Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
Diversion Issues in Real-Time Vehicle Dispatching
Transportation Science
Dynamic Column Generation for Dynamic Vehicle Routing with Time Windows
Transportation Science
Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching
Transportation Science
Adaptive granular local search heuristic for a dynamic vehicle routing problem
Computers and Operations Research
The degree of dynamism for workforce scheduling problem with stochastic task duration
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Waiting and relocation strategies in online stochastic vehicle routing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
On the partitioning of dynamic workforce scheduling problems
Journal of Scheduling
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Manufacturing & Service Operations Management
Look-ahead heuristics for the dynamic traveling purchaser problem
Computers and Operations Research
An improved LNS algorithm for real-time vehicle routing problem with time windows
Computers and Operations Research
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In this paper we examine the traveling saleman problem with time windows for various degrees of dynamism. In contrast to the static problem, where the dispatcher can plan ahead, in the dynamic version, part or all of the necessary information becomes available only during the day of operation. We seek to minimize lateness and examine the impact of this criterion choice on the distance traveled. Our focus on lateness is motivated by the problem faced by overnight mail service providers. We propose a real-time solution method that requires the vehicle, when idle, to wait at the current customer location until it can service another customer without being early. In addition, we develop several enhanced versions of this method that may reposition the vehicle at a location different from that of the current customer based on a priori information on future requests. The results we obtained on both randomly generated data and on a real-world case study indicate that all policies proved capable of significantly reducing lateness. Our results also show that this can be accomplished with only small distance increases. The basic policy outperformed the other methods primarily when lateness and distance were equally minimized and proved very robust in all environments studied. When only lateness was considered, the policy to reposition the vehicle at a location near the current customer generally provided the largest reductions in average lateness and the number of late customers. It also produced the least extra distance to be traveled among the relocation policies.