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.

Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization

2012 ◽  
Vol 253-255 ◽  
pp. 1369-1373
Author(s):  
Tie Jun Wang ◽  
Kai Jun Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Cloud Model ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Adaptive Particle Swarm Optimization ◽  
Searching Ability

Multi-depots vehicle routing problem (MDVRP) is a kind of NP combination problem which possesses important practical value. In order to overcome PSO’s premature convergence and slow astringe, a Cloud Adaptive Particle Swarm Optimization(CAPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In this paper, the algorithm is used to solve MDVRP, a kind of new particles coding method is constructed and the solution algorithm is developed. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.

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

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.

Optimization Mechanism Control Strategy of Vehicle Routing Problem Based on Improved PSO

2013 ◽  
Vol 681 ◽  
pp. 130-136
Author(s):  
Jun Ting Zhang ◽  
Li Xia Qiao
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Discrete Problem ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Operation Rules ◽  
Self Learning

Traveling salesman problem based on vehicle routing problem in the case, according to the discrete domain specificity, redefine the problem domain to the mapping relationship between particles and related operation rules, and the introduction of self learning operator so that the PSO algorithm can deal with discrete problem. Vehicle Routing Problem (VRP) is research on how to plan the vehicles routes in order to save the transportation cost. Improved Particle Swarm Optimization (PSO) algorithm is proposed to solve the VRP in this paper. To improve the efficiency of the Particle Swarm Optimization, self-learning operator is constructed. Particles are re coded and operate rules are redefined to deal with the discrete problem of VRP. The effectiveness of the proposed algorithm is demonstrated by the simulations.

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