A competitive (dual) simplex method for the assignment problem
Mathematical Programming: Series A and B
Stochastic decomposition: an algorithm for two-state linear programs with recourse
Mathematics of Operations Research
Network programming
Monotone structure in discrete-event systems
Monotone structure in discrete-event systems
Scheduling Parallel Machines On-line
SIAM Journal on Computing
Journal of Optimization Theory and Applications
Neuro-Dynamic Programming
Rollout Algorithms for Combinatorial Optimization
Journal of Heuristics
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
Adaptive Labeling Algorithms for the Dynamic Assignment Problem
Transportation Science
A Rollout Policy for the Vehicle Routing Problem with Stochastic Demands
Operations Research
Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching
Transportation Science
Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem
Transportation Science
The optimizing-simulator: An illustration using the military airlift problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimal matching between spatial datasets under capacity constraints
ACM Transactions on Database Systems (TODS)
Continuous spatial assignment of moving users
The VLDB Journal — The International Journal on Very Large Data Bases
Dynamic Vehicle Routing with Priority Classes of Stochastic Demands
SIAM Journal on Control and Optimization
Expert Systems with Applications: An International Journal
A Hybrid Tabu Search and Constraint Programming Algorithm for the Dynamic Dial-a-Ride Problem
INFORMS Journal on Computing
A novel price prediction scheme of grid resources based on time series analysis
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
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There has been considerable recent interest in the dynamic vehicle routing problem, but the complexities of this problem class have generally restricted research to myopic models. In this paper, we address the simpler dynamic assignment problem, where a resource (container, vehicle, or driver) can serve only one task at a time. We propose a very general class of dynamic assignment models, and propose an adaptive, nonmyopic algorithm that involves iteratively solving sequences of assignment problems no larger than what would be required of a myopic model. We consider problems where the attribute space of future resources and tasks is small enough to be enumerated, and propose a hierarchical aggregation strategy for problems where the attribute spaces are too large to be enumerated. Finally, we use the formulation to also test the value of advance information, which offers a more realistic estimate over studies that use purely myopic models.