Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem
Annals of Operations Research - Special issue on Tabu search
A tabu search heuristic for the vehicle routing problem
Management Science
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Scheduling Deliveries in Vehicles with Multiple Compartments
Journal of Global Optimization
A Savings Based Ant System For The Vehicle Routing Problem
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A genetic algorithm for the vehicle routing problem
Computers and Operations Research
D-Ants: savings based ants divide and conquer the vehicle routing problem
Computers and Operations Research
Ant Colony Optimization
Routing Optimization for Waste Management
Interfaces
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows
Applied Intelligence
A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows
Computational Optimization and Applications
A general heuristic for vehicle routing problems
Computers and Operations Research
A memetic algorithm and a tabu search for the multi-compartment vehicle routing problem
Computers and Operations Research
A new hybrid ant colony optimization algorithm for the vehicle routing problem
Pattern Recognition Letters
Localized genetic algorithm for vehicle routing problem with time windows
Applied Soft Computing
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We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem associated with collection of recycling waste from households, treated as nodes in a spatial network. For networks where the nodes are concentrated in separate clusters, the use of k-means clustering can greatly improve the efficiency of the solution. The ACS algorithm is extended to model the use of multi-compartment vehicles with kerbside sorting of waste into separate compartments for glass, paper, etc. The algorithm produces high-quality solutions for two-compartment test problems.