Anticipation in Dynamic Vehicle Routing

Marlin W. Ulmer ()
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Marlin W. Ulmer: Technische Universität Braunschweig

A chapter in Operations Research Proceedings 2017, 2018, pp 11-16 from Springer

Abstract: Abstract For many routing applications, decision making is conducted under incomplete information. The information is only revealed successively during the execution of the routing. In many cases, dispatchers adapt their decisions dynamically to new information. Nevertheless, to avoid myopic decisions, dispatchers have to anticipate future events in current decision making. In this paper, we propose the use of a Markov decision process (MDP) to model stochastic dynamic vehicle routing problems (SDVRPs). For the integration of stochasticity in dynamic decision making, we present novel methods of approximate dynamic programming (ADP). These methods are extensions and combinations of general ADP-methods and are tailored to match the characteristics of SDVRPs. A comparison with conventional state-of-the-art benchmark heuristics for a SDVRP with stochastic customer requests proves the ADP-methods to be highly advantageous.

Keywords: Stochastic dynamic vehicle routing; Approximate dynamic programming; Value function approximation; Rollout algorithms (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-89920-6_2

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DOI: 10.1007/978-3-319-89920-6_2

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