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Intelligent Scheduling of Robotic Flexible Assembly Cells 1st ed. 2016 [Hardback]

  • Formāts: Hardback, 164 pages, height x width: 235x155 mm, weight: 3967 g, 49 Illustrations, color; 29 Illustrations, black and white; XVI, 164 p. 78 illus., 49 illus. in color., 1 Hardback
  • Sērija : Springer Theses
  • Izdošanas datums: 17-Nov-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319262955
  • ISBN-13: 9783319262956
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Intelligent Scheduling of Robotic Flexible Assembly Cells 1st ed. 2016Palielināt
  • Formāts: Hardback, 164 pages, height x width: 235x155 mm, weight: 3967 g, 49 Illustrations, color; 29 Illustrations, black and white; XVI, 164 p. 78 illus., 49 illus. in color., 1 Hardback
  • Sērija : Springer Theses
  • Izdošanas datums: 17-Nov-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319262955
  • ISBN-13: 9783319262956
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319262956.html
Keywords:

This book focuses on the design of Robotic Flexible Assembly Cell (RFAC) with multi-robots. Its main contribution consists of a new effective strategy for scheduling RFAC in a multi-product assembly environment, in which dynamic status and multi-objective optimization problems occur. The developed strategy, which is based on a combination of advanced solution approaches such as simulation, fuzzy logic, system modeling and the Taguchi optimization method, fills an important knowledge gap in the current literature and paves the way for future research towards the goal of employing flexible assembly systems as effectively as possible despite the complexity of their scheduling.

1 Background and Research Scope
1(12)
1.1 Introduction
1(1)
1.2 Flexible Assembly Systems
2(2)
1.2.1 Robots
2(2)
1.2.2 Peripheral Equipment
4(1)
1.3 Classification of Flexible Assembly Systems
4(3)
1.3.1 Robotic Assembly Line
4(1)
1.3.2 Robotic Assembly Cell
5(1)
1.3.3 Simple Comparison Between RAL and RAC
6(1)
1.4 Scheduling of Robotic Flexible Assembly Cell
7(1)
1.5 Motivation for Research in Scheduling of RFAC
8(1)
1.6 Research Gap and Scope
9(1)
1.7 Concluding Remarks
10(3)
References
10(3)
2 Literature Review and Research Objectives
13(18)
2.1 Introduction
13(1)
2.2 Scheduling Problems in Manufacturing Systems
14(3)
2.2.1 Types of Scheduling Problems
14(1)
2.2.2 Characteristics of Scheduling Problems
15(1)
2.2.3 Solution Approaches
16(1)
2.3 Review of Literature on Advanced Scheduling Approaches
17(3)
2.3.1 Simulation Approaches
17(1)
2.3.2 Artificial Intelligence Approaches
18(1)
2.3.3 Observations from the Literature Review
19(1)
2.4 Scheduling of RFAC: A Literature Review
20(2)
2.4.1 Traditional Approaches
20(1)
2.4.2 Simulation Approaches
21(1)
2.4.3 Expert System Approaches
22(1)
2.5 Research Limitations
22(1)
2.6 Research Objectives and Thesis Plan
23(5)
2.6.1 Research Objectives
23(2)
2.6.2 Research Plan and Thesis Structure
25(3)
2.7 Concluding Remarks
28(3)
References
28(3)
3 Development of an Intelligent Methodology for Scheduling RFAC
31(18)
3.1 Introduction
31(1)
3.2 Application of Fuzzy Logic to Scheduling Problems
31(3)
3.3 Proposed Methodology for Scheduling of RFAC
34(11)
3.3.1 Pre-processing Module
35(4)
3.3.2 Scheduling Module
39(3)
3.3.3 Linguistic Variables
42(1)
3.3.4 Membership Functions
43(1)
3.3.5 Fuzzy Rules
43(2)
3.4 Concluding Remarks
45(4)
References
46(3)
4 Case Study 1: Application of the Developed Methodology Using Fuzzy Logic and Simulation
49(20)
4.1 Introduction
49(1)
4.2 A Fuzzy Logic Model for Scheduling RFAC
49(4)
4.2.1 Defining the Linguistic Variables
50(1)
4.2.2 Constructing Membership Functions
50(2)
4.2.3 Constructing Fuzzy Rules
52(1)
4.3 Implementation of Fuzzy Approach for Scheduling of RFAC
53(4)
4.4 Example Application of Scheduling RFAC
57(5)
4.4.1 RFAC Description
57(3)
4.4.2 Simulation of Experimental Design
60(2)
4.5 Simulation Results and Discussion
62(4)
4.6 Concluding Remarks
66(3)
References
67(2)
5 Simulation Modelling and Analysis of Dynamic Scheduling in RFAC
69(24)
5.1 Introduction
69(1)
5.2 Review of Literature on Dynamic Events
70(2)
5.3 A Framework for Developing an Intelligent Approach to Dynamic Scheduling Problems
72(5)
5.3.1 Preparation
73(1)
5.3.2 Application of Taguchi Method
74(1)
5.3.3 Simulation Modelling
74(1)
5.3.4 Statistical Analysis
75(2)
5.4 The Simulation Model
77(3)
5.5 Experimental Design and Results
80(3)
5.5.1 Experimental Setup
80(2)
5.5.2 Taguchi's Orthogonal Array Selection
82(1)
5.5.3 Calculation of the Signal-to-Noise (S/N) Ratio
83(1)
5.6 Analysis of Results and Discussion
83(6)
5.6.1 Analysis of Mean (ANOM)
83(3)
5.6.2 Analysis of Variance (ANOVA)
86(3)
5.7 Concluding Remarks
89(4)
References
90(3)
6 Development of an Optimization Approach for Dynamic Scheduling Problems in RFAC
93(28)
6.1 Introduction
93(1)
6.2 Multi-criteria Decision-Making
94(4)
6.2.1 MCDM Process
94(1)
6.2.2 In Search of a Powerful Method for Complex Decision Making Problems
95(3)
6.3 A Hybrid Approach for Optimization of Dynamic Scheduling Problems
98(4)
6.3.1 Problem Description
99(1)
6.3.2 Application of Fuzzy MCDM Methods
100(1)
6.3.3 Analysis of the Results
101(1)
6.4 Implementation of Fuzzy Decision Support System
102(6)
6.4.1 Structure of Fuzzy Decision Support System
102(3)
6.4.2 Design of the Proposed Fuzzy Decision Support System
105(3)
6.5 Implementation of Fuzzy AHP-Fuzzy TOPSIS
108(5)
6.5.1 Methodology of FAHP
108(3)
6.5.2 Methodology of FTOPSIS
111(2)
6.6 Concluding Remarks
113(8)
References
115(6)
7 Case Study 2: Application of Hybrid Fuzzy MCDM Approach to Optimize Dynamic Scheduling in RFAC
121(22)
7.1 Introduction
121(1)
7.2 Case Study
122(1)
7.3 Application Using FDSS
123(6)
7.3.1 Defining Input and Output Variables
124(1)
7.3.2 Specifying Input and Output Membership Functions
124(1)
7.3.3 Constructing Decision Rules and Knowledge Base
125(2)
7.3.4 Determining MPCI by Using Defuzzification
127(2)
7.4 Application Using FAHP-FTOPSIS
129(5)
7.4.1 Application of FAHP in Determining Weights of Criteria
129(3)
7.4.2 Application of FTOPSIS in Ranking of Alternatives
132(2)
7.5 Analysis of Results and Discussion
134(6)
7.5.1 Comparison of the Results
134(1)
7.5.2 Sensitivity Analysis
135(4)
7.5.3 Effect of Scheduling Factors on MPCI
139(1)
7.5.4 Confirmation Test
139(1)
7.6 Concluding Remarks
140(3)
8 Conclusions and Recommendations for Future Work
143(7)
8.1 Introduction
143(1)
8.2 Summary of the Research
144(2)
8.2.1 Scheduling RFAC in a Multi-product Assembly Environment
144(1)
8.2.2 Scheduling RFAC in a Dynamic Situation
145(1)
8.2.3 Scheduling RFAC in Multi-objective Optimization Problems
145(1)
8.3 Conclusions
146(1)
8.4 Recommendations for Future Work
147(2)
8.4.1 Robust Scheduling of RFAC with Interruptions
147(1)
8.4.2 Virtual Reality for RFAC Simulation
148(1)
8.4.3 Deadlock Prevention and Avoidance in RFAC
149(1)
8.5 Final Word
149(1)
References 150(1)
Appendix A 151(2)
Appendix B 153(2)
Appendix C 155(4)
Appendix D 159(4)
Curriculum Vitae 163