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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 posesnew challenges to logistic service providers as the underlying stochasticdynamic vehicle routing problems (SDVRPs) require anticipatory real-timerouting actions. Searching the combinatorial action space for efficient routingactions is by itself a complex task of mixed-integer programming (MIP)well-known by the operations research community. This complexity is nowmultiplied by the challenge of evaluating such actions with respect to theireffectiveness given future dynamism and uncertainty, a potentially ideal casefor reinforcement learning (RL) well-known by the computer science community.For solving SDVRPs, joint work of both communities is needed, but as we show,essentially non-existing. Both communities focus on their individual strengthsleaving potential for improvement. Our survey paper highlights this potentialin research originating from both communities. We point out current obstaclesin SDVRPs and guide towards joint approaches to overcome them.

Where the Action is: Let's make Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problems work!