By Marlin Wolf Ulmer
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Extra info for Approximate Dynamic Programming for Dynamic Vehicle Routing
To allow quantitative decision support for vehicle routing, the presented problems have to be scientifically modeled. For SDVRPs, a classical static modeling is not suitable. The model has to reflect both uncertainty and replanning to allow the application of prescriptive analytics and the development of suitable solution algorithms and flexible plans. 3. , an integration of possible uncertain future events and future decisions in the current planning may avoid myopic, ineffective decisions and maintain flexibility.
Another field of uncertain service times is healthcare. In many cases, the physicians are unaware of the patient’s condition before they arrive. As a result, the required amount of time to spend at the patient’s home significantly differs. 3 Demands In some cases, the volume of customer demands are large and the vehicles’ loading capacities have to be considered in planning. This may be the case in oil distribution or waste collection. An insufficient amount of goods or insufficient free space may force the vehicles to return to a depot for replenishment or unloading.
Planning for the application may be too complex to achieve an a priori plan. , on a rolling horizon). For example, this could be experienced if the travel time changes over the day according to a known variation in the load of the street capacity (Malandraki and Dial 1996). , if the application contains uncertainty. In this case, adaptions of plans or stepwise planning are required to react to the changed circumstances and updated information. Some applications allow the straightforward determination of a (robust) a priori plan with only a few adaptions during the day.