生產排程主要是透過有效地資源分配來提高生產效率、降低生產成本，為了能達到既定的目標 (滿足交貨時間或縮短機台閒置時間)，生產排程至今仍是多目標最佳化領域中常見的研究題目。大部分的排程問題都是屬於組合最佳化問題並且難以求出最佳解，零工式工廠生產排程問題就是屬於此問題之一，多目標彈性零工式工廠排程問題 (Multi-objective Flexible Job-shop Scheduling Problem) 旨在如何分配適當的機台給每一零件的製程使用 (路由問題)以及如何將這些已選定機台的製程排序 (排程問題) 以最小化完工時間 (makespan)、最大機台工作量 (maximal machine workload)、總機台工作量 (total workload) 。
本論文提供基因演算法 (Genetic Algorithm, GA) 搭配禁忌搜尋法 (Tabu Search, TS) 去解多目標彈性零工式工廠生產排程問題，有別於文獻中合併函式 (aggregation function) 適應值 (fitness) 的算法，我們利用柏拉圖法 (Pareto) 計算適應值以求得柏拉圖最佳解 (Pareto front)。其中在禁忌搜尋法中加入變動鄰域尋優演算法 (Variable Neighborhood Descent, VND),從開始時間到完工時間的最長路徑 (critical path) 找到關鍵製程 (critical operations)，利用交換與插入關鍵製程改變最長路徑來縮短最小完工時間。
實驗問題包含Kacem data與BR data共十五個測試問題。本研究在Kacem data皆能透過一次的實驗就能找到過去所有文獻中提出的最佳解，而BR data則有五個測試問題可以更新文獻中的最佳解。

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