- 學位論文
智慧型基因演算法於單機多目標排程之發展與應用
Develop An Intelligent Genetic Algorithm for the Multi-objective Single Machine Scheduling
摘要
本研究為針對單機情況下的多目標排程問題，試著提出改良式的智慧型基因演算法。多目標基因演算法雖然已提出多年，並經過眾多學者研究後已成功應用於排程問題，但基因運算元演化過程及求解效率仍有很大改善空間。有鑑於此，提出了以考量境外移入之特性的基因演算法為基礎作為修改的智慧型基因演算法，應用於單機排程並考量工作的處理時間、交期、抵達時間與遲交權重，又以總完工時間與總加權遲交時間為排程目標，演算搜尋求得多目標的非劣解集合。最後，以兩種基因演算法演算多個不同的樣本並比較其求解成效，所得結果為雖然本論文所提出的智慧型基因演算法花費時間要比基因演算法多，但能以較少的世代數求得較好的非劣解集合。
並列摘要
The research focuses on multi-objects scheduling problem with one processing machine, and try to find an improved genetic algorithms. The multi-objects genetic algorithms has been studied for many years and applied to solve the schedule problem successfully, but the procrdure and the efficiency are still need to be improved. This research consider the attribute of immigrant and offered a new algorithms called intelligent genetic algorithms to solve the single machine scheduling problems. The new genetic algorithms considers the process time, lead time, delivery date and tardiness weight to find the non‐inferior solutions with best performance on total makespan and total weighted tardiness. Finally, we compare the the new genetic algorithms and the normal genetic algorithms by processing many different samples, and we find that the intelligent genetic algorithms needs more time to process, but it can find the better non‐inferior solutions with less generations.