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Anticipation versus reactive reoptimization for dynamic vehicle routing with stochastic requests
Author(s) -
Ulmer Marlin W.
Publication year - 2019
Publication title -
networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.977
H-Index - 64
eISSN - 1097-0037
pISSN - 0028-3045
DOI - 10.1002/net.21861
Subject(s) - anticipation (artificial intelligence) , computer science , routing (electronic design automation) , vehicle routing problem , dimension (graph theory) , dynamism , mathematical optimization , operations research , stochastic optimization , computer network , artificial intelligence , mathematics , physics , quantum mechanics , pure mathematics
Due to new business models and technological advances, dynamic vehicle routing is gaining increasing interest. Especially solving dynamic vehicle routing problems with stochastic customer requests becomes increasingly important, for example, in e‐commerce and same‐day delivery. Solving these problems is challenging, because it requires optimization along two dimensions. First, as a reaction to new customer requests, current routing plans need to be reoptimized. Second, potential future requests need to be anticipated in current decision making. Decisions need to be derived in real‐time. The limited time often prohibits extensive optimization in both dimensions and the question arises how to utilize the limited calculation time effectively. In this paper, we analyze the merits of reactive route reoptimization and anticipation for a dynamic vehicle routing problem with stochastic requests. To this end, we compare an existing method from each dimension as well a policy allowing for a tunable combination of the two approaches. We show how the appropriate optimization combination is strongly connected to the degree of dynamism, the percentage of unknown requests. We also show that our combination does not provide significant benefit compared to the respectively best optimization dimension.