Network Models and Optimization: Multiobjective Genetic Algorithm ApproachNetwork models are critical tools in business, management, science and industry. Network Models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems. |
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Page 1
Each individual in the population is called a chromosome (or individual), representing a solution to the problem at hand. Chromosome is a string of symbols usually, but not necessarily, a binary bit string.
Each individual in the population is called a chromosome (or individual), representing a solution to the problem at hand. Chromosome is a string of symbols usually, but not necessarily, a binary bit string.
Page 2
others so as to keep the population size constant. Fitter chromosomes have higher probabilities of being selected. After several generations, the algorithms converge to the best chromosome, which hopefully represents the optimum or ...
others so as to keep the population size constant. Fitter chromosomes have higher probabilities of being selected. After several generations, the algorithms converge to the best chromosome, which hopefully represents the optimum or ...
Page 3
At the beginning of genetic search, there is a widely random and diverse population and crossover operator tends to perform wide-spread search for exploring all solution space. As the high fitness solutions develop, the crossover ...
At the beginning of genetic search, there is a widely random and diverse population and crossover operator tends to perform wide-spread search for exploring all solution space. As the high fitness solutions develop, the crossover ...
Page 4
Population-based. Search. Generally, an algorithm for solving optimization problems is a sequence of computational steps which asymptotically converge to optimalsolution. Most classical optimization methods generate a deterministic ...
Population-based. Search. Generally, an algorithm for solving optimization problems is a sequence of computational steps which asymptotically converge to optimalsolution. Most classical optimization methods generate a deterministic ...
Page 9
Solution space Encoding space 1-to-1 mapping 1-to-n mapping n-to-1 mapping 1.2.2.4 Initialization In general, there are two ways to generate the initial population, i.e., the heuristic initialization and random initialization while ...
Solution space Encoding space 1-to-1 mapping 1-to-n mapping n-to-1 mapping 1.2.2.4 Initialization In general, there are two ways to generate the initial population, i.e., the heuristic initialization and random initialization while ...
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Contents
| 1 | |
| 49 | |
Logistics Network Models | 135 |
Communication Network Models | 229 |
Advanced Planning and Scheduling Models | 297 |
Project Scheduling Models | 419 |
Assembly Line Balancing Models | 477 |
Tasks Scheduling Models | 551 |
References | 604 |
Index | 687 |
Other editions - View all
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2008 |
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2010 |
Common terms and phrases
activity aircraft applications approach arcs assembly assignment average balancing begin calculate capacity chromosome complex computational connection considered constraints container cost create crossover decision decoding demand determine developed direct distribution edge encoding evaluate example experiments feasible first fitness flow follows formulated function gene genetic algorithm given graph heuristic Illustration improve initial input Journal logistics machine manufacturing means method minimize mutation node objective obtained offspring operation optimization output parameters parents path performance Phase planning population position precedence priority priority-based probability problem procedure proposed random reliability representation represents requirements Research resource robot roulette wheel selection routine routing schedule selection sequence ship shown in Fig shows simple solution solving space spanning station Step Table task tion transportation tree units weight
Popular passages
Page 179 - A supply chain is a network of facilities and distribution options that performs the functions of procurement of materials, transformation of these materials into intermediate and finished products, and the distribution of these finished products to customers.
Page 14 - The method may work reasonably well when the feasible search space is convex and it constitutes a reasonable part of the whole search space.
Page 20 - This rule states that the ratio of successful mutations to all mutations should be 1/5, hence if the ratio is greater than 1/5 then increase the step size, and if the ratio is less than 1/5 then decrease the step size.
Page 94 - ... replacing the genes lost from the population during the selection process so that they can be tried in a new context or (b) providing the genes that were not present in the initial population.
Page 15 - GA have proved to be a versatile and effective approach for solving optimization problems. Nevertheless, there are many situations in which the simple GA does not perform particularly well, and various methods of hybridization have been proposed. One of most common forms of hybrid genetic algorithm (hGA) is to incorporate local optimization as an add-on extra to the canonical GA loop of recombination and selection.
Page 155 - Logistics management is that part of supply chain management that plans, implements, and controls the efficient, effective forward and reverse flow and storage of goods, Services, and related Information between the point of origin and the point of consumption in order to meet customers
Page 332 - Baker, 1974, p. 185), a non-delay schedule is a schedule where "no machine is kept idle at a time when it could begin processing some operation".
Page 1 - ... rejecting others so as to keep the population size constant. Fitter chromosomes have higher probabilities of being selected. After several generations, the algorithms converge to the best chromosome, which hopefully represents the optimum or suboptimal solution to the problem.
Page 11 - The genetic operators and their significance can now be explained. Crossover. Crossover is the main genetic operator. It operates on two individuals at a time and generates an offspring by combining schemata from both parents. A simple way to achieve crossover would be to choose a random cut point and generate the offspring by combining the segment of one parent to the left of the cut point with the segment of the other parent to the right of the cut point. This method works well with the bit string...
Page 12 - If it is too low, many genes that would have been useful are never tried out; but if it is too high, there will be much random perturbation, the offspring will start losing their resemblance to the parents, and the algorithm will lose the ability to learn from the history of the search.
References to this book
Bibliographic information
| Title | Network Models and Optimization: Multiobjective Genetic Algorithm Approach Decision Engineering |
| Authors | Mitsuo Gen, Runwei Cheng, Lin Lin |
| Edition | illustrated |
| Publisher | Springer Science & Business Media, 2008 |
| ISBN | 1848001819, 9781848001817 |
| Length | 692 pages |
| Subjects | › › Business & Economics / Operations Research Computers / Programming / Algorithms Mathematics / Combinatorics Technology & Engineering / Engineering (General) Technology & Engineering / General |
| Export Citation | BiBTeX EndNote RefMan |