A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows

2014 ◽  
Vol 47 (6) ◽  
pp. 825-842 ◽  
Author(s):  
Yanfang Ma ◽  
Jiuping Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Decision Maker ◽  
Time Windows ◽  
Particle Swarm ◽  
Random Time ◽  
Vehicle Routing Problems ◽  
Swarm Optimization ◽  
Cloud Theory ◽  
Fuzzy Random

scholarly journals Novel Encoding and Routing Balance Insertion Based Particle Swarm Optimization with Application to Optimal CVRP Depot Location Determination

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Yin-Mou Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Vehicle Routing ◽  
Particle Swarm ◽  
Transportation Cost ◽  
Vehicle Routing Problems ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Routing Problems ◽  
Vehicle Routes

A depot location has a significant effect on the transportation cost in vehicle routing problems. This study proposes a hierarchical particle swarm optimization (PSO) including inner and outer layers to obtain the best location to establish a depot and the corresponding optimal vehicle routes using the determined depot location. The inner layer PSO is applied to obtain optimal vehicle routes while the outer layer PSO is to acquire the depot location. A novel particle encoding is suggested for the inner layer PSO, the novel PSO encoding facilitates solving the customer assignment and the visiting order determination simultaneously to greatly lower processing efforts and hence reduce the computation complexity. Meanwhile, a routing balance insertion (RBI) local search is designed to improve the solution quality. The RBI local search moves the nearest customer from the longest route to the shortest route to reduce the travel distance. Vehicle routing problems from an operation research library were tested and an average of 16% total routing distance improvement between having and not having planned the optimal depot locations is obtained. A real world case for finding the new plant location was also conducted and significantly reduced the cost by about 29%.

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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