生產排程依據生產類型、處理時間、生產約束及目標式等因素的不同，已發展出許多方法來解決排程的各種變化形式問題，在這些研究當中大多針對集中式生產製造系統，在面臨全球化快速發展階段，當企業版圖不斷擴大與供應鏈複雜化等因素產生，分散式生產製造系統逐漸成為主流，因其分散性具備風險分散、靠近商業行銷市場與原料供應地等優勢，可幫助企業創造更高價值的商業利益。
針對分散式製造系統，過往研究較少探討更複雜的分散式彈性零工式生產排程問題（Distributed Flexible Job Shop Scheduling Problem, DFJSP），為求解此NP-hard問題，本研究提出兩種混合基因簡化群體演算法（Hybrid Genetic Simplified Swarm Optimization, HGSSO），HGSSO結合簡化群體演算法（Simplified Swarm Optimization, SSO）之彈性更新機制與搜索空間記憶能力以強化基因演算法（Genetic Algorithm, GA）搜索能力，並運用兩種啟發式演算法針對DFJSP問題決策進行解碼，幫助縮小搜索空間加快找到高品質解，其中HGSSO2為首度將DFJSP運用事先分群技巧拆解為多個FJSP的求解方法，透過此種降維處理方式，能更大幅度縮小搜索空間以加快求解效率，而分群技巧則成為了此方法之關鍵要素，本研究發展出兩種分群策略並將HGSSO2算法使用DFJSP中71種資料集與過往研究進行比較，結果顯示在解品質上皆能優於其他算法。

Production scheduling is based on factors such as production type, processing time, production constraints, and objectives. Many methods have been developed to solve the problem of various forms of scheduling. Most of these studies are aimed at centralized manufacturing systems. When factors such as the continuous expansion of the company's territory and the complexity of the supply chain occur, the decentralized manufacturing system has gradually become the mainstream. Because of its decentralized nature, it has the advantages of risk dispersion, proximity to the commercial marketing market and raw material supply, which can help enterprises Create higher value business interests.
For distributed manufacturing systems, the more complex Distributed Flexible Job Shop Scheduling Problem (DFJSP) is rarely discussed in previous studies. To solve this NP-hard problem, this study proposes two hybrid algorithm. Hybrid Genetic Simplified Swarm Optimization (HGSSO), HGSSO combines the elastic update mechanism and search space memory capability of Simplified Swarm Optimization (SSO) to strengthen the Genetic Algorithm (GA) search capability, and Two heuristic algorithms are used to decode the DFJSP problem decision, which helps to narrow the search space and speed up the finding of high-quality solutions. Among them, HGSSO2 is the first solution method to disassemble DFJSP into multiple FJSPs using pre-grouping techniques. Through this dimensionality reduction The processing method can greatly reduce the search space to speed up the solution efficiency, and the clustering technique has become the key element of this method. This study developed two clustering strategies and used the HGSSO2 algorithm to compare with previous research on 71 data sets in DFJSP. The results show that the solution quality is better than other algorithms.

摘要 i
Abstract ii
目錄 iv
表目錄 vi
圖目錄 viii
第一章、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究架構 3
第二章、文獻回顧 5
2.1排程問題各類變化形式 5
2.2分散式彈性零工式排程問題(DFJSP) 7
2.3 基因演算法(Genetic Algorithm, GA) 9
2.4 簡化群體演算法(Simplified Swarm Optimization) 14
第三章、問題描述 15
3.1數學符號 16
3.2 數學模型 17
3.3 研究假設 18
第四章、研究方法 21
4.1 編碼 21
4.2 解碼 23
4.3 混合基因簡化群體演算法(HGSSO) 32
4.4 工件分群策略 42
4.5　HGSSO應用於DFJSP流程 45
第五章、實驗結果與分析 47
5.1 實驗資料集 47
5.2 演算法參數實驗設計 48
5.3 實驗結果比較 54
第六章、結論與未來研究方向 67
6.1 結論 67
6.2 未來發展 68
參考文獻 69

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