應用基因演算法於多部門多重專案選擇與排程問題

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陳貝生(Pei-Sheng Chen) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

應用基因演算法於多部門多重專案選擇與排程問題
(A GA-based Approach for Solving Project Selection and Scheduling Problems in a Multiple-Department Environment)

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摘要(中)

中文摘要：
專案選擇與排程問題至目前已被深入的探討，但探討的觀點卻只著重在單一部門的環境，至於多部門的環境至目前並沒有被深入的著墨。有鑑於此，此篇論文考慮在多部門環境中的專案選擇與排程問題，並以公司整體利益最大化為主要的目標函數，發展此問題的數學模型。
而專案選擇與排程問題本質上屬於一種組合問題，以解析式的方法來解決組合問題當問題的複雜度增加時，求解的時間必定以指數型的方式漸增。因此，我們試著提出可以解決組合問題的啟發式方法—基因演算法，來解決多部門環境中的專案的選擇與排程問題。
在模擬實驗的過程中，田口方法會被使用於所發展的基因演算法中主要參數的決定，而所模擬出來的結果，會在目標函數值與執行時間此兩個比較維度下與用解析式方法(AMPL modeling language)所得到的結果做比較。

摘要(英)

Abstract:
Before implementing projects, project selection is a very important proceeding work. An organization can’t possibly implement all coming projects because of some limitations such as budgets and human resources. Every project selection result sifting from candidate projects in an organization can be called a “project portfolio”.
Further, after getting a project portfolio in an organization, deciding when to implement (schedule) these selected projects is also an important issue. By scheduling, we can make the resource consumption in each period satisfies the budget constraints. In reality, organizations in a firm such as departments are facing this problem. Multiple departments have multiple candidate projects to choose in a company. How to decide the project portfolio in each department so as to gain the overall maximum profit in a firm? This is a very complex problem in reality.
Although project selection and scheduling problem has been discussed in depth and several related models have been proposed, none of them discussed this problem in a multiple-department environment. For this reason, our paper focuses on the project selection and scheduling problem in a multiple-department environment. Owing to project selection and scheduling problem belongs to a typical combination problem, we try to propose a problem-specific genetic algorithm in project selection and scheduling problem to find a satisfactory result in our paper.
In addition, due to deciding the parameters of the proposed genetic algorithm are necessary, Taguchi method will be applied in the process for deciding the most suitable parameters.

關鍵字(中)

★ 基因演算法
★ 田口方法
★ 專案組合
★ 專案選擇與排程

關鍵字(英)

★ Project portfolio
★ Project selection and scheduling
★ Genetic algorithm
★ Taguchi method

論文目次

Table of contents
Abstract in Chinese I
Abstract II
Table of contents V
Figures VII
Tables VIII
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Problem statement 2
1.3 Research objective 4
1.4 Research framework 5
Chapter 2 Literature review 6
2.1 Project portfolio 6
2.2 Project selection 7
2.2.1 Project selection without scheduling function 8
2.2.2 Project selection with scheduling function 10
2.3 Genetic algorithms 11
2.3.1 Basic principles of genetic algorithms 12
2.3.2 A general procedure of genetic algorithms 15
2.3.3 The reason of applying genetic algorithms 17
2.4 Taguchi method 17
Chapter 3 The proposed model 19
3.1 Problem formulation 19
3.1.1 Decision variables 20
3.1.2 Objective function 20
3.1.3 Total budget constraints 21
3.1.4 Department budget constraints 21
3.1.5 Period budget constraints 21
3.1.6 Completing time constraints 22
3.1.7 Technical interdependence constraints 22
3.1.8 Risk-related constraints 23
3.1.9 Other constraints 23
3.2 A genetic algorithms for project selection and scheduling in multi-
department 26
3.2.1 Direct representation of problem 26
3.2.2 Initialization 29
3.2.3 Fitness function 30
3.2.4 Elitism 31
3.2.5 Crossover 31
3.2.6 Mutation 31
3.2.7 New generation 32
3.3 Outline of the proposed model 34
Chapter 4 Simulation and result analysis 36
4.1 The experimental environment and design 36
4.2 Case generator 38
4.3 Experimental results 39
4.3.1 The generated cases 40
4.3.2 Experiment results 41
4.4 Comparison with AMPL modeling language 48
4.5 Application for large-scale problem 50
Chapter 5 Conclusions and suggestions 57
5.1 Conclusions 57
5.2 Further research suggestions 58
Reference 59
Appendix A The generated project cases for Case 1 to Case 6 62
Appendix B The data of Taguchi method process of each case 86
Appendix C The APML model file 96
Appendix D The results solved by AMPL for each case 99

參考文獻

Reference
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指導教授

何應欽、曾清枝
(Ying-Chin Ho、Ching-Chih Tseng)

審核日期

2005-7-5

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