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|a 1058130490
|a 1136220534
|a 1167585806
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|a 9781680158786
|q (electronic bk.)
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|a 1680158783
|q (electronic bk.)
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|z 9780198096184
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|z 0198096186
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|a (OCoLC)928023157
|z (OCoLC)1058130490
|z (OCoLC)1136220534
|z (OCoLC)1167585806
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|a T57.6
|b .Y33 2014eb
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|a 658.4034
|2 23
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|a UAMI
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|a Yadav, S. R.,
|e author.
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1 |
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|a Operations research /
|c S.R. Yadav, A.K. Malik.
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| 264 |
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|a New Delhi, India :
|b Oxford University Press,
|c 2014.
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| 300 |
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|a 1 online resource (xvi, 691 pages) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Oxford higher education
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|a Print version record.
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|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
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|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
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|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
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|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
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|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
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|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
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|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
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|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
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|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
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|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
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|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
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|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.
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|a English.
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|a Knovel
|b ACADEMIC - General Engineering & Project Administration
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|a Operations research.
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|a Operations Research.
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|a Civil & Environmental Engineering.
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|a Engineering & Applied Sciences.
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|a Recherche opérationnelle.
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|a Operations Research.
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|a Engineering & Applied Sciences.
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|a Malik, A. K.,
|e author.
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|i Print version:
|a Yadav, S.R.
|t Operations research
|z 9780198096184
|w (DLC) 2015472137
|w (OCoLC)896901482
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| 856 |
4 |
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|u https://appknovel.uam.elogim.com/kn/resources/kpOR000006/toc
|z Texto completo
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|6 505-01/(S
|a Contents note continued: 13.7. Branch and Bound Technique to Find Solution of IPP -- 13.8. Zero--One Integer Programming Problem -- 13.8.1. Format of Balas Zero--One Additive Algorithm -- 13.8.2. Some Important Terms used in Balas Additive Algorithm -- 13.8.3. Solution Procedure of Zero--One IPP -- 14. Queuing Theory -- 14.1. Introduction -- 14.2. Basic Elements of Queuing Systems -- 14.3. Markovian Queues -- 14.4. Terminology and Notations used in Queuing Systems -- 14.5. Symbolic Representation of Queuing Models -- 14.6. Probability Distribution of n Arrivals in Time Interval (t, t+Δt) in Pure Birth Processes -- 14.7. Distribution of Inter-arrivals Time -- 14.8. Distribution of Departures in Pure Death Processes -- 14.9. Birth and Death Process -- 14.10. Various Queuing Models with their Characteristic Properties -- 14.10.1. Model-I---(M/M/I): (FCFS/[∞]) -- 14.10.2. Finite Storage Queue System with One Server (M/M/I):(FCFS/N) -- 14.10.3.S-server Case (M/M/S): (FCFS/[∞])
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