發(fā)布時(shí)間：2017-09-12 23:32

  本文關(guān)鍵詞：云制造的服務(wù)聚集、組合與調(diào)度優(yōu)化方法

  更多相關(guān)文章： 云制造 云服務(wù) 制造資源聚集 云服務(wù)組合 云服務(wù)調(diào)度 人工蜂群優(yōu)化算法

【摘要】：近年來(lái),服務(wù)計(jì)算、云計(jì)算、物聯(lián)網(wǎng)等眾多新型信息技術(shù)的不斷涌現(xiàn),使虛擬企業(yè)之間能夠更加敏捷、智能地協(xié)作。在此背景下,云制造作為一種促進(jìn)中國(guó)制造業(yè)由生產(chǎn)型制造向服務(wù)型制造轉(zhuǎn)變的發(fā)展途徑被提出。這一面向服務(wù)的網(wǎng)絡(luò)化制造新模式將進(jìn)一步促進(jìn)中國(guó)制造企業(yè)向網(wǎng)絡(luò)化、智能化和服務(wù)化方向發(fā)展,從而更好地實(shí)現(xiàn)制造資源與制造能力的網(wǎng)絡(luò)化動(dòng)態(tài)共享與智能分配。作為云制造的核心,云制造服務(wù)能夠動(dòng)態(tài)地按需提供虛擬制造鏈的創(chuàng)建所需的制造資源或制造能力服務(wù),支持跨企業(yè)的服務(wù)型制造的全生命周期,從而成為一種新的資源組織和集成方式。研究云制造服務(wù)的聚集、組合、調(diào)度等服務(wù)處理的優(yōu)化方法是十分必要的。然而,云制造是一個(gè)新興領(lǐng)域,其理論和應(yīng)用研究都剛剛起步,尚不夠成熟。為此,本文圍繞云制造的服務(wù)處理模型和基于聚集的云制造框架來(lái)開展研究。該研究覆蓋了三個(gè)核心服務(wù)處理過程:制造資源的聚集、云制造服務(wù)的組合和制造云服務(wù)的調(diào)度。其核心思想是在充分考慮現(xiàn)實(shí)約束的前提下,通過創(chuàng)新的方法/算法來(lái)提高服務(wù)處理的效率,優(yōu)化制造云服務(wù)的運(yùn)作。本文的研究工作和創(chuàng)新成果包括以下幾點(diǎn)。(1)提出了基于密度的、支持云制造服務(wù)分解模型的資源聚集方法。由于制造資源的大規(guī)模海量特點(diǎn),對(duì)這些資源的聚集策略成為云制造服務(wù)處理的重要基礎(chǔ)。其目的是對(duì)具有相似功能參數(shù)的制造資源進(jìn)行劃分,并根據(jù)分布密度將這些制造資源分為多個(gè)服務(wù)集群。為此,本文采用服務(wù)空間模型改進(jìn)了基于密度的和帶有噪聲的應(yīng)用空間聚集算法DBSCAN(Density-Based Spatial Clustering of Applications with Noise),提出了一種新的基于密度的聚集策略,實(shí)現(xiàn)了制造資源服務(wù)的有效聚集。(2)通過擴(kuò)展服務(wù)的初始聚集結(jié)構(gòu)并引入制造資源選擇的經(jīng)驗(yàn)知識(shí),提出了一種服務(wù)集群重構(gòu)方法。該方法基于制造資源間具有配套關(guān)系、常常被一起選擇這一事實(shí),利用概率密度函數(shù)刻畫資源被一起選擇的概率,進(jìn)而重構(gòu)和優(yōu)化服務(wù)集群的結(jié)構(gòu)。本文采用基于人工蜂群的優(yōu)化算法(Artificial Bee Colony Optimization Algorithm,ABC)從現(xiàn)有的集群結(jié)構(gòu)中搜索并發(fā)現(xiàn)最有效的解。(3)云制造服務(wù)組合是制造云(虛擬制造鏈)構(gòu)建的核心。雖然目前已有大量的服務(wù)組合方面的研究,但由于未充分考慮制造的領(lǐng)域特征,因而并不適用。在云制造中,運(yùn)輸活動(dòng)和制造互操作處理是制造服務(wù)組合過程中的關(guān)鍵;還需考慮服務(wù)質(zhì)量的匹配問題。為此,本文在充分考慮服務(wù)質(zhì)量、運(yùn)輸活動(dòng)、制造互操作以及Qo S不匹配替代策略的基礎(chǔ)上,提出了云制造服務(wù)的組合框架,設(shè)計(jì)了基于改進(jìn)ABC算法的制造資源服務(wù)組合優(yōu)化方法(ABC_Cs CCMfg)。(4)任務(wù)調(diào)度是協(xié)同虛擬制造鏈運(yùn)作管理中的復(fù)雜問題。考慮到現(xiàn)行制造企業(yè)多依賴于對(duì)調(diào)度和組織模型的長(zhǎng)期承諾,亟需一種面向云制造模式的調(diào)度框架,在保證制造服務(wù)商的完整性/運(yùn)作穩(wěn)定性前提下,優(yōu)化全局時(shí)間/交貨期。為此,本文提出基于可用性和時(shí)間段分析的制造服務(wù)任務(wù)調(diào)度編排模型,并基于人工蜂群算法實(shí)現(xiàn)了云制造服務(wù)的調(diào)度優(yōu)化。(5)本文給出了一個(gè)應(yīng)用案例。在此案例中,ASEM公司HT700產(chǎn)品的制造需要轉(zhuǎn)換為以集群為基礎(chǔ)的云制造模式。通過資源聚集、組合等過程,我們?yōu)镠T700構(gòu)造了一個(gè)虛擬制造鏈,讓人感受到云制造為中小型企業(yè)帶來(lái)的效益。
【關(guān)鍵詞】：云制造 云服務(wù) 制造資源聚集 云服務(wù)組合 云服務(wù)調(diào)度 人工蜂群優(yōu)化算法
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP393.09
【目錄】：

• 摘要4-6
• ABSTRACT6-20
• ABBREVIATIONS20-22
• CHAPTER 1 INTRODUCTION22-56
• 1.1 BACKGROUND AND MOTIVATION22-29
• 1.2 CLOUD MANUFACTURING AND RELATED ISSUES29-36
• 1.2.1 Cloud Manufacturing Concept29-32
• 1.2.2 Key Advantages of Cloud Manufacturing32-33
• 1.2.3 Manufacturing Resource Service Clustering33-34
• 1.2.4 Manufacturing Resource Service Composition34-35
• 1.2.5 Manufacturing Resource Service Scheduling35-36
• 1.3 SURVEY OF THE STATE OF THE ART OF CLOUD MANUFACTURING AND THERELATED KEY TECHNOLOGIES36-50
• 1.3.1 Key Technologies of Cloud Manufacturing36-45
• 1.3.2 Resources Clustering45-46
• 1.3.3 Service Composition46-48
• 1.3.4 Manufacturing Tasks Scheduling48-50
• 1.4 RESEARCH PROBLEMS & OBJECTIVES50-53
• 1.4.1 Manufacturing Resource Service Clustering51
• 1.4.2 Manufacturing Resource Service Clusters Re -structuring51-52
• 1.4.3 Cloud Manufacturing Service Composition52-53
• 1.4.4 Cloud Manufacturing Service Scheduling53
• 1.5 MAIN CONTENT OF THE THESIS53-56
• CHAPTER 2 CLUSTER BASED CLOUD MANUFACTURING FRAMEWORKAND KEY TECHNOLOGIES56-71
• 2.1 CLUSTER BASED CLOUD MANUFACTURING FRAMEWORK & DEFINITION56-58
• 2.2 SERVICE PROCESSING IN CLOUD MANUFACTURING58-61
• 2.3 KEY TECHNOLOGIES FOR SERVICE PROCESSING OPTIMIZATION IN CLOUDMANUFACTURING61-69
• 2.3.1 Quality of Services in Cloud Manufacturing61-63
• 2.3.2 Geo-perspective model63-64
• 2.3.3 Composition models64
• 2.3.4 Artificial Bee Colony Optimization64-67
• 2.3.5 Density-Based Spatial Clustering of Applications with Noise67-69
• 2.4 SUMMARY69-71
• CHAPTER 3 MULTI-DIMENSION DENSITY-BASED CLUSTERING WITH THE SUPPORT OF CLOUD MANUFACTURING SERVICE DECOMPOSITIONMODEL71-80
• 3.1 PROBLEM STATEMENT71
• 3.2 CMFG CLUSTERING FRAMEWORK71-72
• 3.3 RESOURCE SPACE DEFINITION72-74
• 3.3.1 CMfg service Decomposition Model72-73
• 3.3.2 Resource Space Definition Pre-sets73-74
• 3.4 Modified DBSCAN Algorithm for CMfg Clustering Framework74-77
• 3.5 CMFG_DBSCAN() EXPERIMENTS77-79
• 3.6 SUMMARY79-80
• CHAPTER 4 A RE-STRUCTURING SERVICE CLUSTER ALGORITHM ABCOPTIMIZED BASED ON VIRTUAL RESOURCE SELECTION PROBABILITY80-98
• 4.1 PROBLEM STATEMENT: AN EXTENDED VIEW OF CMFG CLUSTERINGFRAMEWORK80-82
• 4.2 SERVICE CLUSTER RE-STRUCTURATION TRIGGER FUNCTION82-84
• 4.2.1 Structure Efficiency and Cost Evaluation83
• 4.2.2 Service cluster Selection Experience83
• 4.2.3 Restructuring Trigger Function83-84
• 4.3 SERVICE CLUSTERS AND VIRTUAL RESOURCES SELECTION PROBABILITY84-88
• 4.4 SERVICE CLUSTERS FITNESS EVALUATION88-89
• 4.5 RE-STRUCTURING SERVICE CLUSTERS ALGORITHM ABC OPTIMIZED89-91
• 4.5.1 ABC Control Parameters89
• 4.5.2 Re-structuring Service Clusters Algorithm ABC Optimized89-91
• 4.6 PRECISION AND PERFORMANCE EVALUATION91-96
• 4.6.1 Precision Evaluation92-94
• 4.6.2 Performance Evaluation94-96
• 4.7 SUMMARY96-98
• CHAPTER 5 AN OPTIMIZED CLOUD MANUFACTURING SERVICECOMPOSITION BASED ON QOS AND GEO-PERSPECTIVE98-124
• 5.1 PROBLEM STATEMENT98-99
• 5.1.1 Sets and Model variables98
• 5.1.2 Cloud Service Composition Problem Definition98-99
• 5.2 CLOUD MANUFACTURING SERVICE COMPOSITION FRAMEWORK99-100
• 5.3 QOS EVALUATION100-102
• 5.3.1 Sequence Model100-101
• 5.3.2 Parallel Model101
• 5.3.3 Selective Model101
• 5.3.4 Circular Model101-102
• 5.4 SUBSTITUTION STRATEGY FOR UNMATCHED QOS102-104
• 5.5 RESOURCE SERVICE TRANSPORTATION EVALUATION104-108
• 5.5.1 Mean of Transportation Selection105-107
• 5.5.2 Transportation Distance Evaluation107
• 5.5.3 Resource Service Transportation Qo S Evaluation107-108
• 5.6 INTEROPERABILITY CONNECTOR EVALUATION108-109
• 5.6.1 Interoperability in Manufacturing108
• 5.6.2 Interoperability Vision108-109
• 5.7 CLOUD SERVICE COMPOSITION FITNESS DEFINITION109-111
• 5.8 IMPROVED ARTIFICIAL BEE COLONY FOR CLOUD MANUFACTURING SERVICECOMPOSITION EVALUATION111-114
• 5.9 SIMULATIONS114-123
• 5.9.1 ABC_Cs CCMfg Parameters Tuning114
• 5.9.2 Case Generation for ABC_Cs CCMfg Simulations114-116
• 5.9.3 ABC_Cs CCMfg Performance Evaluation116-119
• 5.9.4 ABC_Cs CCMfg Precision Evaluation119-121
• 5.9.5 Transportation Evaluation Impact121
• 5.9.6 CS candidates Availability Impact121-122
• 5.9.7 Interoperability Performance Impact122-123
• 5.10 SUMMARY123-124
• CHAPTER 6 OPTIMIZED SCHEDULING FRAMEWORK BASED ONRESOURCE SERVICE AVAILABILITY124-135
• 6.1 PROBLEM STATEMENT124
• 6.2 CONSTRAINTS DEFINITION124-126
• 6.3 TIMESLOTS AND AVAILABILITY OVERTIME DEFINITION126-128
• 6.4 FITNESS EVALUATION BASED ON MANUFACTURING STARTING TIME128-130
• 6.5 CLOUD MANUFACTURING SCHEDULING ORCHESTRATION130-131
• 6.6 EXPERIMENTS131-134
• 6.6.1 ABC Control Parameters131
• 6.6.2 Performance Evaluation131-133
• 6.6.3 Manufacturing Time Evaluation Impact133-134
• 6.7 SUMMARY134-135
• CHAPTER 7 ASEM USE CASE: SERVICE CLUSTER GENERATION ANDCLOUD MANUFACTURING SERVICE COMPOSITION INTEGRATION135-150
• 7.1 INTRODUCTION135
• 7.2 ASEM PRESENTATION135-137
• 7.3 ASEM HT700137
• 7.4 HT700 CLOUD MANUFACTURING SERVICE MODEL137-140
• 7.5 CMFG_DBSCAN FOR SERVICE CLUSTERING GENERATION BASED ONWELDING MANUFACTURING RESOURCES SUPPLIERS140-144
• 7.6 ABC_CSCCMFG TOWARD EXISTING SOLUTION QOS144-149
• 7.7 SUMMARY149-150
• CONCLUSION AND FUTURE WORK150-152
• REFERENCES152-166
• PUBLICATIONS166-168
• APPENDIXES168-195
• APPENDIX A CASE 1 GENERATED TABLE168-170
• APPENDIX B SERVICE MANUFACTURING GENERATION FOR COMPOSITION170-192
• APPENDIX C TIMESLOTS AND AVAILABILITY GENERATION FOR CLOUDMANUFACTURING SCHEDULING ORGANIZATION192-195
• STATEMENT OF COPYRIGHT195-197
• ACKNOWLEDGEMENTS197-198
• RESUME198

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本文編號(hào)：840176