粒子群優化演算法應用於電信業解決方案選商及專案排程之優化

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姓名

吳宗勳(Tsung-Hsun Wu) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

粒子群優化演算法應用於電信業解決方案選商及專案排程之優化
(Particle Swarm Optimization Algorithm Applied to the Telecom Industry for Total Solution Partner Selection and Project Scheduling Optimization)

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摘要(中)

這幾年電信產業因為競爭激烈導致利潤大幅下降，為了提供給企業客戶客製化的服務，故興起了一站式銷售，又稱作解決方案(Total Solution)的風氣；Total Solution必須整合多家事業夥伴之相關服務，方能提供給客戶最佳解決方案。所以，如何以最有效率的方式選擇這些Partner(又稱為選商)，並於正式取得客戶訂單後，以最短之施工時間及最低之人力成本，依據專案排程陸續完成所需建置之相關標的物，便是本論文可以再深入研究的部份，也是本篇論文研究的目的。
本論文研究主要是以擁有群體智能(Swarm Intelligence，SI)概念的粒子群優化演算法(Particle Swarm Optimization，PSO)為基礎。粒子群優化演算法具有收斂快速的特性，是近年應用以解決最佳化問題的一種群體智能演算法，PSO之特點為強調粒子間訊息的溝通，並有參數設定少、搜尋速度快和可行性高的優點，這幾年國內外已有多位學者及先進陸續發表與此種演算法相關之實務應用。
本論文區分為三個部份；第一部分先介紹群體智能之概念，並進而介紹目前學術界較熱門的幾種群體優化演算法並與PSO演算法做簡單比較；第二部份再針對PSO基礎概念及各種改良型PSO做整體性之闡述及相關性之延伸；第三部份是運用改良型PSO組合模糊決策(Fuzzy Decision)之概念，針對Total Solution取得訂單後之選商以及總投資成本進行優化，以多種不同狀態之數值證明PSO/FD可較有效率的取得選商問題的最佳解，另再以PSO結合零工式排程問題(JSP)之理論，針對施工期間的專案排程部分進行優化以縮短工期，也可達到節省成本之目的；最後章節再作總結及提出對改良型PSO研究之未來展望及相關研究之建議。

摘要(英)

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.

關鍵字(中)

★ 一站式銷售
★ 解決方案
★ 群體智能
★ 選商
★ 粒子群優化演算法
★ 模糊決策
★ 零工式排程問題

關鍵字(英)

★ JSP
★ Fuzzy Decision
★ PSO
★ Swarm Intelligence
★ Partner
★ Total Solution

論文目次

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JSP 31
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
參考文獻 67

參考文獻

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指導教授

賀嘉律(Chia-Lo Ho)

審核日期

2009-7-7

推文