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云制造的服务聚集、组合与调度优化方法

摘要第4-6页
ABSTRACT第6-8页
ABBREVIATIONS第20-22页
CHAPTER 1 INTRODUCTION第22-56页
    1.1 BACKGROUND AND MOTIVATION第22-29页
    1.2 CLOUD MANUFACTURING AND RELATED ISSUES第29-36页
        1.2.1 Cloud Manufacturing Concept第29-32页
        1.2.2 Key Advantages of Cloud Manufacturing第32-33页
        1.2.3 Manufacturing Resource Service Clustering第33-34页
        1.2.4 Manufacturing Resource Service Composition第34-35页
        1.2.5 Manufacturing Resource Service Scheduling第35-36页
    1.3 SURVEY OF THE STATE OF THE ART OF CLOUD MANUFACTURING AND THERELATED KEY TECHNOLOGIES第36-50页
        1.3.1 Key Technologies of Cloud Manufacturing第36-45页
        1.3.2 Resources Clustering第45-46页
        1.3.3 Service Composition第46-48页
        1.3.4 Manufacturing Tasks Scheduling第48-50页
    1.4 RESEARCH PROBLEMS & OBJECTIVES第50-53页
        1.4.1 Manufacturing Resource Service Clustering第51页
        1.4.2 Manufacturing Resource Service Clusters Re -structuring第51-52页
        1.4.3 Cloud Manufacturing Service Composition第52-53页
        1.4.4 Cloud Manufacturing Service Scheduling第53页
    1.5 MAIN CONTENT OF THE THESIS第53-56页
CHAPTER 2 CLUSTER BASED CLOUD MANUFACTURING FRAMEWORKAND KEY TECHNOLOGIES第56-71页
    2.1 CLUSTER BASED CLOUD MANUFACTURING FRAMEWORK & DEFINITION第56-58页
    2.2 SERVICE PROCESSING IN CLOUD MANUFACTURING第58-61页
    2.3 KEY TECHNOLOGIES FOR SERVICE PROCESSING OPTIMIZATION IN CLOUDMANUFACTURING第61-69页
        2.3.1 Quality of Services in Cloud Manufacturing第61-63页
        2.3.2 Geo-perspective model第63-64页
        2.3.3 Composition models第64页
        2.3.4 Artificial Bee Colony Optimization第64-67页
        2.3.5 Density-Based Spatial Clustering of Applications with Noise第67-69页
    2.4 SUMMARY第69-71页
CHAPTER 3 MULTI-DIMENSION DENSITY-BASED CLUSTERING WITH THE SUPPORT OF CLOUD MANUFACTURING SERVICE DECOMPOSITIONMODEL第71-80页
    3.1 PROBLEM STATEMENT第71页
    3.2 CMFG CLUSTERING FRAMEWORK第71-72页
    3.3 RESOURCE SPACE DEFINITION第72-74页
        3.3.1 CMfg service Decomposition Model第72-73页
        3.3.2 Resource Space Definition Pre-sets第73-74页
    3.4 Modified DBSCAN Algorithm for CMfg Clustering Framework第74-77页
    3.5 CMFG_DBSCAN() EXPERIMENTS第77-79页
    3.6 SUMMARY第79-80页
CHAPTER 4 A RE-STRUCTURING SERVICE CLUSTER ALGORITHM ABCOPTIMIZED BASED ON VIRTUAL RESOURCE SELECTION PROBABILITY第80-98页
    4.1 PROBLEM STATEMENT: AN EXTENDED VIEW OF CMFG CLUSTERINGFRAMEWORK第80-82页
    4.2 SERVICE CLUSTER RE-STRUCTURATION TRIGGER FUNCTION第82-84页
        4.2.1 Structure Efficiency and Cost Evaluation第83页
        4.2.2 Service cluster Selection Experience第83页
        4.2.3 Restructuring Trigger Function第83-84页
    4.3 SERVICE CLUSTERS AND VIRTUAL RESOURCES SELECTION PROBABILITY第84-88页
    4.4 SERVICE CLUSTERS FITNESS EVALUATION第88-89页
    4.5 RE-STRUCTURING SERVICE CLUSTERS ALGORITHM ABC OPTIMIZED第89-91页
        4.5.1 ABC Control Parameters第89页
        4.5.2 Re-structuring Service Clusters Algorithm ABC Optimized第89-91页
    4.6 PRECISION AND PERFORMANCE EVALUATION第91-96页
        4.6.1 Precision Evaluation第92-94页
        4.6.2 Performance Evaluation第94-96页
    4.7 SUMMARY第96-98页
CHAPTER 5 AN OPTIMIZED CLOUD MANUFACTURING SERVICECOMPOSITION BASED ON QOS AND GEO-PERSPECTIVE第98-124页
    5.1 PROBLEM STATEMENT第98-99页
        5.1.1 Sets and Model variables第98页
        5.1.2 Cloud Service Composition Problem Definition第98-99页
    5.2 CLOUD MANUFACTURING SERVICE COMPOSITION FRAMEWORK第99-100页
    5.3 QOS EVALUATION第100-102页
        5.3.1 Sequence Model第100-101页
        5.3.2 Parallel Model第101页
        5.3.3 Selective Model第101页
        5.3.4 Circular Model第101-102页
    5.4 SUBSTITUTION STRATEGY FOR UNMATCHED QOS第102-104页
    5.5 RESOURCE SERVICE TRANSPORTATION EVALUATION第104-108页
        5.5.1 Mean of Transportation Selection第105-107页
        5.5.2 Transportation Distance Evaluation第107页
        5.5.3 Resource Service Transportation Qo S Evaluation第107-108页
    5.6 INTEROPERABILITY CONNECTOR EVALUATION第108-109页
        5.6.1 Interoperability in Manufacturing第108页
        5.6.2 Interoperability Vision第108-109页
    5.7 CLOUD SERVICE COMPOSITION FITNESS DEFINITION第109-111页
    5.8 IMPROVED ARTIFICIAL BEE COLONY FOR CLOUD MANUFACTURING SERVICECOMPOSITION EVALUATION第111-114页
    5.9 SIMULATIONS第114-123页
        5.9.1 ABC_Cs CCMfg Parameters Tuning第114页
        5.9.2 Case Generation for ABC_Cs CCMfg Simulations第114-116页
        5.9.3 ABC_Cs CCMfg Performance Evaluation第116-119页
        5.9.4 ABC_Cs CCMfg Precision Evaluation第119-121页
        5.9.5 Transportation Evaluation Impact第121页
        5.9.6 CS candidates Availability Impact第121-122页
        5.9.7 Interoperability Performance Impact第122-123页
    5.10 SUMMARY第123-124页
CHAPTER 6 OPTIMIZED SCHEDULING FRAMEWORK BASED ONRESOURCE SERVICE AVAILABILITY第124-135页
    6.1 PROBLEM STATEMENT第124页
    6.2 CONSTRAINTS DEFINITION第124-126页
    6.3 TIMESLOTS AND AVAILABILITY OVERTIME DEFINITION第126-128页
    6.4 FITNESS EVALUATION BASED ON MANUFACTURING STARTING TIME第128-130页
    6.5 CLOUD MANUFACTURING SCHEDULING ORCHESTRATION第130-131页
    6.6 EXPERIMENTS第131-134页
        6.6.1 ABC Control Parameters第131页
        6.6.2 Performance Evaluation第131-133页
        6.6.3 Manufacturing Time Evaluation Impact第133-134页
    6.7 SUMMARY第134-135页
CHAPTER 7 ASEM USE CASE: SERVICE CLUSTER GENERATION ANDCLOUD MANUFACTURING SERVICE COMPOSITION INTEGRATION第135-150页
    7.1 INTRODUCTION第135页
    7.2 ASEM PRESENTATION第135-137页
    7.3 ASEM HT700第137页
    7.4 HT700 CLOUD MANUFACTURING SERVICE MODEL第137-140页
    7.5 CMFG_DBSCAN FOR SERVICE CLUSTERING GENERATION BASED ONWELDING MANUFACTURING RESOURCES SUPPLIERS第140-144页
    7.6 ABC_CSCCMFG TOWARD EXISTING SOLUTION QOS第144-149页
    7.7 SUMMARY第149-150页
CONCLUSION AND FUTURE WORK第150-152页
REFERENCES第152-166页
PUBLICATIONS第166-168页
APPENDIXES第168-195页
    APPENDIX A CASE 1 GENERATED TABLE第168-170页
    APPENDIX B SERVICE MANUFACTURING GENERATION FOR COMPOSITION第170-192页
    APPENDIX C TIMESLOTS AND AVAILABILITY GENERATION FOR CLOUDMANUFACTURING SCHEDULING ORGANIZATION第192-195页
STATEMENT OF COPYRIGHT第195-197页
ACKNOWLEDGEMENTS第197-198页
RESUME第198页

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