support tool consisting of both planning and execution tools. The modules determine whether new orders should be accepted, when drivers should arrive for work, the real-time assignment of drivers to delivery loads, the dispatching of these drivers to customers and back to plants, and the scheduling of the truck loadings at the plants.For the real-time dispatching and order-taking decisions, optimization models are solved to within 1% of optimality every five minutes throughout the day. This nearly continuous reoptimization of the entire system allows quick reactions to changes. The modeling foundation is a time-space network with integer side constraints. We describe each of the models and explain how we handle imperfect data. We also detail how we overcome a variety of implementation issues.The success of this project can be measured, most importantly, by the fact that the tool is being ported by the parent company, Florida Rock, to each of its other ready-mix concrete companies. Second, the corporation is sufficiently convinced of its importance that they have begun promoting this methodology as a “best practice” at the World of Concrete and ConAgg industry conventions.

Suggested Citation

  • Martin Durbin & Karla Hoffman, 2008. "OR PRACTICE---The Dance of the Thirty-Ton Trucks: Dispatching and Scheduling in a Dynamic Environment," Operations Research, INFORMS, vol. 56(1), pages 3-19, February.
    • Handle: RePEc:inm:oropre:v:56:y:2008:i:1:p:3-19
    DOI: 10.1287/opre.1070.0459
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    References listed on IDEAS

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    2. Jose L. Andrade-Pineda & Pedro L. Gonzalez-R & Jose M. Framinan, 2013. "A Decision-Making Tool for a Regional Network of Clinical Laboratories," Interfaces, INFORMS, vol. 43(4), pages 360-372, August.

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