Operations research
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| Main Authors | , |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
New Delhi, India :
Oxford University Press,
2014.
|
| Series | Oxford higher education
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9781680158786 1680158783 9780198096184 0198096186 |
| Physical Description | 1 online resource (xvi, 691 pages) : illustrations |
Cover
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | kn-ocn928023157 | ||
| 003 | OCoLC | ||
| 005 | 20240717213016.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 151106s2014 enka o 000 0 eng d | ||
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| 020 | |a 9781680158786 |q (electronic bk.) | ||
| 020 | |a 1680158783 |q (electronic bk.) | ||
| 020 | |z 9780198096184 | ||
| 020 | |z 0198096186 | ||
| 035 | |a (OCoLC)928023157 |z (OCoLC)1058130490 |z (OCoLC)1136220534 |z (OCoLC)1167585806 | ||
| 100 | 1 | |a Yadav, S. R., |e author. | |
| 245 | 1 | 0 | |a Operations research / |c S.R. Yadav, A.K. Malik. |
| 264 | 1 | |a New Delhi, India : |b Oxford University Press, |c 2014. | |
| 300 | |a 1 online resource (xvi, 691 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Oxford higher education | |
| 505 | 0 | |6 880-01 |a Machine generated contents note: 1. Introduction to Operations Research -- 1.1. Introduction -- 1.2. Historical Development -- 1.3. Definitions -- 1.4. Models -- 1.5. Scope and Applications -- 1.6. Phases -- 2. Linear Programming Problem I -- Formulation -- 2.1. Introduction -- 2.2. Linear Programming Problem -- 2.3. Basic Assumptions of Linear Programming Problem -- 2.4. Formulation of Linear Programming Model -- 2.5. Limitations of Linear Programming Problem -- 2.6. Applications of Linear Programming Problem in Business and Industries -- 3. Linear Programming Problem II -- Graphical Method -- 3.1. Introduction -- 3.2. Some Definitions -- 3.3. Some Important Theorems -- 3.4. Graphical Method -- 3.4.1. Corner Point Method -- 3.4.2. Iso-profit Method or Isovalue Line Method -- 3.5. Special Cases in Graphical Method -- 3.5.1. Alternate Optimal Solution -- 3.5.2. Mo Feasible Solution -- 3.5.3. Unbounded Solution Space but Bounded Optimal Solution -- 3.5.4. Unbounded Solution Space and Unbounded Solution | |
| 505 | 0 | |a Note continued: 3.6. Limitations of Graphical Method -- 4. Linear Programming Problem III -- Simplex Method -- 4.1. Introduction -- 4.2. Standard Form of Linear Programming Problem -- 4.3. Some Important Terminologies -- 4.4. Some Important Resolutions used in LPP for Simplex Method -- 4.5. Simplex Method -- 4.6. Simplex Table -- 4.7. Criteria of Optimality -- 4.8.Computational or Iterative Procedure for Solving Linear Programming Problem using Simplex Method -- 4.9. Special Cases in Simplex Method -- 4.9.1. Infeasibility -- 4.9.2. Unboundedness -- 4.9.3. Degeneracy -- 4.9.4. Alternate or More Than One Optimal Solution -- 4.9.5. Cycling -- 4.10. Artificial Variable Technique for Solving Linear Programming Problems -- 4.10.1. Big-M Method -- 4.10.2. Two-phase Method -- 4.10.3.Comparison between Big-M and Two-phase Methods -- 4.11. Solving Simultaneous Linear Equations using Simplex Method -- 4.12. Finding Inverse of Square Matrix using Simplex Method | |
| 505 | 0 | |a Note continued: 5. Linear Programming Problem IV -- Revised Simplex Method -- 5.1. Introduction -- 5.2. Revised Simplex Method -- 5.3.Computational Procedure for Solving LPP using Revised Simplex Method -- 6. Duality in Linear Programming -- 6.1. Introduction -- 6.2. Symmetric Form -- 6.3. Definition of Dual of Linear Programming Problem -- 6.4. Primal -- Dual Relationship -- 6.5. Economic Interpretation of Duality -- 6.6. Important Theorems -- 6.7. Dual Simplex Method -- 6.7.1. Procedure for Solving Linear Programming Problems -- 7. Post-optimality Analysis or Sensitivity Analysis -- 7.1. Introduction -- 7.2. Changes Affecting Feasibility and Optimality -- 7.3. Graphical Sensitivity Analysis -- 7.4. Changes in Cost cj in Objective Function -- 7.5. Changes in bi's availabilities -- 7.6. Addition of New Variables -- 7.7. Deletion of Constraints -- 7.8. Deletion of Variables -- 7.9. Addition of Constraints -- 7.10. Change in Column Aj of Coefficient Matrix A -- 7.11. Parametric Linear Programming | |
| 505 | 0 | |a Note continued: 7.11.1. Parametric Changes in Cost Vector c -- 7.11.2. Parametric Changes in Requirement Vector b -- 7.12. Difference between Sensitivity Analysis and Parametric Linear Programming -- 8. Transportation Problems -- 8.1. Introduction -- 8.2. Formulation of Transportation Problem -- 8.3. Development of Transportation Algorithm -- 8.4. Solution of Transportation Problem -- 8.4.1. North-west Corner Method -- 8.4.2. Least Cost Entry or Matrix Minima Method -- 8.4.3. Vogel's Approximation Method -- 8.5. Test of Optimality -- 8.5.1. Modified Distribution Method -- 8.5.2. Stepping Stone Method -- 8.6. Degeneracy in Transportation Problem -- 8.7. Unbalanced Transportation Problem -- 8.8. Transshipment Problem -- 9. Assignment Problems -- 9.1. Introduction -- 9.2. Solving Assignment Problems using Hungarian Method -- 9.3. Minimal Assignment Problem -- 9.4. Maximal Assignment Problem -- 9.5. Unbalanced Assignment Problem -- 9.6. Assignment Problems under Certain Restrictions | |
| 505 | 0 | |a Note continued: 9.7. Travelling Salesman Problem -- 9.8. Difference between Assignment and Transportation Problems -- 10. Sequencing -- 10.1. Introduction -- 10.2. Assumptions, Notations, and Terminologies -- 10.2.1. Assumptions -- 10.2.2. Notations -- 10.2.3. Terminologies -- 10.3. Johnson's Algorithm for Processing n Jobs through Two Machines -- 10.4. Johnson's Algorithm for Processing n Jobs through k Machines -- 10.5. Processing Two Jobs through k Machines -- 11. Project Scheduling -- 11.1. Introduction -- 11.2. Project development -- 11.2.1. Planning -- 11.2.2. Scheduling -- 11.2.3. Controlling -- 11.3.Network -- 11.3.1. Notations -- 11.3.2. Fulkerson's Rule for Numbering Events -- 11.4. Critical Path Method -- 11.5. Program Evaluation and Review Technique -- 11.6. Optimum Scheduling by Critical Path Method -- 11.7. Time-Cost Optimization Algorithm -- 12. Dynamic Programming -- 12.1. Introduction -- 12.2. Terminology used in Dynamic Programming -- 12.3. Multi-decision Process | |
| 505 | 0 | |a Note continued: 12.4. Bellman's Principle of Optimality -- 12.5. Characteristics of Dynamic Programming Problems -- 12.6. Dynamic Programming Algorithm -- 12.7. Deterministic and Probabilistic Dynamic Programming -- 12.8. Models of Dynamic Programming -- 12.8.1. Model I -- Shortest Route Problem -- 12.8.2. Model III -- Solving Dynamic Programming using Calculus Method -- 12.8.3. Model III -- 12.9. Solving Linear Programming Problems using Dynamic Programming -- 12.10. Dynamic Programming Problem vs Linear Programming Problem -- 12.11. Applications of Dynamic Programming -- 13. Integer Programming -- 13.1. Introduction -- 13.2. Mathematical Formulation of Integer Programming Problems -- 13.3. Types of Integer Programming Problems -- 13.4. Gomory's Cutting Plane Method for AIPP -- 13.4.1. Algorithm for Gomory's Cutting Plane Method -- 13.5. Gomory's Cutting Plane Method for MIPP -- 13.6. Difference between Gomory's Cutting Plane Method for AIPP and MIPP | |
| 505 | 0 | |a Note continued: 14.10.4.S-server Case with Finite Accommodation Capacity (M/M/S): (FCFS/N) -- 14.11. Advantages of Queuing Theory -- 15. Goal Programming -- 15.1. Introduction -- 15.2. Formulation of Goal Programming -- 15.3. Basic Terminologies -- 15.4. Single-goal Models -- 15.5. GP Algorithm or Modified Simplex Method -- 15.6. Multiple-goal Models -- 15.6.1. Multiple-goal Models with Equal or No Priorities -- 15.6.2. Multiple-goal Models with Priorities -- 15.6.3. Multiple-goal Models with Priorities and Weights -- 15.7. Graphical Solution of Goal Programming Problems -- 16. Game Theory -- 16.1. Introduction -- 16.2. Characteristics of Games -- 16.3. Basic Terminology used in Game Theory -- 16.4. Lower and Upper Value of Game -- 'Minimax' Principle with Pure Strategies -- 16.5. Procedure to Determine Saddle Point -- 16.6. Matrix Reduction by Dominance Principle -- 16.7. Games without Saddle Point -- 16.7.1.2 x 2 Game without Saddle Point -- 16.8.(3 x 3) Games with No Saddle Point | |
| 505 | 0 | |a Note continued: 16.9. Graphical Method for (2 x n) and (m x 2) Games -- 16.9.1. Graphical Method for 2 x n Games -- 16.9.2. Graphical Method for mx2 Games -- 16.10. Method of Submatrices or Subgames for (2 x n) or (m x 2) Games with No Saddle Point -- 16.11. Two-person Zero-sum Game with Mixed Strategies or Linear Programmning Method -- 16.12. Limitations of Game Theory -- 17. Decision Theory -- Analysis -- 17.1. Introduction -- 17.2. Decision Models -- 17.2.1. Decision Alternatives -- 17.2.2. States of Nature or Events -- 17.2.3. Pay-off -- 17.3. Decision-making Situations -- 17.3.1. Decision-making Under Certainty -- 17.3.2. Decision-making Under Risk -- 17.3.3. Decision-making Under Uncertainty or Fuzzy Environment -- 17.3.4. Posterior Probability and Bayesian Analysis -- 17.3.5. Decision-making Under Conflict -- Game Theory -- 18.Networking -- 18.1. Introduction -- 18.2. Definitions and Notations used in Networking -- 18.3. Shortest Route Problem -- 18.4. Minimum Spanning Tree Problem | |
| 505 | 0 | |a Note continued: 18.5. Maximum Flow Problems -- 19. Replacement Models -- 19.1. Introduction -- 19.2. Replacement Policy Models -- 19.3. Replacement Policy When the Value of Money does not Change with Time -- 19.4. Replacement Policy When the Value of Money Changes with Time -- 19.5. Procedure to Select the Better Equipment -- 19.6. Replacement of Equipment that Fails Suddenly -- 19.7. Group Replacement Theorem -- 20. Simulation -- 20.1. Introduction -- 20.2. Basic Terminologies -- 20.3. Random Numbers and Pseudo-random Numbers -- 20.3.1. Mid-square Method or Technique of Generating Pseudo-random Numbers -- 20.3.2. Limitations of Mid-square Method -- 20.3.3. Multiplicative Congruential or Power Residual Technique -- 20.3.4. Mixed Congruential Method -- 20.4. Monte Carlo Simulation -- 20.5. Generation of Random Variates -- 20.5.1. Continuous Random Variable X -- 20.5.2. Discrete Case -- 20.6. Applications of Simulation in Queuing Models -- 20.7. Advantages and Disadvantages of Simulation | |
| 505 | 0 | |a Note continued: 20.8. Simulation Languages -- 21. Inventory Models -- 21.1. Introduction -- 21.2. Inventory -- 21.3. Some Basic Terminologies used in Inventory -- 21.4. Inventory Control -- 21.5. Inventory Costs -- 21.6. Inventory Management and its Benefits -- 21.7. Economic Order Quantity -- 21.7.1. Deterministic Inventory Models with No Shortages -- 21.8. Deterministic Inventory Models with Shortages -- 21.9. EOQ Problem with Price Breaks or Quantity Discount -- 21.10. Probabilistic Inventory Models -- 21.10.1. Single Period Problem without Set-up Cost and Uniform Demand -- 21.10.2. Single Period Problems without Set-up Cost and Instantaneous Demand -- 21.11. Some Important Inventory Control Techniques -- 22. Classical Optimization Techniques -- 22.1. Introduction -- 22.2. Unconstrained Optimization Problems -- 22.2.1. Single-variable Unconstrained Optimization Problems -- 22.2.2. Conditions for Local Maxima or Minima of Single-variable Function | |
| 505 | 0 | |a Note continued: 22.2.3. Procedure to Find Extreme Points of Functions of Single Variables -- 22.3. Multivariable Optimization Problems -- 22.3.1. Working Rule to Find Extreme Points of Functions of Two Variables -- 22.3.2. Working Rule to Find Extreme Points of Functions of n Variables -- 22.4. Multivariable Constrained Optimization Problems with Equality Constraints -- 22.4.1. Direct Substitution Method -- 22.4.2. Lagrange Multipliers Method -- 22.5. Multivariable Constrained Optimization Problems with Inequality Constraints -- 23. Non-linear Programming Problem I -- Search Techniques -- 23.1. Introduction -- 23.2. Unconstrained Non-linear Programming Problem -- 23.3. Direct Search Methods -- 23.4. Search Techniques in One Dimension -- 23.4.1. Fibonacci Method of Search -- 23.4.2. Golden Section Method -- 23.4.3. Univariate Method -- 23.4.4. Pattern Search Methods -- 23.5. Indirect Search Methods -- 23.5.1. Steepest Descent or Cauchy's Method | |
| 505 | 0 | |a Note continued: 23.6. Constrained Non-linear Programming Problems -- 23.7. Direct Methods -- 23.7.1.Complex Method -- 23.7.2. Zoutendijk Method or Method of Feasible Direction -- 23.8. Indirect Methods -- 23.8.1. Transform Techniques -- 23.8.2. Penalty Function Methods -- 23.9. Rosen's Gradient Projection Method -- 24. Non-linear Programming II -- Quadratic and Separable -- 24.1. Introduction -- 24.2. Kuhn -- Tucker Conditions -- 24.3. Quadratic Programming -- 24.3.1. Wolfe s Modified Simplex Method -- 24.3.2. Beak's Method -- 24.4. Separable Programming. | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Operations research. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Malik, A. K., |e author. | |
| 776 | 0 | 8 | |i Print version: |a Yadav, S.R. |t Operations research |z 9780198096184 |w (DLC) 2015472137 |w (OCoLC)896901482 |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpOR000006/operations-research?kpromoter=marc |y Full text |