Opportunities for reinforcement learning in stochastic dynamic vehicle routing

There has been a paradigm-shift in urban logistic services in the last years; demand for real-time, instant mobility and delivery services grows. This poses new challenges to logistic service providers as the underlying stochastic dynamic vehicle routing problems (SDVRPs) require anticipatory real-t...

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Veröffentlicht in:	Computers & operations research Jg. 150; S. 106071
Hauptverfasser:	Hildebrandt, Florentin D., Thomas, Barrett W., Ulmer, Marlin W.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2023
Schlagworte:	Approximate dynamic programming Combinatorial optimization Mixed integer programming Reinforcement learning Stochastic dynamic vehicle routing Survey Stochastic dynamic vehicle routing Approximate dynamic programming Mixed integer programming Survey Combinatorial optimization Reinforcement learning
ISSN:	0305-0548, 1873-765X
Online-Zugang:	Volltext
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