整合生產排程與車輛途程之問題可分為兩部分問題，其一為生產之排程問題，考慮機台與訂單如何於廠內有效規劃生產順序；其二為送貨之車輛途程問題，針對訂單的指定地點與車輛路徑上亦須有效安排，但在整合生產排程與車輛途程問題中為了使其更接近接近實際情形，本研究將訂單送達指定地點上提供一時間窗限制，欲滿足客戶的需求，整合兩部分問題須同步考量。
本研究是以最小化總加權延遲時間且為配送車隊，為單一場站內完全相同平行機台、單一車輛種類、多車輛、多個需求、時窗限制和路徑時間等的整合生產排程與車輛途程問題，此類型問題屬於NP-hard問題，故本研究嘗試運用布穀鳥演算搜尋法、和粒子群搜尋法改善尋找最佳解的過程，分析各求解品質及求解過程，不單是為整合生產排程與車輛途程問題增加求解工具之選擇並期待有更佳的搜尋品質。

In this study, we focus on solving the integrating problem of parallel machine scheduling and vehicle routing with the objective of minimizing the total weighted tardiness time. We coordinate the production sequence in identical parallel machines and delivery routes in identical vehicles simultaneously after accepting the customers’ order requests. Both of the cuckoo search and particle swarm optimization algorithm are developed to find the optimal solution. In order to intensify the capability of cuckoo search, cuckoo search and particle swarm optimization algorithm into variable neighborhood search and compare their solving effects.
In experimental analysis, two hybrid meta heuristics including cuckoo search hybrid with variable neighborhood searches and particle swarm optimization algorithms embedded with the proposed variable neighborhood searches are implemented to obtain their solving effects in different sizes of problems. The numerical results show that the embedded particle swarm optimization algorithm can obtain better objective values.

摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VII
第一章、緒論 1
1.1研究背景 1
1.2研究動機與目的 2
1.3研究架構 3
第二章、文獻回顧 5
2.1 整合問題相關文獻 5
2.1.1 供應鏈型態之整合 5
2.1.2 生產與配送型態整合 6
2.1.3 整合生產排程與車輛途徑相關文獻 8
2.2 演算法相關文獻 16
2.2.1 變動鄰域搜尋法 16
2.2.2 粒子群演算法 17
2.2.3 布穀鳥搜尋法 19
第三章、研究議題與數學模型 21
3.1問題描述 21
3.2問題假設 21
3.3 數學模型 23
第四章、演算法建置與執行 27
4.1程式起始編碼 27
4.2 鄰域結構 29
4.3 變動鄰域搜尋法 32
4.4 改良數值編碼方式 35

4.4.1迴圈型數值編碼(Cycle Value) 35
4.4.2浮點數型編碼(Float Value) 36
4.5 混合啟發式演算法 37
4.5.1 布穀鳥搜尋法混合變動鄰域搜尋法(Hybrid CS/VNS) 38
4.5.2 粒子群搜尋法混合變動鄰域搜尋法(Hybrid PSO/VNS) 41
第五章、實驗設計與結果 45
5.1 實驗參數設置 46
5.1.1問題參數上、下界設定値 46
5.1.2粒子群演算法混合變動鄰域搜尋法參數設定 47
5.1.3 布穀鳥搜尋法混合變動鄰域搜尋法參數最佳化實驗 48
5.2兩種演算法於小型問題上比較 51
5.3不同編碼方式之演算法於大型問題上比較 52
5.3.1三種不同編碼方式之布穀鳥演算法比較 53
5.3.2三種不同編碼方式之粒子群演算法比較 53
5.4三種粒子群演算法與三種布穀鳥演算法比較 54
第六章、結論與未來研究 57
參考文獻 59
附錄 63

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