A tabu search heuristic for the heterogenous fleet vehicle routing problem
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Operations Research
Exchange strategies for multiple Ant Colony System
Information Sciences: an International Journal
Particle swarm optimization-based algorithms for TSP and generalized TSP
Information Processing Letters
A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery
Computers and Operations Research
An Improved Discrete Particle Swarm Optimization Based on Cooperative Swarms
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Computers and Industrial Engineering
Two memetic algorithms for heterogeneous fleet vehicle routing problems
Engineering Applications of Artificial Intelligence
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Vehicle routing scheduling using an enhanced hybrid optimization approach
Journal of Intelligent Manufacturing
A probability matrix based particle swarm optimization for the capacitated vehicle routing problem
Journal of Intelligent Manufacturing
Efficient metaheuristics for pick and place robotic systems optimization
Journal of Intelligent Manufacturing
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Today, companies need to collect and to deliver goods from and to their depots and their customers. This problem is described as a Vehicle Routing Problem with Mixed Linehaul and Backhaul customers (VRPMB). The goods delivered from the depot to the customers can be alternated with the goods picked up. Other variants of VRP added to VRPMB are Heterogeneous fleet and Time Windows. This paper studies a complex VRP called HVRPMBTW which concerns a logistic/transport society, a problem rarely studied in literature. In this paper, we propose a Particle Swarm Optimization (PSO) with a local search. This approach has shown its effectiveness on several combinatorial problems. The adaptation of this approach to the problem studied is explained and tested on the benchmarks. The results are compared with our previous methods and they show that in several cases PSO improves the results.