Optimization and Management in Manufacturing Engineering (eBook)

Resource Collaborative Optimization and Management through the Internet of Things

Xinbao Liu, Jun Pei, Lin Liu, Hao Cheng, Mi Zhou, Panos M. Pardalos (Autoren)

eBook Download: PDF

2017 | 1st ed. 2017
XVIII, 264 Seiten
Springer International Publishing (Verlag)
978-3-319-64568-1 (ISBN)

Infos

Problems facing manufacturing clusters that intersect information technology, process management, and optimization within the Internet of Things (IoT) are examined in this book. Recent advances in information technology have transformed the use of resources and data exchange, often leading to management and optimization problems attributatble to technology limitations and strong market competition. This book discusses several problems and concepts which makes significant connections in the areas of information sharing, organization management, resource operations, and performance assessment.

Geared toward practitioners and researchers, this treatment deepens the understanding between resource collaborative management and advanced information technology. Those in manufacturing will utilize the numerous mathematical models and methods offered to solve practical problems related to cutting stock, supply chain scheduling, and inventory management.  Academics and students with a basic knowledge of manufacturing, combinatorics, and linear programming will find that this discussion widens the research area of resource collaborative management and unites the fields of information technology, manufacturing management, and optimization. 

Inhalt

Preface 6
Acknowledgements 10
Contents 11
Abbreviations 16
Chapter 1: Information Sharing and Risk Management 18
1.1 Introduction 18
1.1.1 The Benefits and Risks Resulting from IoT 18
1.1.2 Information Sharing and Information Security Investment 19
1.2 Literature Review 20
1.2.1 Just Considering Security Investment 20
1.2.2 Considering Both the Security Investment and Information Sharing 22
1.3 Model Setting 23
1.3.1 Previous Game in Traditional Environment 25
1.3.2 Our Game in the Environment of IoT 25
1.4 Non-cooperative Game 26
1.4.1 Firms´ Strategies in the Non-cooperative Game 27
1.4.2 Parameters Analyses 28
1.4.3 Economical Analysis 30
1.4.4 Numerical Experiments 31
1.5 Totally Cooperative Game 35
1.5.1 Firms´ Strategies in the Totally Cooperative Game 35
1.5.2 Parameters Analyses 36
1.5.3 Economical Analysis 39
1.5.4 Numerical Experiments 40
1.6 Optimum Analysis and Coordination Mechanism 44
1.6.1 Optimum Analysis 45
1.6.2 Coordination Mechanism 47
1.7 Conclusion and Future Research 49
1.7.1 Conclusion 49
1.7.2 Future Research 50
Chapter 2: Optimal Allocation of Decision-Making Authority in IoT-Based Manufacturing Enterprises 51
2.1 Introduction 51
2.2 Literature Review 54
2.3 Optimization Model for Decision Authority Distribution 57
2.3.1 The Multi-objective Optimization Model 58
2.3.2 Numerical Study 63
2.4 Sequential Game Model for Centralization Behavior 69
2.4.1 The Sequential Game Model 69
2.4.2 Numerical Study 74
2.5 Conclusion and Further Research 76
2.5.1 Conclusion 76
2.5.2 Future Research 77
Chapter 3: Dynamic Coordinated Supply Chain Scheduling in an IoT Environment 78
3.1 Introduction 78
3.2 Literature Review 79
3.2.1 The Applications of the IoT in Industrial Manufacturing 79
3.2.2 The Development of Supply Chain Scheduling 80
3.2.3 The Development of Batch Scheduling 80
3.2.4 The Applications of the IoT in Supply Chain Scheduling 82
3.3 Problem Description 82
3.4 A Mixed Integer Programming Formulation 83
3.5 Heuristic for Batch Formation and Scheduling in Single Machine 86
3.6 Key Steps in SFLF-PR 91
3.6.1 Coding and Encoding 91
3.6.2 Basic Shuffled Frog Leaping Algorithm (SFLA) 92
3.6.3 Basic Path-Relinking Algorithm 93
3.6.4 SFLA-PR Algorithm 93
3.7 Computational Experiments 102
3.8 Conclusion and Future Research 105
3.8.1 Conclusion 105
3.8.2 Future Research 105
Chapter 4: Hybrid Manufacturing Distributed Inventory Management with Sharing Logistics 106
4.1 Introduction 106
4.1.1 Background 106
4.1.2 Inventory Routing Problem in Hybrid Manufacturing 108
4.2 Literature Review 109
4.2.1 Vehicle Routing Problem 109
4.2.2 Inventory Routing Problem 110
4.2.3 Crossing Field Literature 111
4.2.4 Our Contribution 111
4.3 Model Descriptions 112
4.3.1 Model with One Retailer and Ignorance of Transportation 113
4.3.2 Model with Multiretailers and the Consideration of Transportation 114
4.4 The Proposed Solution Method 116
4.4.1 The Main Structure of Algorithm 117
4.4.2 Improvement Neighborhood Structure 118
4.4.3 Perturbation Mechanism 121
4.4.4 Adaptive Selection Mechanism 125
4.4.5 Subproblem of Delivery and Pickup Plan 125
4.4.6 Acceptance and Termination Condition 128
4.5 Computational Results 128
4.5.1 Construction of Instances 128
4.5.2 Algorithm Setting 128
4.5.3 Lower Bound 130
4.5.4 Upper Bound 131
4.5.5 Comparison with Upper Bound and Lower Bound 131
4.5.6 The Performance of Heuristic in the Subproblem of Delivery and Pickup Plan 133
4.5.7 The Effect of Novel Neighborhood Structures 135
4.5.8 The Sensitivity Analysis of Vehicle Capacity 135
4.5.9 Sensitivity Analysis of li and C 137
4.6 Conclusion and Future Research 139
4.6.1 Conclusion 139
4.6.2 Future Research 139
Chapter 5: Cutting Stock Problem with the IoT 141
5.1 Introduction 141
5.1.1 The Cutting Stock Problem 141
5.1.2 Notation of the Cutting Stock Problem 142
5.1.3 Cutting Stock in Manufacturing 142
5.1.4 The Cutting Stock Problem with the IoT 142
5.2 Literature Review 143
5.2.1 One-Dimensional Cutting Stock Problem 144
5.2.2 Two-Dimensional Cutting Stock Problem 146
5.2.3 Applications of the Cutting Stock Problem 147
5.3 Variable Cross-Sectional Cutting Stock Problem 148
5.3.1 Background and Model of the Problem 149
5.3.2 The Improved Algorithm for Solving Circular Truncated Cone Cutting Stock Problem 153
5.3.2.1 Length Conversion Algorithm 153
Manual Ruler Method 154
Formula Method 155
Relation Between Position and Length 156
5.3.2.2 SHP Algorithm 157
5.3.2.3 Group Selection and Local Optimization 159
Quantity-Based Selection Algorithm 159
Sorting-Based Selection Algorithm 160
5.4 CSP in the Internet of Things 162
5.4.1 Product Cutting Status and Position Monitoring 162
5.4.2 The Model of CSP in Internet of Things 163
5.4.3 Improved SHP Algorithm 167
5.4.3.1 Generate Preliminary Cutting Plan 168
5.4.3.2 Substitution Principle and Algorithm 169
5.4.3.3 Regenerating Cutting Program 170
5.4.4 Simulation and Analysis 171
5.5 Conclusion and Future Research 173
5.5.1 Conclusion 173
5.5.2 Future Research 174
Chapter 6: Total Quality Management of the Product Life Cycle in an IoT Environment 176
6.1 Introduction 176
6.1.1 Total Quality Management 176
6.1.2 Product Life Cycle Quality Management 177
6.1.3 Quality of Service (QoS) in a Cloud Manufacturing Environment 177
6.2 Literature Review 178
6.2.1 The Application of TQM to Realize Continuous Quality Improvements 178
6.2.2 The TQM Approach 179
6.2.3 Meeting Customer Requirements with Cloud Manufacturing 180
6.3 Entire Life Cycle Quality Management 182
6.3.1 Difference of Quality Management Under Internet and Big Data 182
6.3.2 Entire Life Cycle Quality Management Concepts System 183
6.3.2.1 Concepts and Composition of Entire Life Cycle Quality 183
6.3.2.2 Concepts of Life Cycle Quality Management and Its Development 185
6.3.2.3 Concepts and Components of Quality Cost 187
6.3.3 Life Cycle Quality Assurance System and Methods for Its Implementation 190
6.3.3.1 Life Cycle Quality Management Principles 191
6.3.3.2 Life Cycle Quality Improvement Methods 192
6.3.4 Life Cycle Quality Management System 195
6.3.4.1 Correlational Analysis Method 195
6.3.4.2 Cause and Effect Analysis Method 196
6.3.4.3 Quality System Assessment 197
6.3.4.4 The Quality Improvement Method in Design Phase 199
6.3.4.5 Multi-vendor Collaborative Manufacturing Quality Control Methods 199
6.3.4.6 Service Quality Gap Improvement Methods 202
6.3.4.7 Decision-Making Method of Remanufacturing Quality 206
6.4 A Quality Management Model Considering Service Level 208
6.4.1 Model Description 208
6.4.2 Equilibrium Analysis 210
6.4.2.1 The Manufacturer Invests an Amount C to Inspect All the Goods Purchased from Supplier 210
6.4.2.2 The Supplier´s Quality Is Taken as Common Knowledge and the q and p Are Determined by a Third Party 214
6.4.2.3 The Manufacturer Invests an Amount C to Inspect All the Goods and Share the Manufacture Cost of Semi-manufactured Good... 218
6.5 Conclusion and Future Research 221
6.5.1 Conclusion 221
6.5.2 Future Research 221
Chapter 7: Life Cycle Assessment in an IoT Environment 222
7.1 Introduction 222
7.1.1 Life Cycle Assessment 222
7.1.2 LCA in an IoT Environment 223
7.2 Literature Review 224
7.2.1 Inventory Analysis and Impact Assessment 225
7.2.2 Life Cycle Cost 227
7.2.3 Social Life Cycle Assessment 228
7.2.4 LCA Methods 228
7.3 Life Cycle Assessment of Mobile Phone from Environmental Dimension 229
7.3.1 Summary of Life Cycle of Mobile Phone 230
7.3.2 Construction of LCA System Model 231
7.4 Inventory Analysis 234
7.4.1 Production Capacity of iPhone4S 234
7.4.2 Components Inventory of iPhone4S 235
7.4.3 Energy Consumption and Emissions of Material Production and Mobile Phone Manufacturing for iPhone4S 236
7.4.4 Inventory in the Transportation Phase 243
7.4.5 Inventory in the Use Phase 244
7.4.6 Inventory in the Recycling Phase 245
7.4.7 Inventory in the Whole Life Cycle 245
7.5 Impact Assessment and Improvement Analysis 246
7.5.1 Impact Assessment 246
1 Impact Classification 246
2 Characterization 247
3 Quantitative Assessment 248
7.5.2 Improvement Analysis 248
7.6 Social Life Cycle Assessment to iPhone 4S 249
7.6.1 Construction of S-LCA Structure 249
7.6.2 Brief Introduction of Yang´s ER Approach 250
7.6.3 Assignment of Belief Degrees and Attribute Weights 253
7.6.4 Generating the Overall Belief Degrees 254
7.7 Conclusion and Future Research 257
7.7.1 Conclusion 257
7.7.2 Future Research 258
Appendix: Materials of the Main Components of iPhone4S 260
References 263

Produkt-Details

• Erscheint lt. Verlag: 2.10.2017
• Reihe/Serie: Springer Optimization and Its Applications
• Reihe/Serie: Springer Optimization and Its Applications
• Zusatzinfo: XVIII, 264 p. 14 illus., 6 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Mathematik / Informatik ► Mathematik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion
• Schlagworte: collaborative problems for manufacturers • cutting stock algorithm • decision methods • decision-right distribution method • hybrid heuristic • internet of things • Inventory Management • IOT • manufacture enterprise • Manufacturing Process Management • neighborhood-searching • optimal organizational structure • practical manufacturing • Quality management • Routing algorithm • Supply chain scheduling
• ISBN-10: 3-319-64568-4 / 3319645684
• ISBN-13: 978-3-319-64568-1 / 9783319645681