MACS-VRPTW

MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows 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. 2 Chapter 5 MACS-VRPTW: A MULTIPLE ANT COLONY SYSTEM FOR VEHICLE ROUTING PROBLEMS WITH TIME WINDOWS 5.1. Introduction This chapter presents MACS-VRPTW, a Multiple Ant Colony System for Vehicle Routing Problems with Time Windows. MACS-VRPTW is based on Ant Colony System (ACS) (Gambard.


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  1. McNabb, Marcus E.; Weir, Jeffery D.; Hill, Raymond R.; Hall, Shane N.: Testing local search move operators on the vehicle routing problem with split deliveries and time windows (2015)
  2. Chiang, Tsung-Che; Hsu, Wei-Huai: A knowledge-based evolutionary algorithm for the multiobjective vehicle routing problem with time windows (2014)
  3. Ghannadpour, S.F.; Noori, S.; Tavakkoli-Moghaddam, R.: A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority (2014)
  4. Huang, Min; Ding, Ping: An improved ant colony algorithm and its application in vehicle routing problem (2013)
  5. Mora, A.M.; García-Sánchez, P.; Merelo, J.J.; Castillo, P.A.: Pareto-based multi-colony multi-objective ant colony optimization algorithms: an island model proposal (2013)
  6. Mora, A.M.; Merelo, J.J.; Castillo, P.A.; Arenas, M.G.: hCHAC: a family of MOACO algorithms for the resolution of the bi-criteria military unit pathfinding problem (2013)
  7. Pereira, Jordi; Averbakh, Igor: The robust set covering problem with interval data (2013)
  8. Xu, Zilong; Zhao, Hong; Min, Fan; Zhu, William: Ant colony optimization with three stages for independent test cost attribute reduction (2013)
  9. Cheng, Jixang; Zhang, Gexiang; Li, Zhidan; Li, Yuquan: Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems (2012)
  10. Créput, Jean-Charles; Hajjam, Amir; Koukam, Abderrafiaa; Kuhn, Olivier: Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem (2012)
  11. Gambardella, Luca Maria; Montemanni, Roberto; Weyland, Dennis: Coupling ant colony systems with strong local searches (2012)
  12. Nazif, Habibeh; Lee, Lai Soon: Optimised crossover genetic algorithm for capacitated vehicle routing problem (2012)
  13. Noori, Siamak; Ghannadpour, S.Farid: High-level relay hybrid metaheuristic method for multi-depot vehicle routing problem with time windows (2012)
  14. Sprenger, Ralf; Mönch, Lars: A methodology to solve large-scale cooperative transportation planning problems (2012)
  15. Yildirim, U.Mahir; Çatay, Bülent: A time-based pheromone approach for the ant system (2012)
  16. Balseiro, S.R.; Loiseau, I.; Ramonet, J.: An Ant Colony algorithm hybridized with insertion heuristics for the Time Dependent Vehicle Routing Problem with Time Windows (2011)
  17. Cardoso, Pedro; Jesus, Mário; Márquez, Alberto: $\epsilon$- DANTE: an ant colony oriented depth search procedure (2011)
  18. Berrichi, A.; Yalaoui, F.; Amodeo, L.; Mezghiche, M.: Bi-objective ant colony optimization approach to optimize production and maintenance scheduling (2010)
  19. Chica, Manuel; Cordón, Óscar; Damas, Sergio; Bautista, Joaquín: Multiobjective constructive heuristics for the 1/3 variant of the time and space assembly line balancing problem: ACO and random greedy search (2010)
  20. Garrido, Pablo; Riff, María Cristina: DVRP: a hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic (2010)

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