Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Dynamic shortest paths in acyclic networks with Markovian arc costs
Operations Research
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Least Expected Time Paths in Stochastic, Time-Varying Transportation Networks
Transportation Science
Waiting Strategies for Dynamic Vehicle Routing
Transportation Science
Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching
Transportation Science
New policies for the dynamic traveling salesman problem
Optimization Methods & Software
Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem
Transportation Science
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
A Preliminary Study on Anticipatory Stigmergy for Traffic Management
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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Mobile communication technologies enable communication between dispatchers and drivers and hence can enable fleet management based on real-time information. We assume that such communication capability exists for a single pickup and delivery vehicle and that we know the likelihood, as a function of time, that each of the vehicle's potential customers will make a pickup request. We then model and analyze the problem of constructing a minimum expected total cost route from an origin to a destination that anticipates and then responds to service requests, if they occur, while the vehicle is en route. We model this problem as a Markov decision process and present several structured results associated with the optimal expected cost-to-go function and an optimal policy for route construction. We illustrate the behavior of an optimal policy with several numerical examples and demonstrate the superiority of an optimal anticipatory policy, relative to a route design approach that reflects the reactive nature of current routing procedures for less-than-truckload pickup and delivery.