Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows
Journal of Heuristics
A cooperative parallel meta-heuristic for the vehicle routing problem with time windows
Computers and Operations Research
A Guided Cooperative Search for the Vehicle Routing Problem with Time Windows
IEEE Intelligent Systems
A general heuristic for vehicle routing problems
Computers and Operations Research
The vehicle routing problem with flexible time windows and traveling times
Discrete Applied Mathematics - Special issue: Discrete algorithms and optimization, in honor of professor Toshihide Ibaraki at his retirement from Kyoto University
Active guided evolution strategies for large-scale vehicle routing problems with time windows
Computers and Operations Research
Discrete Applied Mathematics
Randomized adaptive spatial decoupling for large-scale vehicle routing with time windows
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A GRASP with evolutionary path relinking for the truck and trailer routing problem
Computers and Operations Research
Adaptive Path Relinking for Vehicle Routing and Scheduling Problems with Product Returns
Transportation Science
Statistical analysis of distance-based path relinking for the capacitated vehicle routing problem
Computers and Operations Research
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We propose a path relinking approach for the vehicle routing problem with time windows. The path relinking is an evolutionary mechanism that generates new solutions by combining two or more reference solutions. In our algorithm, those solutions generated by path relinking operations are improved by a local search whose neighborhood consists of slight modifications of the representative neighborhoods called 2-opt*, cross exchange and Or-opt. To make the search more efficient, we propose a neighbor list that prunes the neighborhood search heuristically. Infeasible solutions are allowed to be visited during the search, while the amount of violation is penalized. As the performance of the algorithm crucially depends on penalty weights that specify how such penalty is emphasized, we propose an adaptive mechanism to control the penalty weights. The computational results on well-studied benchmark instances with up to 1000 customers revealed that our algorithm is highly efficient especially for large instances. Moreover, it updated 41 best known solutions among 356 instances.