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Multiple Criteria Decision Making Applications in Environmentally Conscious Manufacturing and Product Recovery [Hardback]

(Northeastern University, Boston, USA), (Celal Bayar University, Turkey)
  • Formāts: Hardback, 173 pages, height x width: 234x156 mm, weight: 476 g, 1 Line drawings, color; 7 Line drawings, black and white; 8 Halftones, color
  • Izdošanas datums: 16-Oct-2017
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 1498700659
  • ISBN-13: 9781498700658
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Multiple Criteria Decision Making Applications in Environmentally Conscious Manufacturing and Product RecoveryPalielināt
  • Formāts: Hardback, 173 pages, height x width: 234x156 mm, weight: 476 g, 1 Line drawings, color; 7 Line drawings, black and white; 8 Halftones, color
  • Izdošanas datums: 16-Oct-2017
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 1498700659
  • ISBN-13: 9781498700658
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781498700658.html
Keywords:

In order to ensure environmentally responsible production and disposal of products, local governments are imposing stricter environmental regulations, some of which even require manufacturers to take back their products at the end of the product's useful life. These government regulations, together with increasing environmental awareness, have forced manufacturers to invest in environment-conscious manufacturing. The multiple Criteria Decision Making Techniques presented in this book can be employed to solve the problems of environment-conscious manufacturers in product design, logistics, disassembly and remanufacturing.

Preface xiii
Acknowledgments xv
Authors xvii
1 Multiple Criteria Decision Making in Environmentally Conscious Manufacturing and Product Recovery
1(34)
1.1 Introduction
1(1)
1.2 Quantitative Techniques
2(6)
1.2.1 Goal Programming
2(2)
1.2.2 Fuzzy Goal Programming
4(1)
1.2.3 Physical Programming
5(1)
1.2.3.1 Reverse and Closed-Loop Supply-Chain Network Design
5(1)
1.2.3.2 Disassembly-to-Order Systems
6(1)
1.2.4 Data Envelopment Analysis
7(1)
1.2.5 Other Mathematical Models
7(1)
1.3 Qualitative Techniques
8(6)
1.3.1 Analytical Hierarchy Process
8(1)
1.3.2 Fuzzy Analytical Hierarchy Process
9(1)
1.3.3 Analytical Network Process
10(1)
1.3.4 DEMATEL
10(1)
1.3.5 TOPSIS
11(1)
1.3.6 ELECTRE
11(1)
1.3.7 PROMETHEE
12(1)
1.3.8 Multiattribute Utility Theory (MAUT)
12(1)
1.3.9 VIKOR
12(1)
1.3.10 MACBETH
13(1)
1.3.11 Case-Based Reasoning
13(1)
1.3.12 Gray Relational Analysis
13(1)
1.3.13 Other Techniques
14(1)
1.4 Mixed Techniques
14(4)
1.4.1 Analytical Hierarchy Process and Data Envelopment Analysis
14(1)
1.4.2 PROMETHEE and Goal Programming
14(1)
1.4.3 PROMETHEE and Analytical Hierarchy Process
15(1)
1.4.4 PROMETHEE and Analytical Network Process
15(1)
1.4.5 Analytical Hierarchy Process and Case-Based Reasoning
15(1)
1.4.6 Analytical Network Process and Goal Programming
15(1)
1.4.7 Analytical Network Process and Data Envelopment Analysis
15(1)
1.4.8 Analytical Hierarchy Process and Genetic Algorithms
16(1)
1.4.9 Analytical Hierarchy Process and Neural Networks
16(1)
1.4.10 Analytical Hierarchy Process and Analytical Network Process
16(1)
1.4.11 Analytical Hierarchy Process and TOPSIS
16(1)
1.4.12 Analytical Network Process and Gray Relational Analysis
16(1)
1.4.13 Analytical Hierarchy Process and Simulation
16(1)
1.4.14 Analytical Hierarchy Process and Structural Equation Modeling
17(1)
1.4.15 Approaches Involving More than Two Techniques
17(1)
1.5 Heuristics and Metaheuristics
18(1)
1.6 Simulation
19(1)
1.7 Conclusions
20(15)
References
21(14)
2 Techniques Used in the Book
35(28)
2.1 Goal Programming
35(1)
2.2 Fuzzy Logic
36(3)
2.3 Linear Physical Programming
39(2)
2.4 Data Envelopment Analysis
41(3)
2.4.1 CCR Model
42(1)
2.4.2 BCC Model
43(1)
2.5 Analytical Hierarchy Process
44(1)
2.6 Analytic Network Process
45(1)
2.7 DEMATEL
46(2)
2.8 TOPSIS
48(1)
2.9 ELECTRE
49(3)
2.10 PROMETHEE
52(2)
2.11 VIKOR
54(2)
2.12 MACBETH
56(1)
2.13 Gray Relational Analysis
57(2)
2.14 Simulation
59(1)
2.15 Conclusions
60(3)
References
60(3)
3 Goal Programming
63(8)
3.1 The Model
63(5)
3.1.1 Revenues
63(1)
3.1.1.1 Reuse Revenue
63(1)
3.1.1.2 Recycle Revenue
64(1)
3.1.1.3 New Product Sale Revenue
64(1)
3.1.2 Costs
64(1)
3.1.2.1 Collection/Retrieval Cost
64(1)
3.1.2.2 Processing Cost
64(1)
3.1.2.3 Inventory Cost
65(1)
3.1.2.4 Transportation Cost
65(1)
3.1.2.5 Disposal Cost
65(1)
3.1.3 System Constraints
66(2)
3.2 Numerical Example
68(1)
3.3 Other Models
69(1)
3.4 Conclusions
70(1)
References
70(1)
4 Fuzzy Goal Programming
71(10)
4.1 The Model
71(6)
4.1.1 Revenues
72(1)
4.1.1.1 Reuse Revenue
72(1)
4.1.1.2 Recycle Revenue
73(1)
4.1.1.3 New Product Sale Revenue
73(1)
4.1.2 Costs
73(1)
4.1.2.1 Collection/Retrieval Cost
73(1)
4.1.2.2 Processing Cost
73(1)
4.1.2.3 Inventory Cost
74(1)
4.1.2.4 Transportation Cost
74(1)
4.1.2.5 Disposal Cost
75(1)
4.1.3 System Constraints
75(2)
4.2 Numerical Example
77(1)
4.3 Other Models
78(1)
4.4 Conclusions
79(2)
References
79(2)
5 Linear Physical Programming
81(8)
5.1 The Model
81(3)
5.1.1 Model Formulation
81(1)
5.1.1.1 Class-IS Criteria (Smaller-Is-Better)
81(1)
5.1.1.2 Class-IH Criteria
82(1)
5.1.1.3 Goal Constraints
82(1)
5.1.1.4 System Constraints
83(1)
5.2 Numerical Example
84(1)
5.3 Other Models
85(1)
5.4 Conclusions
86(3)
References
87(2)
6 Data Envelopment Analysis
89(4)
6.1 The Model
89(1)
6.2 Numerical Example
89(2)
6.3 Other Models
91(1)
6.4 Conclusions
91(2)
References
92(1)
7 AHP
93(6)
7.1 The Model
93(3)
7.2 Other Models
96(1)
7.3 Conclusions
97(2)
References
97(2)
8 Fuzzy AHP
99(8)
8.1 The Model
99(1)
8.2 Numerical Example
100(3)
8.3 Other Models
103(1)
8.4 Conclusions
104(3)
References
104(3)
9 Analytic Network Process
107(10)
9.1 The Model
107(5)
9.2 Other Models
112(3)
9.3 Conclusions
115(2)
References
116(1)
10 DEMATEL
117(4)
10.1 The Model
117(3)
10.1.1 Determination of Criteria
117(1)
10.1.1.1 Green Supply-Chain Practices
117(1)
10.1.1.2 Organizational Performance
117(1)
10.1.1.3 External Driving Factors
118(1)
10.1.2 Application of DEMATEL Methodology
118(2)
10.2 Other Models
120(1)
10.3 Conclusions
120(1)
References
120(1)
11 TOPSIS
121(12)
11.1 The First Model (Evaluation of Recycling Programs)
121(6)
11.1.1 Success Factors for a Recycling Program
121(1)
11.1.2 Ranking of Recycling Programs Using Fuzzy TOPSIS
122(5)
11.2 The Second Model (Selection of Recycling Partners)
127(2)
11.2.1 Determination of the Selection Criteria
127(1)
11.2.2 Ranking Recycling Partners Using TOPSIS
127(2)
11.3 Other Models
129(1)
11.4 Conclusions
130(3)
References
130(3)
12 ELECTRE
133(6)
12.1 The Model
133(4)
12.2 Other Models
137(1)
12.3 Conclusions
137(2)
References
138(1)
13 PROMETHEE
139(6)
13.1 The Model
139(4)
13.2 Other Models
143(1)
13.3 Conclusions
143(2)
References
144(1)
14 VIKOR
145(6)
14.1 The Model
145(3)
14.2 Other Models
148(1)
14.3 Conclusions
149(2)
References
149(2)
15 MACBETH
151(6)
15.1 The Model
151(4)
15.1.1 Evaluating 3PRLPs Using M-MACBETH Software
151(4)
15.2 Other Model
155(1)
15.3 Conclusions
156(1)
References
156(1)
16 Gray Relational Analysis
157(4)
16.1 The Model
157(3)
16.2 Other Models
160(1)
16.3 Conclusions
160(1)
References
160(1)
17 Conclusions
161(2)
Subject Index 163(8)
Author Index 171
Surendra M. Gupta, Ph.D., is a Professor of Mechanical and Industrial Engineering and the Director of the Laboratory for Responsible Manufacturing, Northeastern University. He received his BE in Electronics Engineering from Birla Institute of Technology and Science, MBA from Bryant University, and MSIE and Ph.D. in Industrial Engineering from Purdue University. He is a registered professional engineer in the State of Massachusetts. Dr. Guptas research interests span the areas of Production/Manufacturing Systems and Operations Research. He is mostly interested in Environmentally Conscious Manufacturing, Reverse and Closed-Loop Supply Chains, Disassembly Modeling and Remanufacturing. He has authored or coauthored ten books and well over 500 technical papers published in edited books, journals and international conference proceedings. His publications have received over ten thousand citations from researchers all over the world in journals, proceedings, books, and dissertations. He has traveled to all seven continents viz., Africa, Antarctica, Asia, Australia, Europe, North America and South America and presented his work at international conferences on six continents. Dr. Gupta has taught over 150 courses in such areas as operations research, inventory theory, queuing theory, engineering economy, supply chain management, and production planning and control. Among the many recognitions received, he is the recipient of outstanding research award and outstanding industrial engineering professor award (in recognition of teaching excellence) from Northeastern University as well as a national outstanding doctoral dissertation advisor award.

Mehmet Ali Ilgin, Ph.D., is an Assistant Professor of Industrial Engineering at Manisa Celal Bayar University. He holds a PhD in industrial engineering from Northeastern University in Boston, and BS and MS in industrial engineering from Dokuz Eylul University. His research interests are in the areas of environmentally conscious manufacturing, product recovery, remanufacturing, reverse logistics, spare parts inventory management, and simulation. He has published a number of research papers in refereed international journals such as Computers and Industrial Engineering, Robotics and Computer Integrated Manufacturing and the International Journal of Advanced Manufacturing Technology. He is a co-author of the CRC Press book "Remanufacturing Modelling and Analysis".