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.


References in zbMATH (referenced in 83 articles )

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  1. Lombera Rodríguez, Hassán; Tello, J. Ignacio: On the Reynolds equation and the load problem in lubrication: literature review and mathematical modelling (2019)
  2. Koç, Çağrı; Laporte, Gilbert: Vehicle routing with backhauls: review and research perspectives (2018)
  3. Hosseinabadi, Ali Asghar Rahmani; Rostami, Najmeh Sadat Hosseini; Kardgar, Maryam; Mirkamali, Seyedsaeid; Abraham, Ajith: A new efficient approach for solving the capacitated vehicle routing problem using the gravitational emulation local search algorithm (2017)
  4. Yang, Zhiwei; van Osta, Jan-Paul; van Veen, Barry; van Krevelen, Rick; van Klaveren, Richard; Stam, Andries; Kok, Joost; Bäck, Thomas; Emmerich, Michael: Dynamic vehicle routing with time windows in theory and practice (2017)
  5. Jagatheesan, K.; Anand, B.; Baskaran, K.; Dey, Nilanjan: Evolutionary computational technique in automatic generation control of multi-area power systems with nonlinearity and energy storage unit (2016)
  6. Padrón, Silvia; Guimarans, Daniel; Ramos, Juan José; Fitouri-Trabelsi, Salma: A bi-objective approach for scheduling ground-handling vehicles in airports (2016)
  7. 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)
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  15. 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)
  16. Pereira, Jordi; Averbakh, Igor: The robust set covering problem with interval data (2013)
  17. Tricoire, Fabien; Bostel, Nathalie; Dejax, Pierre; Guez, Pierre: Exact and hybrid methods for the multiperiod field service routing problem (2013)
  18. van Stein, Bas; Emmerich, Michael; Yang, Zhiwei: Fitness landscape analysis of NK landscapes and vehicle routing problems by expanded barrier trees (2013) ioport
  19. Xu, Zilong; Zhao, Hong; Min, Fan; Zhu, William: Ant colony optimization with three stages for independent test cost attribute reduction (2013) ioport
  20. Cheng, Jixang; Zhang, Gexiang; Li, Zhidan; Li, Yuquan: Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems (2012)

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