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E-raamat: Genetic Algorithms and Engineering Design [Wiley Online]

(Ashikaga Institute of Technology, Japan), (Ashikaga Institute of Technology, Japan)
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Genetic Algorithms and Engineering DesignSuurem pilt
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470172254
Describes the current application of genetic algorithms to problems in industrial engineering and operations research. Introduces the fundamentals of genetic algorithms and their use in solving constrained and combinatorial optimization problems. Then looks at problems in specific areas, including sequencing, scheduling and production plans, transportation and vehicle routing, facility layout, and location allocation. The explanation are intuitive rather than highly technical, and are supported with numerical examples. Suitable for self-study or classrooms. Annotation c. by Book News, Inc., Portland, Or.

The last few years have seen important advances in the use of genetic algorithms to address challenging optimization problems in industrial engineering. Genetic Algorithms and Engineering Design is the only book to cover the most recent technologies and their application to manufacturing, presenting a comprehensive and fully up-to-date treatment of genetic algorithms in industrial engineering and operations research.

Beginning with a tutorial on genetic algorithm fundamentals and their use in solving constrained and combinatorial optimization problems, the book applies these techniques to problems in specific areas--sequencing, scheduling and production plans, transportation and vehicle routing, facility layout, location-allocation, and more. Each topic features a clearly written problem description, mathematical model, and summary of conventional heuristic algorithms. All algorithms are explained in intuitive, rather than highly-technical, language and are reinforced with illustrative figures and numerical examples.

Written by two internationally acknowledged experts in the field, Genetic Algorithms and Engineering Design features original material on the foundation and application of genetic algorithms, and also standardizes the terms and symbols used in other sources--making this complex subject truly accessible to the beginner as well as to the more advanced reader.

Ideal for both self-study and classroom use, this self-contained reference provides indispensable state-of-the-art guidance to professionals and students working in industrial engineering, management science, operations research, computer science, and artificial intelligence. The only comprehensive, state-of-the-art treatment available on the use of genetic algorithms in industrial engineering and operations research . . .

Written by internationally recognized experts in the field of genetic algorithms and artificial intelligence, Genetic Algorithms and Engineering Design provides total coverage of current technologies and their application to manufacturing systems. Incorporating original material on the foundation and application of genetic algorithms, this unique resource also standardizes the terms and symbols used in other sources--making this complex subject truly accessible to students as well as experienced professionals. Designed for clarity and ease of use, this self-contained reference:
* Provides a comprehensive survey of selection strategies, penalty techniques, and genetic operators used for constrained and combinatorial optimization problems
* Shows how to use genetic algorithms to make production schedules, solve facility/location problems, make transportation/vehicle routing plans, enhance system reliability, and much more
* Contains detailed numerical examples, plus more than 160 auxiliary figures to make solution procedures transparent and understandable
1 Foundations of Genetic Algorithms
1(41)
1.1 Introduction
1(6)
1.1.1 General Structure of Genetic Algorithms
1(3)
1.1.2 Exploitation and Exploration
4(1)
1.1.3 Population-Based Search
5(1)
1.1.4 Meta-heuristics
6(1)
1.1.5 Major Advantages
6(1)
1.1.6 Genetic Algorithm Vocabulary
7(1)
1.2 Examples with Simple Genetic Algorithms
7(9)
1.2.1 Optimization Problem
7(8)
1.2.2 Word-Matching Problem
15(1)
1.3 Encoding Problem
16(4)
1.4 Selection
20(11)
1.4.1 Sampling Space
21(1)
1.4.2 Sampling Mechanism
22(3)
1.4.3 Selection Probability
25(4)
1.4.4 Selective Pressures
29(2)
1.5 Hybrid Genetic Algorithms
31(3)
1.5.1 Lamarckian Evolution
32(1)
1.5.2 Memetic Algorithms
33(1)
1.6 Important Events in the Genetic Algorithm Community
34(8)
1.6.1 Books on Genetic Algorithms
34(1)
1.6.2 Conferences and Workshops
35(4)
1.6.3 Journals and Special Issues on Genetic Algorithms
39(1)
1.6.4 Public-Accessible Internet Service for Genetic Algorithm Information
40(2)
2 Constrained Optimization Problems
42(55)
2.1 Unconstrained Optimization
42(7)
2.1.1 Ackley's Function
43(1)
2.1.2 Genetic Algorithm Approach for Minimization of Achley's Function
44(5)
2.2 Nonlinear Programming
49(19)
2.2.1 Handling Constraints
49(2)
2.2.2 Penalty Function
51(8)
2.2.3 Genetic Operators
59(5)
2.2.4 Numerical Examples
64(4)
2.3 Stochastic Optimization
68(8)
2.3.1 Mathematical Model
68(1)
2.3.2 Monte Carlo Simulation
69(1)
2.3.3 Evolution Program for Stochastic Optimization Problems
70(6)
2.4 Nonlinear Goal Programming
76(7)
2.4.1 Formulation of Nonlinear Goal Programming
77(1)
2.4.2 Genetic Algorithms for Nonlinear Goal Programming
78(2)
2.4.3 Numerical Examples
80(3)
2.5 Interval Programming
83(14)
2.5.1 Introduction
84(6)
2.5.2 Genetic Algorithm
90(5)
2.5.3 Numerical Example
95(2)
3 Combinatorial Optimization Problems
97(36)
3.1 Introduction
97(1)
3.2 Knapsack Problem
98(5)
3.2.1 Binary Representation Approach
99(2)
3.2.2 Order Representation Approach
101(1)
3.2.3 Variable-Length Representation Approach
101(2)
3.3 Quadratic Assignment Problem
103(4)
3.3.1 Encoding
104(1)
3.3.2 Genetic Operators
105(2)
3.4 Minimum Spanning Tree Problem
107(11)
3.4.1 Problem Description
108(1)
3.4.2 Tree Encodings
109(4)
3.4.3 Genetic Algorithm Approach
113(5)
3.5 Traveling Salesman Problem
118(10)
3.5.1 Representation
118(1)
3.5.2 Crossover Operators
119(6)
3.5.3 Mutation Operators
125(3)
3.6 Film-Copy Deliverer Problem
128(5)
3.6.1 Representation
128(2)
3.6.2 Genetic Operators
130(3)
4 Reliability Optimization Problems
133(40)
4.1 Introduction
133(6)
4.1.1 Combinatorial Aspects of System Reliability
134(2)
4.1.2 Reliability Optimization Models with Several Failure Modes
136(2)
4.1.3 Reliability Optimization Models with Alternative Design
138(1)
4.2 Simple Genetic Algorithm for Reliability Optimization
139(5)
4.2.1 Problem Formulation
139(2)
4.2.2 Genetic Algorithm and Numerical Example
141(3)
4.3 Reliability Optimization with Redundant Unit and Alternative Design
144(7)
4.3.1 Problem Formulation
144(1)
4.3.2 Genetic Algorithm and Numerical Example
145(6)
4.4 Reliability Optimization with Redundant Mixing Components
151(5)
4.4.1 Problem Formulation
151(2)
4.4.2 Genetic Algorithm and Numerical Example
153(3)
4.5 Reliability Optimization with Fuzzy Goal and Fuzzy Constraints
156(7)
4.5.1 Problem Formulation
156(3)
4.5.2 Genetic Algorithm and Numerical Example
159(4)
4.6 Reliability Optimization with Interval Coefficients
163(10)
4.6.1 Problem Formulation
163(3)
4.6.2 Genetic Algorithm
166(3)
4.6.3 Numerical Example
169(4)
5 Flow-Shop Sequencing Problems
173(17)
5.1 Introduction
173(1)
5.2 Two-Machine Flow-Shop Problem
174(2)
5.3 Heuristics for General m-Machine Problems
176(3)
5.3.1 Palmer's Heuristic Algorithm
176(1)
5.3.2 Gupta's Heuristic Algorithm
176(1)
5.3.3 CDS Heuristic Algorithm
177(1)
5.3.4 RA Heuristic Algorithm
178(1)
5.3.5 NEH Heuristic Algorithm
178(1)
5.4 Gen. Tsujimura, and Kubota's Approach
179(3)
5.4.1 Representation
179(1)
5.4.2 Evaluation Function
179(1)
5.4.3 Crossover and Mutation
179(1)
5.4.4 Examples
180(2)
5.5 Reeve's Approach
182(4)
5.5.1 Initial Population
182(1)
5.5.2 Genetic Operators
182(1)
5.5.3 Selection
183(2)
5.5.4 Numerical Example
185(1)
5.6 Ischibuchi, Yamamoto, Murata, and Tanaka's Approach
186(4)
5.6.1 Fuzzy Flow-Shop Problem
186(1)
5.6.2 Hybrid Genetic Algorithm
187(2)
5.6.3 Numerical Example
189(1)
6 Job-Shop Scheduling Problems
190(44)
6.1 Introduction
190(1)
6.2 Classical Job-Shop Model
191(6)
6.2.1 IP Model
193(2)
6.2.2 LP Model
195(1)
6.2.3 Graph Model
196(1)
6.3 Conventional Heuristics
197(5)
6.3.1 Priority Dispatching Heuristics
197(2)
6.3.2 Randomized Dispatching Heuristic
199(2)
6.3.3 Shifting Bottleneck Heuristic
201(1)
6.4 Genetic Algorithms for Job-Shop Scheduling Problems
202(21)
6.4.1 Representation
202(12)
6.4.2 Discussion
214(3)
6.4.3 Hybrid Genetic Search
217(6)
6.5 Gen, Tsujimura, and Kubota's Approach
223(3)
6.6 Cheng, Gen, and Tsujimura's Approach
226(2)
6.7 Falkenauer and Bouffouix's Approach
228(2)
6.8 Dorndorf and Pesch's Approach
230(1)
6.9 Computational Results and Discussion
231(3)
7 Machine Scheduling Problems
234(28)
7.1 Introduction
234(10)
7.1.1 Single-Machine Sequencing Problem
235(4)
7.1.2 Earliness and Tardiness Scheduling Problems
239(3)
7.1.3 Parallel Machine Scheduling Problem
242(2)
7.2 Cleveland and Smith's Approach
244(3)
7.2.1 Genetic Operators
245(1)
7.2.2 Selection
246(1)
7.3 Gupta, Gupta, and Kumar's Approach
247(2)
7.3.1 Evaluation Function
247(1)
7.3.2 Replacement Strategy
247(1)
7.3.3 Convergence Policy
248(1)
7.3.4 Overall Procedure
248(1)
7.4 Lee and Kim's Approach
249(4)
7.4.1 Representation
250(1)
7.4.2 Parallel Subpopulations
251(1)
7.4.3 Crossover and Mutation
252(1)
7.4.4 Evaluation and Selection
252(1)
7.4.5 Parallel Genetic Algorithm
253(1)
7.5 Cheng and Gen's Approach
253(9)
7.5.1 Representation and Initialization
254(1)
7.5.2 Crossover
255(1)
7.5.3 Mutation
255(3)
7.5.4 Determining the Best Due Date
258(1)
7.5.5 Evaluation and Selection
259(1)
7.5.6 Numerical Example
260(2)
8 Transportation Problems
262(30)
8.1 Introduction
262(1)
8.2 Linear Transportation Problem
263(8)
8.2.1 Formulation of LTP
263(2)
8.2.2 Representation
265(1)
8.2.3 Genetic Operation
266(5)
8.3 Bicriteria Linear Transportation Problem
271(7)
8.3.1 Formulation of BLTP
271(1)
8.3.2 Evaluation
271(2)
8.3.3 Overall Procedure
273(3)
8.3.4 Numerical Examples
276(2)
8.4 Bicriteria Solid Transportation Problem
278(5)
8.4.1 Formulation of BSTP
278(1)
8.4.2 Initialization
279(1)
8.4.3 Genetic Operation
279(2)
8.4.4 Numerical Examples
281(2)
8.5 Fuzzy Multicriteria Solid Transportation Problem
283(9)
8.5.1 Problem Formulation
283(1)
8.5.2 Genetic Algorithm Approach
284(5)
8.5.3 Numerical Example
289(3)
9 Facility Layout Design Problems
292(38)
9.1 Introduction
292(1)
9.2 Machine Layout Problem
293(2)
9.3 Single-Row Machine Layout Problem
295(4)
9.3.1 Mathematical Model
295(1)
9.3.2 Genetic Algorithm for Single-Row Machine Layout Problem
296(3)
9.4 Multiple-Row Machine Layout Problem
299(11)
9.4.1 Mathematical Model
299(2)
9.4.2 Representation
301(3)
9.4.3 Initialization
304(2)
9.4.4 Crossover
306(1)
9.4.5 Mutation
307(2)
9.4.6 Evaluation Function
309(1)
9.4.7 Example
310(1)
9.5 Fuzzy Facility Layout Problem
310(20)
9.5.1 Facility Layout Problem
312(1)
9.5.2 Fuzzy Interflow
312(2)
9.5.3 Representation
314(1)
9.5.4 Initialization
314(2)
9.5.5 Crossover
316(1)
9.5.6 Mutation
317(2)
9.5.7 Constructing a Layout from a Chromosome
319(4)
9.5.8 Evaluation and Selection
323(1)
9.5.9 Numerical Example
323(7)
10 Selected Topics in Engineering Design
330(50)
10.1 Resource-Constrained Project Scheduling Problems
330(11)
10.1.1 Problem Statement
331(1)
10.1.2 Hybrid Genetic Algorithms
332(6)
10.1.3 Examples
338(3)
10.2 Fuzzy Vehicle Routing and Scheduling Problem
341(18)
10.2.1 Problem Formulation
342(5)
10.2.2 Related Genetic Algorithm Studies
347(1)
10.2.3 Hybrid Genetic Algorithm
348(9)
10.2.4 Experimental Results
357(2)
10.3 Location-Allocation Problem
359(7)
10.3.1 Location-Allocation Model
360(1)
10.3.2 Hybrid Evolutionary Method
361(4)
10.3.3 Numerical Example
365(1)
10.4 Obstacle Location-Allocation Problem
366(7)
10.4.1 Obstacle Location-Allocation Model
367(3)
10.4.2 Feasibility of Location
370(1)
10.4.3 Shortest Path of Avoiding Obstacles
370(1)
10.4.4 Hybrid Evolutionary Method
370(1)
10.4.5 Case Study
371(2)
10.5 Production Plan Problem
373(7)
10.5.1 Formulation of Production Plan Problem
374(1)
10.5.2 Evolution Program for Production Plan Problem
375(3)
10.5.3 Example
378(2)
Bibliography 380(27)
Index 407


MITSUO GEN, PhD, is a professor in the Department of Industrial andSystems Engineering at the Ashikaga Institute of Technology inJapan. An associate editor of the Engineering Design and AutomationJournal and Journal of Engineering Valuation & Cost Analysis,he is also a member of the international editorial advisory boardof Computers & Industrial Engineering. He is the author of twoother books, Linear Programming Using Turbo C and Goal ProgrammingUsing Turbo C.

RUNWEI CHENG, PhD, is a visiting associate professor at theAshikaga Institute of Technology in Japan and also an associateprofessor at the Institute of Systems Engineering at NortheastUniversity in China. Both authors are internationally known expertsin the application of genetic algorithms and artificialintelligence to the field of manufacturing systems.
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