Titlebook: Approximate Dynamic Programming for Dynamic Vehicle Routing; Marlin Wolf Ulmer Book 2017 Springer International Publishing AG 2017 Operati

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Martin Kotula,Christian Michalak We define the MDP in Sect. .. The MDP contains three (sub-)models: decision states, dynamic decision making, and stochastic transitions. To model routing applications as MDP, we have to determine the three (sub-)models. In Sect. ., we model replanning as .. In Sect. ., we model a . for a vehicle ro

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Siegfried Gänßlen,Jürgen Dierolf,Udo Krauson is to incorporate a predictive lookahead model of future stochasticity and decision making in the solution approach. Further, we present a classification for approaches based on their degree of anticipation.

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Nachhaltigkeitsberichterstattungain reason for replanning is given by uncertain customer requests. Therefore, we consider a .. After a short motivation, we show how the problem is modeled, present a variety of solution methods with different degree of anticipation, and compare these methods in an extensive computational study.

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1387-666X om the real-world application. The author describes a SDVRP with stochastic customer requests often addressed in the literature,  and then shows in detail how this problem can be modeled as a Markov decision pr978-3-319-85681-0978-3-319-55511-9Series ISSN 1387-666X Series E-ISSN 2698-5489

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