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Operations research

Author: S R Yadav; A K Malik
Publisher: New Delhi, India : Oxford University Press, 2014.
Series: Oxford higher education
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Operations Research is designed as a textbook for undergraduate students of Mechanical Engineering. It is intended to help them understand and apply operations research techniques to industrial  Read more...

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Genre/Form: Electronic books
Additional Physical Format: Print version:
Yadav, S.R.
Operations research
(DLC) 2015472137
(OCoLC)896901482
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: S R Yadav; A K Malik
ISBN: 9781680158786 1680158783
OCLC Number: 928023157
Description: 1 online resource (xvi, 691 pages) : illustrations
Contents: 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 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 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 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 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 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 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 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 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 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 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 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.
Series Title: Oxford higher education
Responsibility: S.R. Yadav, A.K. Malik.

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Primary Entity

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    schema:description "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"@en ;
    schema:description "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"@en ;
    schema:description "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"@en ;
    schema:description "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"@en ;
    schema:description "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"@en ;
    schema:description "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"@en ;
    schema:description "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"@en ;
    schema:description "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."@en ;
    schema:description "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"@en ;
    schema:description "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"@en ;
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    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/2234559018#Person/malik_a_k> # A. K. Malik
    a schema:Person ;
    schema:familyName "Malik" ;
    schema:givenName "A. K." ;
    schema:name "A. K. Malik" ;
    .

<http://experiment.worldcat.org/entity/work/data/2234559018#Person/yadav_s_r> # S. R. Yadav
    a schema:Person ;
    schema:familyName "Yadav" ;
    schema:givenName "S. R." ;
    schema:name "S. R. Yadav" ;
    .

<http://experiment.worldcat.org/entity/work/data/2234559018#Series/oxford_higher_education> # Oxford higher education
    a bgn:PublicationSeries ;
    schema:hasPart <http://www.worldcat.org/oclc/928023157> ; # Operations research
    schema:name "Oxford higher education" ;
    .

<http://experiment.worldcat.org/entity/work/data/2234559018#Topic/civil_&_environmental_engineering> # Civil & Environmental Engineering
    a schema:Intangible ;
    schema:name "Civil & Environmental Engineering"@en ;
    .

<http://experiment.worldcat.org/entity/work/data/2234559018#Topic/engineering_&_applied_sciences> # Engineering & Applied Sciences
    a schema:Intangible ;
    schema:name "Engineering & Applied Sciences"@en ;
    .

<http://experiment.worldcat.org/entity/work/data/2234559018#Topic/operations_research> # Operations Research
    a schema:Intangible ;
    schema:name "Operations Research"@en ;
    schema:name "Operations research"@en ;
    .

<http://worldcat.org/isbn/9781680158786>
    a schema:ProductModel ;
    schema:isbn "1680158783" ;
    schema:isbn "9781680158786" ;
    .

<http://www.worldcat.org/oclc/896901482>
    a schema:CreativeWork ;
    rdfs:label "Operations research" ;
    schema:description "Print version:" ;
    schema:isSimilarTo <http://www.worldcat.org/oclc/928023157> ; # Operations research
    .

<https://login.libproxy.uregina.ca:8443/login?url=http://app.knovel.com/hotlink/toc/id:kpOR000006/operations-research>
    rdfs:comment "Full text available from Knovel, Academic, General Engineering & Project Administration" ;
    .


Content-negotiable representations

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