A new optimization algorithm for the vehicle routing problem with time windows
Operations Research
The vehicle routing problem
2-Path Cuts for the Vehicle Routing Problem with Time Windows
Transportation Science
Time-Varying Travel Times in Vehicle Routing
Transportation Science
A dynamic vehicle routing problem with time-dependent travel times
Computers and Operations Research
Vehicle routing problem with elementary shortest path based column generation
Computers and Operations Research
The Shortest-Path Problem with Resource Constraints and k-Cycle Elimination for k ≥ 3
INFORMS Journal on Computing
Selected Topics in Column Generation
Operations Research
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
Formulations and exact algorithms for the vehicle routing problem with time windows
Computers and Operations Research
Subset-Row Inequalities Applied to the Vehicle-Routing Problem with Time Windows
Operations Research
Computers and Operations Research
New Route Relaxation and Pricing Strategies for the Vehicle Routing Problem
Operations Research
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This paper presents a branch-and-price algorithm for the time-dependent vehicle routing problem with time windows TDVRPTW. We capture road congestion by considering time-dependent travel times, i.e., depending on the departure time to a customer, a different travel time is incurred. We consider the variant of the TDVRPTW where the objective is to minimize total route duration and denote this variant the duration minimizing TDVRPTW DM-TDVRPTW. Because of time dependency, vehicles' dispatch times at the depot are crucial as road congestion might be avoided. Because of its complexity, all known solution methods to the DM-TDVRPTW are based on meta-heuristics. The decomposition of an arc-based formulation leads to a set-partitioning problem as the master problem, and a time-dependent shortest path problem with resource constraints as the pricing problem. The master problem is solved by means of column generation, and a tailored labeling algorithm is used to solve the pricing problem. We introduce new dominance criteria that allow more label dominance. For our numerical results, we modified Solomon's data sets by adding time dependency. Our algorithm is able to solve about 63% of the instances with 25 customers, 38% of the instances with 50 customers, and 15% of the instances with 100 customers.