| Description |
1 online resource (267 p.) |
| Series |
Mathematical Engineering, Manufacturing, and Management Sciences Series |
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Mathematical Engineering, Manufacturing, and Management Sciences Series
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| Contents |
Cover -- Half Title -- Series -- Title -- Copyright -- Contents -- Acknowledgments -- Biography of editors -- List of contributors -- 1 Ant colony optimization algorithm for the university course timetabling problem using events based on groupings of students -- 1.1 Introduction -- 1.2 Related work -- 1.3 Statement of university course timetabling problem -- 1.4 Ant colony optimization -- 1.5 The proposed algorithm -- 1.6 Experimental results -- 1.6.1 Experiments on Socha's benchmark dataset -- 1.6.2 Experiments on ITC2007 benchmark dataset -- 1.7 Conclusions -- References |
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2 Genetic optimization and their applications -- 2.1 Introduction -- 2.1.1 Difficulties faced -- 2.1.2 Evolutionary algorithms -- 2.2 Classical optimization techniques -- 2.2.1 Single variable function optimization -- 2.2.2 Multi-variable function optimization -- 2.2.3 A general optimization algorithm -- 2.3 Modern optimization techniques -- 2.3.1 Simulated annealing -- 2.3.2 Particle swarm optimization -- 2.3.3 Ant colony optimization -- 2.3.4 Honey bee optimization -- 2.3.5 Genetic algorithms -- 2.4 Proposed method -- 2.4.1 Input -- 2.4.2 Convolution -- 2.4.3 Genetic optimizer for CNN (GOCNN) |
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2.4.4 Case study on FER -- 2.5 Conclusion -- References -- 3 Different variants of unreliable server: an economic approach -- 3.1 Introduction -- 3.2 Service system with server breakdown -- 3.3 Service system with working breakdown of the server -- 3.4 Service system with customer impatience, WB, service pressure condition, and threshold recovery policy -- 3.5 Special cases -- 3.6 Cost analysis -- 3.6.1 Steady-state analysis -- 3.6.2 Cost function -- 3.6.3 Quasi-Newton method -- 3.7 Numerical results and discussion -- 3.8 Conclusion -- References |
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5.4 Approximation of piecewise linear membership function -- 5.5 STP with unit transportation costs represented as fuzzy numbers -- 5.5.1 Solution methodology -- 5.5.2 A methodology using the concept minimum of fuzzy number -- 5.6 Numerical example -- 5.6.1 Solution using proposed methodology as in Section 5.1 -- 5.6.2 Solution using methodology as in Section 5.2 -- 5.7 Conclusion -- References -- 6 Profit maximization inventory model for non-instantaneous deteriorating items with imprecise costs -- 6.1 Introduction -- 6.2 Assumptions and notations -- 6.2.1 Assumptions -- 6.2.2 Notations |
| Notes |
Description based upon print version of record |
|
6.3 Mathematical formulation |
| Subject |
Systems engineering -- Data processing
|
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Operations research -- Data processing
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Operations research -- Data processing
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Systems engineering -- Data processing
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| Form |
Electronic book
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| Author |
Varshney, Shreekant
|
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Shekhar, Chandra
|
| ISBN |
9781003801399 |
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1003801390 |
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