Dynamic Programming Treatment of the Travelling Salesman Problem
Journal of the ACM (JACM)
The vehicle routing problem
Metaheuristics for the capacitated VRP
The vehicle routing problem
A Metaheuristic for the Pickup and Delivery Problem with Time Windows
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
Exact algorithms for NP-hard problems: a survey
Combinatorial optimization - Eureka, you shrink!
The harpy speech recognition system.
The harpy speech recognition system.
The Granular Tabu Search and Its Application to the Vehicle-Routing Problem
INFORMS Journal on Computing
A general heuristic for vehicle routing problems
Computers and Operations Research
A tabu search approach for the livestock collection problem
Computers and Operations Research
Vehicle Scheduling and Routing with Drivers' Working Hours
Transportation Science
European Driver Rules in Vehicle Routing with Time Windows
Transportation Science
Vehicle routing under time-dependent travel times: The impact of congestion avoidance
Computers and Operations Research
Vehicle routing under time-dependent travel times: The impact of congestion avoidance
Computers and Operations Research
On an exact method for the constrained shortest path problem
Computers and Operations Research
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Most successful solution methods for solving large vehicle routing and scheduling problems are based on local search. These approaches are designed and optimized for specific types of vehicle routing problems (VRPs). VRPs appearing in practice typically accommodate restrictions that are not accommodated in classical VRP models, such as time-dependent travel times and driving hours regulations. We present a new construction framework for solving VRPs that can handle a wide range of different types of VRPs. In addition, this framework accommodates various restrictions that are not considered in classical vehicle routing models, but that regularly appear in practice. Within this framework, restricted dynamic programming is applied to the VRP through the giant-tour representation. This algorithm is a construction heuristic which for many types of restrictions and objective functions leads to an optimal algorithm when applied in an unrestricted way. We demonstrate the flexibility of the framework for various restrictions appearing in practice. The computational experiments demonstrate that the framework competes with state of the art local search methods when more realistic constraints are considered than in classical VRPs. Therefore, this new framework for solving VRPs is a promising approach for practical applications.