Particle Swarm Optimization to solve the Vehicle Routing Problem with Heterogeneous fleet, Mixed Backhauls, and time windows

2010 ◽  
Author(s):  
Farah Belmecheri ◽  
Christian Prins ◽  
Farouk Yalaoui ◽  
Lionel Amodeo
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Heterogeneous Fleet

scholarly journals A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 21033-21053 ◽  
Author(s):  
Sheng-Hua Xu ◽  
Ji-Ping Liu ◽  
Fu-Hao Zhang ◽  
Liang Wang ◽  
Li-Jian Sun
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Routing Problem

scholarly journals Vehicle Routing Problem with Time Windows and Simultaneous Delivery and Pick-Up Service Based on MCPSO

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaobing Gan ◽  
Yan Wang ◽  
Shuhai Li ◽  
Ben Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Particle Swarm ◽  
Mathematic Model ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Simultaneous Delivery

This paper considers two additional factors of the widely researched vehicle routing problem with time windows (VRPTW). The two factors, which are very common characteristics in realworld, are uncertain number of vehicles and simultaneous delivery and pick-up service. Using minimization of the total transport costs as the objective of the extension VRPTW, a mathematic model is constructed. To solve the problem, an efficient multiswarm cooperative particle swarm optimization (MCPSO) algorithm is applied. And a new encoding method is proposed for the extension VRPTW. Finally, comparing with genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, the MCPSO algorithm performs best for solving this problem.

Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

2004 ◽  
Vol 471-472 ◽  
pp. 801-805 ◽  
Author(s):  
Yan Wei Zhao ◽  
B. Wu ◽  
W.L. Wang ◽  
Ying Li Ma ◽  
W.A. Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Nearest Neighbor ◽  
Time Windows ◽  
Particle Swarm ◽  
Main Theme ◽  
Nearest Neighbor Algorithm ◽  
Swarm Optimization ◽  
Routing Problem

The investigation of the performance of the Particle Swarm Optimization (PSO) method for Vehicle Routing Problem with Time Windows is the main theme of the paper. “Exchange minus operator” is constructed to compute particle’s velocity. We use Saving algorithm, Nearest Neighbor algorithm, and Solomon insertion heuristics for parameter initialization and apply the “Routing first and Cluster second” strategy for solution generation. By PSO, customers are sorted in an ordered sequence for vehicle assignment and Nearest Neighbor algorithm is used to optimize every vehicle route. In our experiments, two different PSO algorithms (global and local), and three construct algorithms are investigated for omparison. Computational results show that global PSO algorithm with Solomon insertion heuristics is more efficiency than the others.

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