Distributed Swarm Optimization Modeling for Waste Collection Vehicle Routing Problem

In this paper, we consider a complex garbage collection problem, where the residents of a particular area dispose of recyclable garbage, which is collected and managed using a fleet of trucks with different weight capacities and volume. This tour is characterized by a set of constraints such as the maximum tour duration (in term of distance and the timing) consumed to collect wastes from several locations. This problem is modeled as a garbage collection vehicle routing problem, which aims to minimize the cost of traveling routes (minimizing the distance traveled) by finding optimal routes for vehicles such that all waste bins are emptied and the waste is driven towards the disposal locations. We propose a distributed technique based on the Ant Colony system Algorithm to find optimal routes that help vehicles to visit all the wastes bins using interactive agents consumed based on the behavior of real ants. The designed solution will try to create a set of layers to control and manage the waste collection, each layer will be handled by an intelligent agent which is characterized by a specific behavior, in this architecture a set of behaviors have been designed to optimizing routes and control the real time capacity of vehicles. Finally, manage the traffic messages between the different agents to select the best solutions that will be assigned to each vehicle. The developed solution performs well compared to the traditional solution on small cases.

@article{Mouhcine2017,
  title     = {Distributed Swarm Optimization Modeling for Waste Collection Vehicle Routing Problem},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {9},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {ELGAREJ Mouhcine and MANSOURI Khalifa and YOUSSFI Mohamed and BENMOUSSA Nezha and EL FAZAZI Hanae},
  doi       = {10.14569/IJACSA.2017.080943},
  url       = {https://doi.org/10.14569/IJACSA.2017.080943}
}