A new heuristic for the multi-depot vehicle routing problem that improves upon best-known solutions
American Journal of Mathematical and Management Sciences - Special issue: vehicle routing 2000: advances in time windows, optimality, fast bounds, & multi-depot routing
A tabu search heuristic for the multi-depot vehicle routing problem
Computers and Operations Research
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
A general heuristic for vehicle routing problems
Computers and Operations Research
A hybrid genetic algorithm for the multi-depot vehicle routing problem
Engineering Applications of Artificial Intelligence
Computers and Operations Research
Color Image Segmentation Using Clonal Selection-Based Shuffled Frog Leaping Algorithm
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Engineering Applications of Artificial Intelligence
Efficient stochastic hybrid heuristics for the multi-depot vehicle routing problem
Robotics and Computer-Integrated Manufacturing
An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation
Information Sciences: an International Journal
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In the present work, an improved Shuffled Frog Leaping Algorithm (SFLA) and its multi-phase model are presented to solve the multi-depots vehicle routing problems (MDVRPs). To further improve the local search ability of SFLA and speed up convergence, a Power Law Extremal Optimization Neighborhood Search (PLEONS) is introduced to SFLA. In the multi-phase model, firstly the proposed algorithm generates some clusters randomly to perform the clustering analyses considering the depots as the centroids of the clusters for all the customers of MDVRP. Afterward, it implements the local depth search using the SFLA for every cluster, and then globally re-adjusts the solutions, i.e., rectifies the positions of all frogs by PLEONS. In the next step, a new clustering analyses is performed to generate new clusters according to the best solution achieved by the preceding process. The improved path information is inherited to the new clusters, and the local search using SFLA for every cluster is used again. The processes continue until the convergence criterions are satisfied. The experiment results show that the proposed algorithm possesses outstanding performance to solve the MDVRP and the MDVRP with time windows.