查詢結果分析
來源資料
頁籤選單縮合
題名 | A Genetic Optimization Algorithm and Perceptron Learning Rules for a Bi-Criteria Parallel Machine Scheduling=運用基因最佳化以及感知器學習法之多功能平行機臺排程問題 |
---|---|
作者 | Fazlollahtabar, Hamed; Hassanzadeh, Reza; Mahdavi, Iraj; Amiri, Nezam-Mahdavi; | 書刊名 | 工業工程學刊 |
卷期 | 29:3 2012.04[民101.04] |
頁次 | 頁206-218 |
分類號 | 494.5 |
關鍵詞 | 平行機臺排程; 感知器學習法; 設置及移除時間; 數學規劃模型; 基因最佳化; Parallel machine scheduling; Perceptron learning rule; Setup and removal times; Mathematical programming model; Genetic optimization; |
語文 | 英文(English) |
英文摘要 | This work considers scheduling problems minding the setup and removal times of jobs rather than processing times. For some production systems, setup times and removal times are so important to be considered independent of processing times. In general, jobs are performed according to the automatic machine processing in production systems, and the processing times are considered to be constant regardless of the process sequence. As the human factor can influence the setup and removal times, when the setup process is repetitive the setup times decreases. This fact is considered as learning effect in scheduling literature. In this study, a bi-criteria m-identical parallel machines scheduling problem with learning effects of setup and removal times is considered. The learning effect is proposed using a perceptron neural network algorithm. The objective function of the problem is minimization of the weighted sum of total earliness and tardiness. A mathematical programming model is developed for the problem, which is NP-hard. Results of computational tests show that the LINGO 9 software is effective in solving problems with up to 25 jobs and five machines. Therefore, for larger sized problems, a genetic algorithm for optimization is developed. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。