Using Constraint-Based Reasoning and Particle Swarm Optimization Approach in Call Center Staff Rostering
碩士 === 元智大學 === 資訊管理學系 === 95 === This paper presents a new improve method combining the particle swarm optimization (PSO) with the constraint-based reasoning during the stage of the initialization and evolution in the PSO. We call it constraint-based particle swarm optimization (CBPSO). CBPSO can...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2007
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| Online Access: | http://ndltd.ncl.edu.tw/handle/73493466759210575985 |
| Summary: | 碩士 === 元智大學 === 資訊管理學系 === 95 === This paper presents a new improve method combining the particle swarm optimization (PSO) with the constraint-based reasoning during the stage of the initialization and evolution in the PSO. We call it constraint-based particle swarm optimization (CBPSO). CBPSO can reduce the search space which violates pre-defined constraints through forward checking. After generates a whole particle using forward checking, this particle is a feasible solution. Then we propose a select strategy in CBPSO and use forward checking for the stage of the evolution. We can use this select strategy to select the partial particle for improving the fitness value of the whole particle. We use the Call Center Staff Rostering Problem to prove our method and this result shows that CBPSO has better efficiency than PSO in the evolution’s time and fitness value.
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