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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... nodes and a set A of unordered pairs of nodes called arcs. The descriptions of the models are as follows. 2.1.1. Shortest. Path. Model: Node. Selection. and. Sequencing. It is desired to find a set of nodes. What is the shortest path ...
... node set N = {1,2,···,n} is specified by: 1. The number of nodes n; 2. n lists S1 ,···,S i ,···,S n, where Si contains all nodes j for which G contains an arc (i, j). The digraph G of Fig. 2.2 may be represented by adjacency lists as ...
... nodes N = {1, 2, ···, n} and a set of directed arcs A = {(i,j), (k,l), ···, (s,t)} connecting m pairs of nodes in N.Arc(i, j) is said to be incident with nodes i and j, and is directed from node i to node j. Suppose that each arc (i,j) ...
... nodes). This assumption is essentially for notational convenience. Indices i, j,k : index of node (1, 2, ···, n) Parameters n : number of nodes cij: transmission cost of arc (i, j) dij : transmission delay of arc (i, j) Decision ...
... nodes other than s or t. That is, what goes out of node i, ∑nj=1 xij must be equal to what comes in, ∑nk=1 xki. 2.2.2 Priority-based GA for SPP Models Let P(t) and C(t) be parents and offspring in current generation t, respectively ...
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 |