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Student Number 965303019 Author Tsung-Hsun Wu(吳宗勳) Author's Email Address No Public. Statistics This thesis had been viewed 1452 times. Download 0 times. Department Executive Master of Communication Engineering Year 2008 Semester 2 Degree Master Type of Document Master's Thesis Language zh-TW.Big5 Chinese Title Particle Swarm Optimization Algorithm Applied to the Telecom Industry for Total Solution Partner Selection and Project Scheduling Optimization Date of Defense 2009-06-26 Page Count 83 Keyword Fuzzy Decision JSP Partner PSO Swarm Intelligence Total Solution Abstract The Telecommunications market has many competitor in Taiwan, result in profits decline. The carrier in order to provide customized services for “Key Account and Small Medium Enterprise Customers”, these carrier create a “one-stop-shopping” Selling model, it also call “Total Solution”. Total Solution must integrate a number of business partners for related implement services, and they can provide the best solution to the customer. The purpose of this thesis that can select best partners after carrier obtain customer official orders, and focus the construction project improve a shortest implement time and the lowest labor costs, and according to the project scheduling to steps by steps implement and build these engineering.

The Thesis is based on the concept of Swarm Intelligence, it call “Particle Swarm Optimization (PSO)” Algorithm. PSO Algorithm contains rapid convergence characteristics. In recent years, it is a swarm intelligence algorithm for applied to solve some optimization question. PSO emphasis on inter-particle communication and it has some advantage, for example, low parameters setting, fast to search and feasibility of high-speed, etc. Now it has many scholars that has been published with PSO related improvement algorithms associated with such a practical application.

The thesis was divided into three parts; the first part of the first to introduce the concept of swarm intelligence and then introduce more popular optimization algorithm at present in several academic organization, and based on PSO optimization algorithm to do a simple comparison algorithm with others. The second part, we will focus on basic concept of the PSO and all kind of the improvement PSO algorithm description its further concepts and variety. The third part is the combination of the use of improved PSO concept and fuzzy decision-making, according to Total Solution executing partner election and total cost ownership optimization after the carrier get the orders, and use values of multi status to proof PSO/FD can be made more efficient partner selection of the optimal solution to the problem, and then we used PSO combine “Job-shop Scheduling Problem(JSP)” strategies for the project during construction period, to optimize the scheduling in order to shortest the construction period, and it can achieve cost savings. Final chapter we can propose some conclusions and face to further improvement PSO, and propose future study and some related research suggestions.Table of Content 摘 要

Abstract

誌 謝

目 錄

圖 目 錄 List of Figures

表 目 錄 List of Tables

第一章 緒 論1

1-1 研究背景與動機1

1-2 論文架構與流程3

第二章 數種優化演算法之介紹與比較5

2-1 群體智能之概念5

2-2 優化演算法介紹7

2-2-1 基因演算法7

2-2-2 模擬退火演算法10

2-2-3 螞蟻群優化演算法12

2-3 數種演算法之比較16

2-3-1 PSO演算法初探16

2-3-2 PSO與基因演算法(GA)的比較17

2-3-3 PSO與模擬退火法(SA)之比較19

第三章 粒子群優化演算法及進階研究20

3-1 粒子群優化演算法20

3-2 PSO之改進演算法之發展27

3-3 PSO整合模糊決策30

3-4 PSO整合JSP31

第四章 PSO應用於電信業解決方案之優化35

4-1 台灣電信產業近年之發展35

4-2 電信業解決方案(Total Solution)之銷售模式38

4-3 選商問題之優化42

4-3-1 問題假設與定義42

4-3-2 實測結果及分析52

4-4 專案排程問題之優化59

4-4-1 問題定義及模型59

4-4-2 實測結果及分析60

第五章 結論與未來展望64

5-1 結論64

5-2 未來展望66

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965303019.pdf Date of Submission 2009-07-07

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