研究生: |
潘英發 Frans - Januar Panto |
---|---|
論文名稱: |
A job-shop scheduling algorithm based on hybrid bees algorithm combined with tabu search A job-shop scheduling algorithm based on hybrid bees algorithm combined with tabu search |
指導教授: |
洪西進
Shi-Jinn Horng |
口試委員: |
邱舉明
none 金台齡 none 項天瑞 none |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
論文出版年: | 2010 |
畢業學年度: | 98 |
語文別: | 英文 |
論文頁數: | 39 |
中文關鍵詞: | Job-shop scheduling problem (JSSP) 、Bees Algorithm (BA) 、Tabu Search (TS) 、Neighborhood Structure (NS) |
外文關鍵詞: | Job-shop scheduling problem (JSSP), Bees Algorithm (BA), Tabu Search (TS), Neighborhood Structure (NS) |
相關次數: | 點閱:178 下載:3 |
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The job-shop scheduling problem is one of the most NP-Hard problems and has attracted many researchers to develop new algorithm based on heuristic or meta-heuristic approach. Many algorithms based on Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization have been proposed to solve it, respectively but the results are still not yet satisfactory. In this paper we proposed a new algorithm based on bees algorithm which is very good for discrete optimization problem; furthermore, the results are optimized with the famous tabu search for local search to improve the results. The robustness, efficiency, and effectiveness of this algorithm are examined using a set of benchmark instances and compared with existing algorithms. The results show that the proposed algorithm could obtain competitive results within reasonable computing times.
The job-shop scheduling problem is one of the most NP-Hard problems and has attracted many researchers to develop new algorithm based on heuristic or meta-heuristic approach. Many algorithms based on Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization have been proposed to solve it, respectively but the results are still not yet satisfactory. In this paper we proposed a new algorithm based on bees algorithm which is very good for discrete optimization problem; furthermore, the results are optimized with the famous tabu search for local search to improve the results. The robustness, efficiency, and effectiveness of this algorithm are examined using a set of benchmark instances and compared with existing algorithms. The results show that the proposed algorithm could obtain competitive results within reasonable computing times.
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