| 000 | 11290nam a22005173i 4500 | ||
|---|---|---|---|
| 001 | EBC3021711 | ||
| 003 | MiAaPQ | ||
| 005 | 20240729124153.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 240724s2012 xx o ||||0 eng d | ||
| 020 |
_a9781624173615 _q(electronic bk.) |
||
| 020 | _z9781612095790 | ||
| 035 | _a(MiAaPQ)EBC3021711 | ||
| 035 | _a(Au-PeEL)EBL3021711 | ||
| 035 | _a(CaPaEBR)ebr10683451 | ||
| 035 | _a(OCoLC)834136812 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
||
| 050 | 4 | _aT57.74 -- .M36 2012eb | |
| 082 | 0 | _a519.7/2 | |
| 100 | 1 | _aMann, Zoltán Ádám. | |
| 245 | 1 | 0 |
_aLinear Programming - New Frontiers in Theory and Applications : _bNew Frontiers in Theory and Applications. |
| 250 | _a1st ed. | ||
| 264 | 1 |
_aHauppauge : _bNova Science Publishers, Incorporated, _c2012. |
|
| 264 | 4 | _c©2012. | |
| 300 | _a1 online resource (391 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 490 | 1 | _aMathematics Research Developments | |
| 505 | 0 | _aIntro -- LINEAR PROGRAMMINGNEW FRONTIERS INTHEORY AND APPLICATIONS -- MATHEMATICS RESEARCH DEVELOPMENTS -- LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA -- CONTENTS -- PREFACE -- Linear Programming: A Multidisciplinary Success Story -- PART I. THEORY -- TWO-STAGE STOCHASTIC MIXED INTEGER LINEAR PROGRAMMING -- Abstract -- 1. Introduction -- 2. Introduction to 2S-MILP -- 3. A Novel Dynamic 2S-MILP Formulation and Solution Approaches for Multi-Period Multi-Uncertainty -- 3.1. Dynamic 2S-MILP Formulation under Multi-Period Multi-Uncertainty -- 3.2. Evolution of MPMU and Rolling Horizon Strategy / Moving Horizon Strategy -- 3.3. Static MPMU and Scenario Group Based Approach (SGA) -- 4. The Extension of EPS Logistics to 2S-MILP -- 4.1. DDEP Extension for RHS / MHS -- 4.2. DDEP-S2S Extension for SGA -- 4.2.1. The First Solution Step -- 4.2.2. The Second Solution Step -- 4.2.3. The Remaining Solution Steps -- 5. Numerical Results -- 5.1. EPS-DDEP-S2S Models on SGA -- 5.1.1. Results Based on Scenarios -- 5.1.2. Results for the First Two Periods -- 5.1.3.The Comparison between 2S-MILP and EEV -- 5.2. EPS-DDEP Models on RHS -- 5.3. EPS-DDEP Models on MHS -- 5.3.1. Multi-Period Demand Uncertainties (MPDU) -- 5.3.2. Multi-Period Multi-Uncertainty (MPMU) -- 5.3.2.1. Case 1: Multi-Period Plant Capacity and Demand Uncertainties (CDU) -- 5.3.2.2. Case 2: Multi-Period Yields, Plant Capacity and Demand Uncertainties (YCDU) -- 6. Conclusion -- Appendix A: Problem Data -- A.1. Grain Size Distributions -- A.2. Problem Data for Section 5.2 -- A.3. Problem Data for Section 5.3.1 -- A.4. Problem Data for Section 5.3.2.1 -- A.5. Problem Data for Section 5.3.2.2 -- References -- INTERVAL LINEAR PROGRAMMING: A SURVEY -- Abstract -- 1.Introduction -- 1.1.Intervalcomputing -- 1.2.Intervallinearprogramming -- 2.Basicquestions -- 2.1.Feasibility -- 2.2.Unboundedness. | |
| 505 | 8 | _a2.3.Optimality -- 3.Optimalvaluerange -- 4.Setofoptimalsolutions -- 5.Basisstability -- 5.1. B-stability -- 5.2.Non-degenerate B-stability -- 6.Specialcases -- 6.1.Intervalright-handside -- 6.2.Intervalobjectivefunctioncoefficients -- 7.Duality -- 8.Conclusion -- 8.1.Applications -- 8.2.Software -- 8.3.Inverseproblems -- 8.4.Openproblems -- References -- THE INFINITE-DIMENSIONALLINEAR PROGRAMMING PROBLEMSAND THEIR APPROXIMATION -- Abstract -- 1.Introduction -- 2.DualSpacesandDualOperators -- 3.Infinite-DimensionalLinearProgramming -- 4.ApproximationandInterpolationofSpacesandOperators -- 5.ApproximationoftheLinearProgrammingProblems -- 6.NoetherOperatorsinLinearSpaces -- References -- PART II. APPLICATIONS IN MATHEMATICS -- A POLYNOMIAL-TIME APPROXIMATION ALGORITHM FOR MAXIMUM CONCURRENT FLOW PROBLEMS -- Abstract -- 1. Introduction -- 2. Preliminaries and Problem Formulation -- 3. Approximate Optimality Conditions and Exponential Length Functions -- 4. Analysis of the Combinatorial approximation Algorithm- CACF -- 5. Implementations and Applications -- 5.1. Implementations and Computational Results -- Conclusion and Discussions -- Acknowledgments -- References -- MINIMIZINGA REGULAR FUNCTIONON UNIFORMMACHINESWITH ORDERED COMPLETION TIMES -- Abstract -- 1.Introduction -- 2.Problem Q | rj , pmtn,Dj,C1 . .. Cn | F -- 3.Problem Q | rj , pmtn,Dj,C1 . .. Cn | PwjTj -- 4.Conclusion -- Acknowledgements -- References -- PART III. PRACTICAL APPLICATIONS -- LINEAR PROGRAMMING FOR IRRIGATION SCHEDULING - A CASE STUDY -- ABSTRACT -- NOTATIONS -- 1. INTRODUCTION -- 2. THE MODEL FORMULATION AND CONCEPT -- 3. THE CONCEPTUAL MODEL -- Variation of Evapotranspiration with the Available Soil Moisture -- Root Zone Depth Growth -- Relative Yield Ratio -- Water Requirements of the Crops -- 4. INTEGRATED LP FORMULATION -- 5. CROP SIMULATION MODEL. | |
| 505 | 8 | _a6. STOCHASTIC ANALYSIS OF EVAPOTRANSPIRATION -- 7. LP MODEL FORMULATION FOR OPTIMAL CROPPING PATTERN -- Model Application -- Real-Time Operation Program -- Main Program -- Subroutine for Framing Constraints -- LP Subroutine -- Post Processing Routine -- Updating Subroutine -- Analysis -- 10. RESULTS AND DISCUSSION -- Optimum Crop Pattern -- Results from Real-Time Operation Model (LP) -- Relative Yield Ratios -- CONCLUSION -- REFERENCES -- LINEAR PROGRAMMING FOR DAIRY HERD SIMULATION AND OPTIMIZATION: AN INTEGRATED APPROACH FOR DECISION-MAKING -- Abstract -- Introduction -- Dairy Herd Population Dynamics -- Markov-Chain Simulation -- Dynamic Transition Matrices -- The Need for Dynamic Programming -- The Need for Linear Programming -- Overview of the Next Sections -- Formulation of Linear Programming to Solve a Dynamic Programming Optimization Problem for Dairy Herd Management -- Mathematical Formulation -- Matrix Formulation -- 1. Set the Dimensions of the Model -- 2. Define the Transition Matrices -- 2.1. Involuntary Culling and Mortality -- 2.2. Reproduction and Abortion -- 2.3. Milk Production -- 3. Define the Expected Net Returns -- 3.1. Income over Feed Cost (IOFC) -- 3.2. Involuntary Culling Cost -- 3.3. Mortality Cost -- 3.4. Reproduction Cost -- 3.5. Income from Calving -- 4. Setting up the Model -- 5. Solve the Model -- 6. Analyze the Results -- Applications Using Linear Programming for Dairy Herd Simulation and Optimization -- 1. Monthly Model -- 2. Event-Driven Model -- Conclusion -- References -- A REVIEW ON LINEAR PROGRAMMING (LP) AND MIXED INTEGER LINEAR PROGRAMMING (MILP) APPLICATIONS IN CHEMICAL PROCESS SYNTHESIS -- Abstract -- Introduction -- Representation of Alternatives -- Developing Unit Models for Linear Mass Balances -- Mixer Unit -- Splitter Unit -- Reactor (Fixed Conversion Model) -- Flash Units -- Case1: / and P or T Fixed. | |
| 505 | 8 | _aCase 2: T and P Fixed -- Case 3: / and P (or T) Fixed -- Inclusion of Logic Inference in MILP Models -- Example 1. Reaction Path Synthesis (1) -- Example 2. Reaction Path Synthesis (2) -- Extension of Logic Inference to Heuristic Rules -- Modeling of Disjunctions -- Aggregated Process Optimization -- Example 3. Optimization of a Thermal Cracker via Linear Programming -- Example 4. Synthesis of a Chemical Plant -- Example 5. Superstructure Optimization by MILP Approach -- Reactor Networks -- Example 6. Stoichiometric Reaction Network -- Utility Systems -- Example 7. Boiler/Turbo-Generator System Optimization -- Utility Plant Synthesis through MILP Optimization -- Example 8. Utility Plant Synthesis -- Separation Systems -- Example 9. Membrane Separation -- Sequences of Distillation Columns -- Example 10 -- LP and MILP in Heat Exchanger Networks (HEN) -- Minimum Utilities Cost -- Example 11 -- Minimum Number of Heat Exchangers -- Conclusions -- References -- A MEDIUM-TERM PRODUCTION PLANNING PROBLEM: THE EPS LOGISTICS -- Abstract -- 1. Introduction -- 2. Introduction to the EPS Logistics -- 2.1. Manufacturing Section -- 2.2. Marketing Section -- 3. The MILP Model of EPS Logistics -- 3.1. Parameters and Variables -- 3.2. The MILP Model -- 3.3. Numerical Results -- Conclusion -- Appendix A: Analysis on Constraints of Finishing Line Operations -- A.1. Entity Analysis -- A.2. Boundary Analysis -- Appendix B: Analysis on Constraints of Material Supply Balance -- B.1. Entity Analysis -- B.1.1. Accumulation Balance -- B.1.2. Period Balance -- B.2. Mathematic Proof -- References -- COMPLEXITYOF DIFFERENT ILPMODELSOFTHEFREQUENCY ASSIGNMENT PROBLEM -- Abstract -- 1.Introduction -- 2.FrequencyAssignmentProblems -- 2.1.CommonApplicationDomains -- 2.1.1.MobilePhoneNetworks -- 2.1.2.RadioandTelevisionTransmission -- .1.3.MilitaryApplications. | |
| 505 | 8 | _a2.1.4.SatelliteCommunication -- 2.1.5.WirelessLocalAreaNetworks -- 2.2.ModelsofFrequencyPlanning -- 2.2.1.GeneralConstraints -- 2.2.2.F-FAP -- 2.2.3.Max-FAP -- 2.2.4.MO-FAP -- 2.2.5.MS-FAP -- 2.2.6.MI-FAP -- 3.ILPFormulationsofFAP -- 3.1.UsingBinaryVariables -- 3.1.1.F-FAP(Feasibility-FAP) -- 3.1.2.Max-FAP(MaximumServiceFAP) -- 3.1.3.MO-FAP(MinimumOrderFAP) -- 3.1.4.MS-FAP(MinimumSpanFAP) -- 3.2.UsingIntegerVariables -- 3.2.1.LinearizingAbsoluteValues -- 3.2.2.F-FAP(Feasibility-FAP) -- 3.2.3.MS-FAP(MinimumSpanFAP) -- 4.EmpiricalMeasurements -- 4.1.Implementation -- 4.1.1.TestingProcess -- 4.2.ComplexityofF-FAP -- 4.2.1.ConstantNumberofFrequenciesandCommunicationChannels -- 4.2.2.VaryingNumberofCommunicationChannels -- 4.2.3.VaryingNumberofFrequencies -- 4.3.ComparingDifferentILPFormulations -- 4.4.Sub-optimalSolutionsoftheOptimizationVersionsof FAP -- 4.5.AccelerationwithRestarts -- 5.Conclusion -- Acknowledgements -- ListofAbbreviations -- References -- OPTIMIZATION OF POLYGENERATION SYSTEMS SERVING A CLUSTER OF BUILDINGS -- Abstract -- Nomenclature -- Greek Letters -- Superscripts -- Subscripts -- Introduction -- Method for the Optimization of Polygeneration Systems -- Cost Models for the Main Plant Components -- Phisical Model and Mathematical Problem Formulation -- Single Building Optimization (SBO) -- Multi Building Optimization (MBO) -- Implementation Issues -- Power Plant Optimization and Smart Building Aggregation Algorithm -- Computational Burden -- The MATLAB Graphic User Interface -- Conclusions -- References -- LINEAR PROGRAMMING APPLIED FOR THE OPTIMIZATION OF HYDRO AND WIND ENERGY RESOURCES -- Abstract -- 1. Introduction -- Nomenclature -- 2. Problem Formulation -- 2.1. Short-Term Hydro Scheduling -- 1. Objective Function -- 2. Hydro Constraints -- 2.2. Development of Offering Strategies for Wind Power Producers. | |
| 505 | 8 | _a1. Objective Function. | |
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 650 | 0 | _aLinear programming. | |
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aMann, Zoltn DM. | |
| 776 | 0 | 8 |
_iPrint version: _aMann, Zoltán Ádám _tLinear Programming - New Frontiers in Theory and Applications _dHauppauge : Nova Science Publishers, Incorporated,c2012 _z9781612095790 |
| 797 | 2 | _aProQuest (Firm) | |
| 830 | 0 | _aMathematics Research Developments | |
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=3021711 _zClick to View |
| 999 |
_c61354 _d61354 |
||