A Hybrid Particle Swarm Optimization with Genetic Algorithm for Solving Capacitated Vehicle Routing Problem with Fuzzy Demand – A Case Study on Garbage Collection System
碩士 === 國立臺灣科技大學 === 工業管理系 === 98 === This research proposes a Hybrid Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) for solving Capacitated Vehicle Routing Problem (CVRP) and CVRP with Fuzzy Demand (CVRPFD). The CVRPFD is developed using Change Constraint Program model with credibilit...
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| Format: | Others |
| Language: | en_US |
| Published: |
2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/28967884157406431561 |
| Summary: | 碩士 === 國立臺灣科技大學 === 工業管理系 === 98 === This research proposes a Hybrid Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) for solving Capacitated Vehicle Routing Problem (CVRP) and CVRP with Fuzzy Demand (CVRPFD). The CVRPFD is developed using Change Constraint Program model with credibility measurement. The proposed method uses the idea of particle’s best solution and social’s best solution in PSO algorithm, followed by combining it with crossover and mutation of GA. This method also modifies the particle’s coding to ensure particle always can generate feasible solution. The proposed method is evaluated by using nine benchmark data sets for CVRP and garbage collection system data for CVRPFD. The results indicate that the proposed Hybrid PSO with GA has promising performance for solving CVRP and CVRPFD. It not only can obtain better solutions, but also only requires small number of particles and iterations.
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