臺灣博碩士論文加值系統

本研究為多目標演算法研究，利由ε-constraint方法結合敏感度分析，目標為找出足夠變化並且具有其代表性的非劣等解給決策者。利用一線性模式做為範例(主要目標式為最小化延遲成本，目標限制式為最小化分配預算成本)，發展一新演算法進行求解，發現利用此演算法可快速進行求解，並將求解時間快速縮短。

This research proposes a multi-objective algorithm using the ε-constraint method combining sensitivity analysis. The target is to find adequate representation of non-inferiorsolutions to decision maker. As an example, the linear model (primarily object to minimize delay costs, the object of constraint to minimize the cost of allocate budgets). This research finds that the use this algorithm solve the problem quickly.

目錄
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究架構 2
第二章 文獻探討 3
2.1 多目標規畫： 3
2.1.1 權重法(weight method) 5
2.1.2 ε限制法(ε-constraint method) 6
2.2. 多目標應用相關文獻 8
第三章 模式建構 10
3.1 演算法建構 10
3.1.1. 敏感度分析 10
3.2 演算法流程圖 16
3.3 問題敘述與符號說明 25
3.4. 數學模式 26
3.5 模式說明與ε-constraint model 27
第四章 數值分析 28
4.1. 方法一與方法二數據設定 29
4.2 各組實驗結果 30
4.3. 求解時間比較 47
結論 49
參考資料 50
附錄一 (R=0.25) 51
附錄二 (R=0.5) 54
附錄三 (R=0.75) 56
附錄四 (τ=0.25) 59
附錄五 (τ=0.5) 62
附錄六 (τ=0.75) 64
附錄(原始資料) 67

1.Birman, M. and Mosheiov, G. (2004). A note on a due-date assignment on a two-machine flow-shop. Computers and Operations Research, 頁 31, pp.473-480.
2.Chang, P.-C., Hsieh, J.-C. and Lin, S.-G. (2002). The development of gradual-priority weighting approach for the multi-objective flowshop scheduling problem. International Journal of Production Economics, 頁 79, pp.171-183.
3.CohonL.Jared. (2003). Multiobjective programming and planning.
4.FisherL.M. (1976). A dual problem for the one machine scheduling problem. Mathematics Programming 11, 頁 pp.229-251.
5.Haimes.Y. (1973). Control and Dynamic systems: Advances in Theory and Applications. Integrated System Identification and Optimization, 頁 Vol. 9, pp435-518.
6.Lee. ( 2001). Artificial intelligence search methods for multi-machine two-stage scheduling with due date penalty, inventory, and machining costs. Computers and Operations Research, 頁 pp.835-852.
7.Petrovic, D., Duenas, A., and Petrovic, S.,. (2007). Decision support tool for multi-objective job shop scheduling problems with linguistically quantified decision functions. Decision Support Systems, 頁 pp.1527-1538.
8.Varadharajan, T.K., Rajendran, and Chandrasekharan. (2005). A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs. European Journal of Operational Research, 頁 Vol. 167, No. 3, pp. 772-795,.
9.何友鋒,林建宇,王小磷. (1996). 住宅社區多目標規劃之研究. 中華民國設計學報, 頁 88-86.
10.林正中. (民95年12月). 基因演算法於多目標車輛途程問題之應用. 智慧科技與應用統計學報, 頁 19-48.
11.許志義. (2003). 多目標決策. 五南圖書出版社.