Real-Time Dispatching of Guided and Unguided Automobile Service Units with Soft Time Windows
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A reactive method for real time dynamic vehicle routing problem
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Online decision making and automatic decision model adaptation
Computers and Operations Research
ASAP: The After-Salesman Problem
Manufacturing & Service Operations Management
Algorithm engineering: bridging the gap between algorithm theory and practice
Algorithm engineering: bridging the gap between algorithm theory and practice
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Under high load, the automated dispatching of service vehicles for the German Automobile Association (ADAC) must reoptimize a dispatch for 100-150 vehicles and 400 requests in about 10s to near optimality. In the presence of service contractors, this can be achieved by the column generation algorithm ZIBDIP. In metropolitan areas, however, service contractors cannot be dispatched automatically because they may decline. The problem: a model without contractors yields larger optimality gaps within 10s. One way out are simplified reoptimization models. These compute a short-term dispatch containing only some of the requests: unknown future requests will influence future service anyway. The simpler the models the better the gaps, but also the larger the model error. What is more significant: reoptimization gap or reoptimization model error? We answer this question in simulations on real-world ADAC data: only the new models ShadowPrice and ZIBDIPdummy can keep up with ZIBDIP.