A Column Generation Algorithm for a Rich Vehicle-Routing Problem
Transportation Science
Iterated local search for the team orienteering problem with time windows
Computers and Operations Research
Branch and Cut and Price for the Pickup and Delivery Problem with Time Windows
Transportation Science
Constraint-specific recovery network for solving airline recovery problems
Computers and Operations Research
An ILP improvement procedure for the Open Vehicle Routing Problem
Computers and Operations Research
Optimum routing protection against cumulative eavesdropping in multihop wireless networks
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
New Route Relaxation and Pricing Strategies for the Vehicle Routing Problem
Operations Research
Local conflict resolution for automated taxi operations
Proceedings of the 1st International Conference on Application and Theory of Automation in Command and Control Systems
The robust vehicle routing problem with time windows
Computers and Operations Research
Optimal solutions for routing problems with profits
Discrete Applied Mathematics
Lifted and Local Reachability Cuts for the Vehicle Routing Problem with Time Windows
Computers and Operations Research
Using the primal-dual interior point algorithm within the branch-price-and-cut method
Computers and Operations Research
An Exact Algorithm for the Integrated Planning of Berth Allocation and Quay Crane Assignment
Transportation Science
Branch and Price for the Time-Dependent Vehicle Routing Problem with Time Windows
Transportation Science
A set-covering based heuristic algorithm for the periodic vehicle routing problem
Discrete Applied Mathematics
Combined location and routing problems for drug distribution
Discrete Applied Mathematics
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The resource constrained elementary shortest path problem (RCESPP) arises as a pricing subproblem in branch-and-price algorithms for vehicle-routing problems with additional constraints. We address the optimization of the RCESPP and we present and compare three methods. The first method is a well-known exact dynamic-programming algorithm improved by new ideas, such as bidirectional search with resource-based bounding. The second method consists in a branch-and-bound algorithm, where lower bounds are computed by dynamic-programming with state-space relaxation; we show how bounded bidirectional search can be adapted to state-space relaxation and we present different branching strategies and their hybridization. The third method, called decremental state-space relaxation, is a new one; exact dynamic-programming and state-space relaxation are two special cases of this new method. The experimental comparison of the three methods is definitely favorable to decrement state-space relaxation. Computational results are given for different kinds of resources, arising from the capacitated vehicle-routing problem, the vehicle-routing problem with distribution and collection, and the vehicle-routing problem with capacities and time windows. © 2007 Wiley Periodicals, Inc. NETWORKS, 2008