Online decision making and automatic decision model adaptation

The paper investigates an online version of the vehicle routing problem with time windows, in which additionally arriving requests cause the revision of so far followed routes and schedules. An extended online optimization framework is proposed, which automatically adapts to problem variations and enables the explicit consideration of up-to-date knowledge about the current performance of the controlled system. Actually, we use the mean punctuality observed in the transportation system to adjust the objective function utilized for solving the next decision problem instance. The search is guided toward least cost solutions coming along with high punctuality. We prove the applicability of this approach within comprehensive numerical experiments.

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