科技不斷演進，互聯網、物聯網和人工智能…等等新興技術蓬勃發展，車聯網、智慧城市和智慧工廠…等等新型態應用環境不斷產生，伴隨而來數據的大量增加及對於即時回應的要求提高，霧計算概念便在此背景中誕生。霧計算作為雲計算概念的延伸，最早是在2011年由思科(Cisco)公司提出，在霧計算的模式中，資料將不再集中於雲端處理，而是分散到網路邊緣的設備中，不像雲計算需要性能強大的計算機進行集中化的運算，網路邊緣的單一設備不需要太強大的性能，但依賴其分散運算的效果，使其依然可以達成快速地處理資料之需求。
本研究根據過往文獻提出兩種不同模型，第一種模型是考量多樣感測設備的多階層式設施佈署問題模型，第二種模型是延續第一種模型並提出考量霧計算裝置彼此可相連傳輸的多階層式設施佈署問題模型。而設施佈署問題已被證明是NP-Hard問題，故本研究採用啟發式演算法來進行求解。本研究提出一新興的混合算法稱為混合簡化群體正餘弦演算法(Simplified Swarm Optimization-Sine Cosine Algorithm, SSO-SCA)，另外也使用其他著名啟發式演算法如:人工蜂群演算法(Artificial Bee Colony, ABC)、基因演算法(Genetic Algorithm, GA)及二進制粒子群演算法(Binary Particle Swarm Optimization, BPSO)及簡化群體法(Simplified Swarm Optimization, SSO)來進行結果比較及探討。
本研究的目的是在高度限制的整數規劃模型下，利用啟發式演算法在合理時間內找出成本較低之佈署方案。研究假設一智能工廠若考慮佈署雲/霧混和計算系統，主要決策佈署之裝置為閘道、霧計算裝置及邊緣裝置位置，同時提出三種不同規模大小問題來進行實驗，以期能找出最有效之方法幫助決策者建置智能工廠最佳雲/霧混和計算系統佈署。

Technology is constantly evolving, and emerging technologies such as the Internet of Things (IoT), and Artificial Intelligence(AI) are booming. In addition, new forms of application environments such as Internet of Vehicle (IoV), Smart Cities and Smart Factories are steadily appearing. With the large increase in data and the increased demand for immediate response, the concept of fog computing was born. Fog computing was first proposed by Cisco in 2011. In fog computing, data processing will no longer be concentrated in the cloud, but distributed to devices at the edge of the network. Devices at the edge of network don’t need powerful performance, but relying on the effects of their decentralized computing, the system can still process data quickly.
This study proposes two different models based on the previous literature. One is a Hierarchical Facility Location Problem model that considers multiple sensing devices. Another extends the first model and considers that the fog computing devices can be connected to each other. The Facility Location Problem has been proved as an NP-Hard problem, so this study uses the heuristic algorithms to solve it. This study proposes an novel algorithm called Simplified Swarm Optimization-Sine Cosine Algorithm (SSO-SCA), and other well-known heuristic algorithms such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Simplified Swarm Optimization (SSO) are also used to compare the results. The purpose of this study is to use heuristic algorithms to find min-cost deployment under highly restricted model in reasonable time. It is expected to find the most effective way to help decision makers build hybrid cloud/fog computing system in smart factories.

摘要 I
Abstract II
圖目錄 VI
表目錄 VIII
第1章 、緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 研究架構及流程 5
第2章 、文獻回顧 7
2.1 智慧工廠及雲/霧混和計算系統 7
2.2 傳統多階層選址問題及雲/霧混和計算系統佈署問題 7
2.3 啟發式演算法 8
2.3.1 人工蜂群演算法(Artificial Bee Colony, ABC) 9
2.3.2 基因演算法(Genetic Algorithm, GA) 9
2.3.3 二進制粒子群演算法(Binary Particle Swarm Optimization, BPSO) 11
2.3.4 簡化群體法(Simplified Swarm Optimization, SSO) 11
2.3.5 正餘弦演算法(Sine Cosine Algorithm, SCA) 12
第3章 、問題描述 14
3.1 環境架構及假設 14
3.1.1 環境架構 14
3.1.2 問題假設 15
3.2 符號定義 18
3.3 數學模型 21
3.3.1 模型一－原參考文獻模型 21
3.3.2 模型二－考量霧計算裝置互通性 24
3.4 實驗問題 27
第4章 、研究方法 29
4.1 編碼方式 30
4.1.1 編碼介紹 30
4.1.2 連結變數訂定方式 30
4.1.3 編碼範例 32
4.2 初始解產生 33
4.3 啟發式演算法 34
4.3.1 人工蜂群演算法(Artificial Bee Colony, ABC) 34
4.3.2 基因演算法(GA) 39
4.3.3 二進制粒子群演算法(BPSO) 44
4.3.4 簡化群體法(SSO) 48
4.3.5 混合簡化群體正餘弦演算法(SSO-SCA) 52
4.4 適應值函數計算步驟 59
第5章 、實驗結果與分析 61
5.1 實驗問題參數 61
5.2 演算法參數設定與實驗設計 63
5.2.1 簡化群體法實驗參數設計 63
5.2.2 比較之啟發式演算法參數設定 68
5.3 實驗結果 69
5.3.1 啟發式演算法實驗結果分析 69
5.3.2 相同時間設定下啟發式演算法實驗結果 74
5.3.3 模型一及模型二決策比較 78
第6章 、結論與未來研究方向 82
6.1 結論 82
6.2 未來研究方向 83
參考文獻 84
附錄 87

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