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. |
From inside the book
Results 1-5 of 77
... shown in Fig. 1.3. They are often misused in the literature. Infeasibility refers to the phenomenon that a solution decoded from a chromosome lies outside the feasible region of a given problem, while illegality refers to the phenomenon ...
... shown in Fig. 1.13a. The extreme points in the feasible region are x1(0,0), x2(1,0), x3(2/3,2/3),andx4(0,1).The feasible region Z in the criterion space is obtained by mapping set S by using two objectives (Eqs. 1.19 and 1.20), as shown ...
... shown in Fig. 1.16a. At generation 10, population converged towards to the nondominated region as shown in Fig. 1.16b. At generation 100, almost the whole population fell in the region of nondominated solutions as shown in Fig. 1.16c ...
... shown in Fig. 1.24. The rectangle defined by the extreme points (zmax1, zmin2 )and (zmin1, zmax2) is the minimal rectangle containing all current solutions. As show in Fig. +z 1 z 2 z min 2 z max 1 z max 2 z min 1 z subspace ...
... shown in Fig. 1.27: i ≺ j if (ri < rj) or ((ri = rj) and (di > dj)) Interactive Adaptive-weight GA (i-awGA: Lin and Gen [46]): Generally, the main idea of the Pareto ranking-based approach is a clear classification between nondomi ...
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 |