Parallelization of a Two-Phase Metaheuristic for Routing Problems with Time Windows
Journal of Heuristics
Continuous Truck Delivery Scheduling and Execution System with Multiple Agents
Proceedings of the 5th Pacific Rim International Workshop on Multi Agents: Intelligent Agents and Multi-Agent Systems
Multi-cast ant colony system for the bus routing problem
Metaheuristics
A cooperative parallel meta-heuristic for the vehicle routing problem with time windows
Computers and Operations Research
A two-pronged attack on the dragon of intractability
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
A multi-agent algorithm for vehicle routing problem with time window
Proceedings of the 2006 ACM symposium on Applied computing
PSO-based algorithm for home care worker scheduling in the UK
Computers and Industrial Engineering
A New Approach to Improve the Ant Colony System Performance: Learning Levels
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Reasoning elements for a vehicle routing system
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Determination of the heat transfer coefficient by using the ant colony optimization algorithm
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
A distributed metaheuristic for solving a real-world scheduling-routing-loading problem
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
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MACS-VRPTW, an Ant Colony Optimization based approach useful to solve vehicle routing problems with time windows is presented. MACS-VRPTW is organized with a hierarchy of artificial ant colonies designed to successively optimize a multiple objective function: the first colony minimizes the number of vehicles while the second colony minimizes the traveled distances. Cooperation between colonies is performed by exchanging information through pheromone updating. We show that MACS-VRPTW is competitive with the best known existing methods both in terms of solution quality and computation time. Moreover, MACS-VRPTW improves some of the best solutions known for a number of problem instances in the literature.