Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
Diversion Issues in Real-Time Vehicle Dispatching
Transportation Science
Transportation Science
Territory Planning and Vehicle Dispatching with Driver Learning
Transportation Science
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
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
The dynamic multi-period vehicle routing problem
Computers and Operations Research
On the partitioning of dynamic workforce scheduling problems
Journal of Scheduling
Anticipatory routing of police helicopters
Expert Systems with Applications: An International Journal
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Many real-world vehicle routing problems are dynamic optimization problems, with customer requests arriving over time, requiring a repeated reoptimization. In this paper, we consider a dynamic vehicle routing problem where one additional customer arrives at a beforehand unknown location when the vehicles are already under way. Our objective is to maximize the probability that the additional customer can be integrated into one of the otherwise fixed tours without violating time constraints. This is achieved by letting the vehicles wait at suitable locations during their tours, thus influencing the position of the vehicles at the time when the new customer arrives. For the cases of one and two vehicles, we derive theoretical results about the best waiting strategies. The general problem is shown to be NP-complete. Several deterministic waiting strategies and an evolutionary algorithm to optimize the waiting strategy are proposed and compared empirically. It is demonstrated that a proper waiting strategy can significantly increase the probability of being able to service the additional customer, at the same time reducing the average detour to serve that customer.