Research of a Constraint-Based Particla Swarm Optimization and Genetic Algorithm Approach for Scheduling Problems

• Main Authors: Shun-Yuan Cheng, 鄭舜源
• Other Authors: Pei-Lun Hsu
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/22834556468896486387

碩士 === 清雲科技大學 === 電子工程研究所 === 95 === The job-shop scheduling(JSP) problem is a process of assigning a limited number of machines to operations over time in a consistent manner. Existing particle swarm optimization (PSO) designed for the JSP are devise an appropriate representation of solutions together with problem-specific particle operators to avoid infeasible or illegal schedules. In this paper, we propose a constraint-based approach to generate valid particle in either the initial phase or the evolutionary process. This approach allows constraints to be specified as relationships among operations according to precedent constraints and capacity constraints in the form of a constraint network. The constraint-based reasoning is employed to produce valid particles using constraint propagation to assure the particle in complying the predefined constraint network. Additionly, the new udate approach based on genetic algorithm (GA) is incorporated to produce better schedules. The proposed approach is compared with a traditional PSO using well-known benchmarks for the JSP. Better computational efficiency and optimal schedules form constraint-based PSO and GA are demonstrated.