Dynamic lookahead policies for stochastic-dynamic inventory routing in bike sharing systems

DOI10.1016/j.cor.2018.06.004zbMath1458.90011OpenAlexW2808244097MaRDI QIDQ1734854

Marlin W. Ulmer, Jan Brinkmann, Dirk Christian Mattfeld

Publication date: 27 March 2019

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cor.2018.06.004

zbMATH Keywords

value function approximationapproximate dynamic programmingbike sharingdynamic vehicle routinglookahead policiespolicy search

Mathematics Subject Classification ID

Programming involving graphs or networks (90C35) Stochastic programming (90C15) Transportation, logistics and supply chain management (90B06) Inventory, storage, reservoirs (90B05) Dynamic programming (90C39)

Related Items (16)

Minimizing Multimodular Functions and Allocating Capacity in Bike-Sharing Systems ⋮ Estimation of the arrival time of deliveries by occasional drivers in a crowd-shipping setting ⋮ Cyclic inventory routing with dynamic safety stocks under recurring non-stationary interdependent demands ⋮ Dynamic service area sizing in urban delivery ⋮ Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands ⋮ Factors affecting the final solution of the bike-sharing rebalancing problem under heuristic algorithms ⋮ Public transport for smart cities: recent innovations and future challenges ⋮ A sampling-based matheuristic for the continuous-time stochastic inventory routing problem with time-windows ⋮ Dynamic priority rules for combining on-demand passenger transportation and transportation of goods ⋮ Dynamic Adaptive Large Neighborhood Search for Inventory Routing Problem ⋮ Risk‐averse two‐stage stochastic programming for the inventory rebalancing of bike‐sharing systems ⋮ Same-day delivery with pickup stations and autonomous vehicles ⋮ Cross docking for libraries with a depot ⋮ Stochastic dynamic vehicle routing in the light of prescriptive analytics: a review ⋮ A shortfall modelling-based solution approach for stochastic cyclic inventory routing ⋮ Opportunities for reinforcement learning in stochastic dynamic vehicle routing

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