000 03834nam a22005297a 4500
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007 cr nn 008mamaa
008 121009s2013 xxk| s |||| 0|eng d
020 _a9781447143512
_9978-1-4471-4351-2
024 7 _a10.1007/978-1-4471-4351-2
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aTEC004000
_2bisacsh
082 0 4 _a629.8
_223
100 1 _aNúñez, Alfredo A.
_eauthor.
245 1 0 _aHybrid Predictive Control for Dynamic Transport Problems
_h[electronic resource] /
_cby Alfredo A. Núñez, Doris A. Sáez, Cristián E. Cortés.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aXX, 172 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Industrial Control,
_x1430-9491
505 0 _aHybrid Predictive Control: Mono-objective and Multi-objective Design -- Hybrid Predictive Control for a Dial-a-ride System -- Hybrid Predictive Control for Operational Decisions in Public Transport Systems.
520 _aHybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ●hybrid predictive control (HPC) design based on evolutionary multiobjective optimization (EMO); ●HPC based on EMO for dial-a-ride systems; and ●HPC based on EMO for operational decisions in public transport systems. Hybrid Predictive Control for Dynamic Transport Problems is a comprehensive analysis of HPC and its application to dynamic transport systems. Introductory material on evolutionary algorithms is presented in summary in an appendix. The text will be of interest to control and transport engineers working on the operational optimization of transport systems and to academic researchers working with hybrid systems. The potential applications of the generic methods presented here in other process fields will appeal to a wider group of researchers, scientists and graduate students working in other control-related disciplines.
650 0 _aEngineering.
650 0 _aOperations research.
650 0 _aDecision making.
650 0 _aManagement science.
650 0 _aControl engineering.
650 1 4 _aEngineering.
650 2 4 _aControl.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aOperation Research/Decision Theory.
700 1 _aSáez, Doris A.
_eauthor.
700 1 _aCortés, Cristián E.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447143505
830 0 _aAdvances in Industrial Control,
_x1430-9491
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-4351-2
912 _aZDB-2-ENG
942 _2Dewey Decimal Classification
_ceBooks
999 _c43556
_d43556