A metaheuristic for the pickup and delivery problem with time windows
In this paper, we propose a metaheuristic to solve the pickup and delivery problem with time
windows. Our approach is a tabu-embedded simulated annealing algorithm which restarts a
search procedure from the current best solution after several non-improving search
iterations. The computational experiments on the six newly-generated different data sets
marked our algorithm as the first approach to solve large multiple-vehicle PDPTW problem
instances with various distribution properties.
windows. Our approach is a tabu-embedded simulated annealing algorithm which restarts a
search procedure from the current best solution after several non-improving search
iterations. The computational experiments on the six newly-generated different data sets
marked our algorithm as the first approach to solve large multiple-vehicle PDPTW problem
instances with various distribution properties.
In this paper, we propose a metaheuristic to solve the pickup and delivery problem with time windows. Our approach is a tabu-embedded simulated annealing algorithm which restarts a search procedure from the current best solution after several non-improving search iterations. The computational experiments on the six newly-generated different data sets marked our algorithm as the first approach to solve large multiple-vehicle PDPTW problem instances with various distribution properties.
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