遺傳基因演算法在彈性製造系統排程問題之探討

An Approach of Genetic Algorithm in FMS Scheduling

駱景堯(Chin-Yao Low) ； 張俊仁(Chun-Jen Chang)

《大葉學報》 7卷1期 (1998/12) Pp. 79-90

https://doi.org/10.7119/JDYU.199812.0079

摘要

彈性製造系統（flexible manufacturing system：FMS）乃是融合了許多自動化的觀念與彈性技術於單一的生產系統中，是一種多種類少量的彈性生產方式。由於其在生產上擁有途程彈性之特性，使得排程問題由傳統的單一途程規劃轉變成多途程規劃加工背景的排程問題，除需考慮各工件的加工順序外，還需同時考慮各作業選擇的加工機器問題，增加了排程問題的複雜度。本研究運用具求解最佳解能力的遺傳基因演算法（genetic algorithms）之觀念發展一套求解彈性製造系統排程的啟發式解法，隨後在求解之過程中，針對一般廣為業界所關心之三種排程評估標準：最小化平均流程時間、最小化平均工作延遲、與最大化平均資源使用率等在遺傳基因演算法執行中系統參數所造成之影響作一深入之分析與探討，並以實驗設計找出最佳參數組合，以增進排程結果的品質，使遺傳基因演算法在FMS排程上之應用更加穩健。

關鍵字

彈性製造系統 ； 遺傳基因演算法 ； 平均流程時間 ； 平均工作延遲 ； 平均資源使用率

並列摘要

Scheduling in a flexible manufacturing system (FMS) differs from that in a conventional job shop because each operation of a job may be performed by any one of several machines. The routing flexibility is a feature that distinguishes FMS scheduling from a classic general job shop problem. In the general job shop scheduling problem, the researchers only concerns about the job sequence on each machine; however, routing and sequencing need to be decided simultaneously in the FMS environment. In this research, the addressed FMS scheduling problem is solved by one of the well known optimization techniques called genetic algorithm. Furthermore, three performance measure which are the most concerned by the decision makers are considered to evaluate the performance of the Gas. Finally, the factors which are deeply affect the GAs performance are analyzed, and the best combination is suggested to obtain the best performance of GAs

並列關鍵字

scheduling ； flexible manufacturing systems ； job shop ； genetic algorithm