Computing robust solutions for the stochastic CARP
نویسندگان
چکیده
This paper tackles a new problem called Stochastic Capacitated Arc Routing Problem (SCARP), derived from the CARP by randomizing the demands. It is raised by applications like urban waste collection, in which the amount on each arc is a random variable. In that case, the trips computed by any algorithm based on estimated demands can be deeply affected on the field, due to additional moves to the dumping site when vehicles are filled earlier than expected. The goal of the SCARP is to improve robustness by computing a set of trips with a minimum expected cost. A genetic algorithm is developed for this purpose. A mathematical analysis permits to avoid time-consuming simulations when evaluating chromosomes. The method is benchmarked on the well-known DeArmon instances. The results show it is possible to obtain robust solutions without significant cost enlargements.
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