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 موضوع: كتاب Introduction to Operations Research السبت 06 نوفمبر 2021, 10:55 pm  

أخواني في الله أحضرت لكم كتاب Introduction to Operations Research Tenth Edition Frederick S. Hillier Stanford University Gerald J. Lieberman Late of Stanford University
و المحتوى كما يلي :
Table of Contents Preface Xxii Chapter Introduction . The Origins of Operations Research . The Nature of Operations Research . The Rise of Analytics Together with Operations Research . The Impact of Operations Research . Algorithms and OR Courseware Selected References Problems CHAPTER Overview of the Operations Research Modeling Approach . Defining the Problem and Gathering Data . Formulating a Mathematical Model . Deriving Solutions from the Model . Testing the Model . Preparing to Apply the Model . Implementation . Conclusions Selected References Problems CHAPTER Introduction to Linear Programming . Prototype Example . The Linear Programming Model . Assumptions of Linear Programming . Additional Examples . Formulating and Solving Linear Programming Models on a Spreadsheet . Formulating Very Large Linear Programming Models . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Auto Assembly Previews of Added Cases on Our Website Case . Cutting Cafeteria Costs Case . Staffing a Call Center Case . Promoting a Breakfast Cereal xixii CONTENTS CHAPTER Solving Linear Programming Problems: The Simplex Method . The Essence of the Simplex Method . Setting Up the Simplex Method . The Algebra of the Simplex Method . The Simplex Method in Tabular Form . Tie Breaking in the Simplex Method . Adapting to Other Model Forms . Postoptimality Analysis . Computer Implementation . The InteriorPoint Approach to Solving Linear Programming Problems . Conclusions Appendix . An Introduction to Using LINDO and LINGO Selected References Learning Aids for This Chapter on Our Website Problems Case . Fabrics and Fall Fashions Previews of Added Cases on Our Website Case . New Frontiers Case . Assigning Students to Schools CHAPTER The Theory of the Simplex Method . Foundations of the Simplex Method . The Simplex Method in Matrix Form . A Fundamental Insight . The Revised Simplex Method . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems CHAPTER Duality Theory . The Essence of Duality Theory . Economic Interpretation of Duality . Primal–Dual Relationships . Adapting to Other Primal Forms . The Role of Duality Theory in Sensitivity Analysis . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems CHAPTER Linear Programming under Uncertainty . The Essence of Sensitivity Analysis . Applying Sensitivity Analysis . Performing Sensitivity Analysis on a Spreadsheet . Robust Optimization . Chance Constraints . Stochastic Programming with Recourse . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Controlling Air Pollution Previews of Added Cases on Our Website Case . Farm Management Case . Assigning Students to Schools, Revisited Case . Writing a Nontechnical Memo CHAPTER Other Algorithms for Linear Programming . The Dual Simplex Method . Parametric Linear Programming . The Upper Bound Technique . An InteriorPoint Algorithm . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems CHAPTER The Transportation and Assignment Problems . The Transportation Problem . A Streamlined Simplex Method for the Transportation Problem . The Assignment Problem . A Special Algorithm for the Assignment Problem . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Shipping Wood to Market Previews of Added Cases on Our Website Case . Continuation of the Texago Case Study Case . Project Pickings CHAPTER Network Optimization Models . Prototype Example . The Terminology of Networks . The ShortestPath Problem . The Minimum Spanning Tree Problem . The Maximum Flow Problem . The Minimum Cost Flow Problem . The Network Simplex Method . A Network Model for Optimizing a Project’s Time–Cost TradeOff . Conclusions Selected References Learning Aids for This Chapter on Our Website CONTENTS xiiiProblems Case . Money in Motion Previews of Added Cases on Our Website Case . Aiding Allies Case . Steps to Success CHAPTER Dynamic Programming . A Prototype Example for Dynamic Programming . Characteristics of Dynamic Programming Problems . Deterministic Dynamic Programming . Probabilistic Dynamic Programming . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems CHAPTER Integer Programming . Prototype Example . Some BIP Applications . Innovative Uses of Binary Variables in Model Formulation . Some Formulation Examples . Some Perspectives on Solving Integer Programming Problems . The BranchandBound Technique and Its Application to Binary Integer Programming . A BranchandBound Algorithm for Mixed Integer Programming . The BranchandCut Approach to Solving BIP Problems . The Incorporation of Constraint Programming . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Capacity Concerns Previews of Added Cases on Our Website Case . Assigning Art Case . Stocking Sets Case . Assigning Students to Schools, Revisited Again CHAPTER Nonlinear Programming . Sample Applications . Graphical Illustration of Nonlinear Programming Problems . Types of Nonlinear Programming Problems . OneVariable Unconstrained Optimization . Multivariable Unconstrained Optimization . The KarushKuhnTucker (KKT) Conditions for Constrained Optimization . Quadratic Programming xiv CONTENTS . Separable Programming . Convex Programming . Nonconvex Programming (with Spreadsheets) . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Savvy Stock Selection Previews of Added Cases on Our Website Case . International Investments Case . Promoting a Breakfast Cereal, Revisited CHAPTER Metaheuristics . The Nature of Metaheuristics . Tabu Search . Simulated Annealing . Genetic Algorithms . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems CHAPTER Game Theory . The Formulation of TwoPerson, ZeroSum Games . Solving Simple Games—A Prototype Example . Games with Mixed Strategies . Graphical Solution Procedure . Solving by Linear Programming . Extensions . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems CHAPTER Decision Analysis . A Prototype Example . Decision Making without Experimentation . Decision Making with Experimentation . Decision Trees . Using Spreadsheets to Perform Sensitivity Analysis on Decision Trees . Utility Theory . The Practical Application of Decision Analysis . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Brainy Business CONTENTS xvPreview of Added Cases on Our Website Case . Smart Steering Support Case . Who Wants to be a Millionaire? Case . University Toys and the Engineering Professor Action Figures CHAPTER Queueing Theory . Prototype Example . Basic Structure of Queueing Models . Examples of Real Queueing Systems . The Role of the Exponential Distribution . The BirthandDeath Process . Queueing Models Based on the BirthandDeath Process . Queueing Models Involving Nonexponential Distributions . PriorityDiscipline Queueing Models . Queueing Networks . The Application of Queueing Theory . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Reducing InProcess Inventory Preview of an Added Case on Our Website Case . Queueing Quandary CHAPTER Inventory Theory . Examples . Components of Inventory Models . Deterministic ContinuousReview Models . A Deterministic PeriodicReview Model . Deterministic Multiechelon Inventory Models for Supply Chain Management . A Stochastic ContinuousReview Model . A Stochastic SinglePeriod Model for Perishable Products . Revenue Management . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Brushing Up on Inventory Control Previews of Added Cases on Our Website Case . TNT: Tackling Newsboy’s Teaching Case . Jettisoning Surplus Stock CHAPTER Markov Decision Processes . A Prototype Example . A Model for Markov Decision Processes xvi CONTENTS . Linear Programming and Optimal Policies . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems CHAPTER Simulation . The Essence of Simulation . Some Common Types of Applications of Simulation . Generation of Random Numbers . Generation of Random Observations from a Probability Distribution . Outline of a Major Simulation Study . Performing Simulations on Spreadsheets . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Reducing InProcess Inventory, Revisited Case . Action Adventures Previews of Added Cases on Our Website Case . Planning Planers Case . Pricing under Pressure APPENDIXES . Documentation for the OR Courseware . Convexity . Classical Optimization Methods . Matrices and Matrix Operations . Table for a Normal Distribution PARTIAL ANSWERS TO SELECTED PROBLEMS INDEXES Author Index Subject Index ADDITIONAL CASES Case . Cutting Cafeteria Costs Case . Staffing a Call Center Case . Promoting a Breakfast Cereal Case . New Frontiers Case . Assigning Students to Schools Case . Farm Management Case . Assigning Students to Schools, Revisited Case . Writing a Nontechnical Memo Case . Continuation of the Texago Case Study Case . Project Pickings Case . Aiding Allies Case . Steps to Success Case . Assigning Art Case . Stocking Sets Case . Assigning Students to Schools, Revisited Again Case . International Investments Case . Promoting a Breakfast Cereal, Revisited Case . Smart Steering Support Case . Who Wants to be a Millionaire? Case . University Toys and the Engineering Professor Action Figures Case . Queueing Quandary Case . TNT: Tackling Newsboy’s Teachings Case . Jettisoning Surplus Stock Case . Planning Planers Case . Pricing under Pressure SUPPLEMENT TO CHAPTER The LINGO Modeling Language SUPPLEMENT TO CHAPTER More about LINGO SUPPLEMENT TO CHAPTER Linear Goal Programming and Its Solution Procedures Problems Case S. A Cure for Cuba Case S. Airport Security SUPPLEMENT TO CHAPTER A Case Study with Many Transportation Problems SUPPLEMENT TO CHAPTER xviii Using TreePlan Software for Decision TreesSUPPLEMENTS AVAILABLE ON THE TEXT WEBSITE xix SUPPLEMENT TO CHAPTER Derivation of the Optimal Policy for the Stochastic SinglePeriod Model for Perishable Products Problems SUPPLEMENT TO CHAPTER Stochastic PeriodicReview Models Problems SUPPLEMENT TO CHAPTER A Policy Improvement Algorithm for Finding Optimal Policies Problems SUPPLEMENT TO CHAPTER A Discounted Cost Criterion Problems SUPPLEMENT TO CHAPTER VarianceReducing Techniques Problems SUPPLEMENT TO CHAPTER Regenerative Method of Statistical Analysis Problems CHAPTER The Art of Modeling with Spreadsheets . A Case Study: The Everglade Golden Years Company Cash Flow Problem . Overview of the Process of Modeling with Spreadsheets . Some Guidelines for Building “Good” Spreadsheet Models . Debugging a Spreadsheet Model . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Prudent Provisions for Pensions CHAPTER Project Management with PERT/CPM . A Prototype Example—The Reliable Construction Co. Project . Using a Network to Visually Display a Project . Scheduling a Project with PERT/CPM . Dealing with Uncertain Activity Durations . Considering TimeCost TradeOffs . Scheduling and Controlling Project Costs . An Evaluation of PERT/CPM . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . “School’s out forever . . .”xx SUPPLEMENTS AVAILABLE ON THE TEXT WEBSITE CHAPTER Additional Special Types of Linear Programming Problems . The Transshipment Problem . Multidivisional Problems . The Decomposition Principle for Multidivisional Problems . Multitime Period Problems . Multidivisional Multitime Period Problems . Conclusions Selected References Problems CHAPTER Probability Theory . Sample Space . Random Variables . Probability and Probability Distributions . Conditional Probability and Independent Events . Discrete Probability Distributions . Continuous Probability Distributions . Expectation . Moments . Bivariate Probability Distribution . Marginal and Conditional Probability Distributions . Expectations for Bivariate Distributions . Independent Random Variables and Random Samples . Law of Large Numbers . Central Limit Theorem . Functions of Random Variables Selected References Problems CHAPTER Reliability . Structure Function of a System . System Reliability . Calculation of Exact System Reliability . Bounds on System Reliability . Bounds on Reliability Based upon Failure Times . Conclusions Selected References Problems CHAPTER The Application of Queueing Theory . Examples . Decision Making . Formulation of WaitingCost Functions . Decision Models . The Evaluation of Travel Time . Conclusions Selected ReferencesSUPPLEMENTS AVAILABLE ON THE TEXT WEBSITE xxi Learning Aids for This Chapter on Our Website Problems CHAPTER Forecasting . Some Applications of Forecasting . Judgmental Forecasting Methods . Time Series . Forecasting Methods for a ConstantLevel Model . Incorporating Seasonal Effects into Forecasting Methods . An Exponential Smoothing Method for a Linear Trend Model . Forecasting Errors . BoxJenkins Method . Causal Forecasting with Linear Regression . Forecasting in Practice . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems Case . Finagling the Forecasts CHAPTER Examples of Performing Simulations on Spreadsheets with Analytic Solver Platform . Bidding for a Construction Project . Project Management . Cash Flow Management . Financial Risk Analysis . Revenue Management in the Travel Industry . Choosing the Right Distribution . Decision Making with Parameter Analysis Reports and Trend Charts . Conclusions Selected References Learning Aids for This Chapter on Our Website Problems CHAPTER Markov Chains . Stochastic Processes . Markov Chains . ChapmanKolmogorov Equations . Classification of States of a Markov Chain . LongRun Properties of Markov Chains . First Passage Times . Absorbing States . Continuous Time Markov Chains Selected References Learning Aids for This Chapter on Our Website Problems APPENDIX Simultaneous Linear Equations X SUBJECT INDEX Sears, Roebuck and Company, StatoilHydro, Swift & Company, Taco Bell Corporation, Time Inc., United Airlines, Waste Management, Inc., Welch's Inc., Westinghouse Science and Technology Center, approximation methods quadratic, , Russell, , Vogel, – arcs basic, – directed, explanation of, nonbasic, reverse, undirected, – artificial problem construction, artificial variable, artificialvariable technique equality constraints and, – explanation of, functional constraints in ≥ form and, – ASPE Solver. See Analytic Solver Platform for Education (ASPE) assignees, assignment problem constraints and, example of, – explanation of, , Hungarian algorithm for, – minimum cost flow problem and, – model of, – prototype example of, – solution procedures for, – assumption, cost, assumptions additivity, – certainty, , divisibility, linear programming, – requirements, AT&T Bell Laboratories, augmented form, , – augmented solution, augmenting path explanation of, method to find, – augmenting path algorithm explanation of, for maximum flow problem, – Seervada Park maximum flow problem and, – Auto Assembly (case), – auxiliary binary variables, , , – B backlogging, backward induction procedure, balance equation, – Bank Hapoalim Group, Bank One Corporation, barrier algorithms, basic arcs, – basic feasible (BF) solutions adjacent, explanation of, – , – feasible spanning trees and, – initial, matrix form and, – network simplex method and, – optimality test for, in simplex method, – , – , – transportation problem and, – basic solutions explanation of, , , superoptimal, basic tabu search algorithm, – basic variables, Bayes' decision rule explanation of, – sensitivity analysis with, – Bayes' theorem, Better Products Company problem, – bicycle example, – big data, Big M method application of, – explanation of, binary integer programming (BIP). See also integer programming (IP) applications of, – , branchandbound technique for, – branchandcut approach for, – example of, – explanation of, software options for, binary variables auxiliary, , , – SUBJECT INDEX binary representation of general integer variables and, – eitheror constraints and, – explanation of, , fixedcharge problem and, – formulation techniques with, – functions with N possible values and, – K out of N constraints and, – binding constraints, bi parameters, – birthanddeath process analysis of, – assumptions of, – explanation of, queueing models based on, – results for, – bisection method, – bounding, – , Brainy Business (case), – branchandbound algorithm, – branchandbound technique bounding and, – branching and, – explanation of, – fathoming and, – options available for, – branchandcut technique automatic problem processing and, – background of, – generating cutting planes and, – branches, branching, – , , , branching tree, , , , , – branching variable, Brushing Up on Inventory Control (case), – business analytics. See analytics C California Manufacturing Company, – , calling population, , – Canadian Pacific Railway (CPR), Capacity Concerns (case), – capacitycontrolled discount fares model, – Cases Auto Assembly, – Brainy Business, – Brushing Up on Inventory Control, – Capacity Concerns, – Controlling Air Pollution, Fabrics and Fall Fashions, – Money in Motion, – Reducing InProcess, – Savvy Stock Selection, – Shipping Wood to Market, cells changing, – data, – donor, objective, output, recipient, certainty assumption, , Certified Analytics Professional, chance constraints explanation of, , form of, – hard constraints and, – stochastic programming and, changing cells, – chisquare distribution, cj parameters, systemic changes in, – coinflipping game, – CoinMP, column reduction, column vector, combinatorial optimization problems, commercial service systems, complementarity constraint, , complementarity problem, – complementary basic solutions explanation of, , relationships between, – complementary basic solutions property, complementary optimal basic solutions property, , – complementary optimal solutions property, complementary optimal solutions y*, complementary slackness property explanation of, , use of, version of, complementary solutions property, , computer implementation, of simplex method, – computerized inventory systems, computers, operations research field and, concave function, convex set and, explanation of, of several variables, – of single variable, – concave set, connected networks, ConocoPhillips, CONOPT, , constrained optimization with equality constraints, – KKT conditions for, – linearly, constraint boundary, , constraint boundary equations explanation of, – indicating variables for, – constraint programming alldifferent constraints and, – background of, element constraints and, – nature of, – potential of, – research in, – constraints binding, chance, , – complementarity, , dual, – eitheror, – , equality, , – , explanation of, functional, , – , , global, hard, , – inequality, introduction of new, – known, K out of N, – in linear programming model, nonnegativity, , , nonpositivity, redundant, soft, , upperbound, , Continental Airlines, , contingent decisions, continuous simulation, Controlling Air Pollution (case), convex combination, convex function convex set and, explanation of, , , of several variables, – of single variable, – Convexity convex or concave functions of several variables, – convex or concave functions of single variable and, – convexity test, – , convex programming algorithms for, – explanation of, FrankWolfe algorithm for, – software options for, – SUMT and, – convex sets, cooperative game, cornerpoint feasible (CPF) solutions adjacent, , , – augmented, – explanation of, – integer programming and, optimality test and, , optimal solutions and, – properties of, – simplex method and, , – , , , , , – cornerpoint solution, , cost assumption, costbenefit  tradeoff problems, , , cost of ordering, cost tables, equivalent, – County Hospital problem, , – , – . See also queueing models CPF solutions. See cornerpoint feasible (CPF) solutions CPLEX explanation of, for integer programming, CPM (critical path method) explanation of, use of, , crashing, crashing activities, – crashing decisions for activities, – linear programming and, – crew scheduling problem, – CrewSolver, critical path explanation of, in timecost tradeoffs, – critical path method (CPM). See CPM (critical path method) cutting planes, for interger programming problems, – cut value, SUBJECT INDEX SUBJECT INDEX cycle length, cycles explanation of, – undirected, D database requirements, data cells, – data collection, data mining, decision analysis decision making with experimentation and, – decision making without experimentation and, – decision trees and, – game theory vs., overview of, – practical application of, – prototype example of, sensitivity analysis and, – utility theory and, – decision conferencing, decision making with experimentation posterior probabilities and, – prototype example of, value of experimentation and, – decision making without experimentation Bayes' decision rule and, – formulation of prototype example of, maximum likelihood criterion and, – maximum payoff criterion and, – nature of, – sensitivity analysis and, – decision nodes, , decisionsupport system, decision trees construction of, – explanation of, illustration of, performing sensitivity analysis on, – problem analysis using, – decision variables duality and, examples of, explanation of, , in large linear programming problem, as parameter cell, – decreasing marginal utility for money, Deere & Company, defining equations, definite integral, degeneracy, D/Ek//s, demand, demand node, , , dependent demand, dependentdemand products, derivative, of definite integral, descendants, Descriptive analytics, determining reject allowances problem, – deterministic continuousreview models demand for products and, – EOQ model with planned shortages and, – EOQ model with quantity discounts and, – Excel and, explanation of, – illustration of, – justintime inventory management and, – observations about EOQ models and, – deterministic dynamic programming distribution of effort problem and, – example of, – explanation of, structure of, deterministic inventory model, deterministic multiechelon inventory models for supply chain management assumptions for serial multiechelon model and, – extensions of, – model for serial multiechelon system and, – , – overview of, – relaxation and, – revised problem solution and, – rounding procedure for n* and, – serial twoechelon model, – deterministic periodicreview models algorithm for, – example of, – explanation of, Deutsche Post DHL, directed arcs, directed networks, directed path, – discount factor, discount rate, discreteevent simulation, distributing scientists to research teams problem, – distribution of effort problem, – distribution systems, , Distribution Unlimited Co. problem, – , , – diversification, divisibility, as linear programming assumption, dual explanation of, , SOB method to determine form of constraints in, – dual feasible solution, , – duality properties, duality theorem, duality theory adapting to other primal forms and, – applications of, – complementary basic solutions and, – dual problem and, – economic interpretations and, – explanation of, – nonlinear programming and, primaldual relationships and, – , – sensitivity analysis and, , – simplex method and, – dual problem applications of, – construction of, , economic interpretation of, – explanation of, in linear programming, in minimization form, , origin of, – for other primal forms, – relationship between primal problem and, – summary of relationship between primal problem and, – dual simplex method example of, – explanation of, , – summary of, dummy demand node, dummy destination, , – dummy sink, dummy source, , – , dynamic programming deterministic, – explanation of, probabilistic, – prototype example of, – dynamic programming problems, – E echelon, echelon stock, , economic order quantity model. See EOQ models efficient frontier, eitheror constraints, – , Ek/D/s, Ek/M/s, elementary row operations, element constraints, – Em/Ek/s, EOQ formula, , EOQ models basic, – Excel templates for, explanation of, – observations about, – with planned shortages, – with quantity discounts, – equality constraints, , – , , – equivalence property, equivalent cost tables, – equivalent lottery method, – Erlang distribution, , – , event node, Evolutionary Solver, Excel (Microsoft). See also Solver (Excel) EOQ model and, maximum flow problem and, minimum cost flow problem and, – OR applications for, sensitivity analysis and, – shortestpath problem and, – for transportation problems, – expected value of experimentation, – expected value of perfect information (EVPI), – exponential distribution explanation of, properties of, – in queueing systems, – , , random observation generation and, – exponential growth, exponential service times, exponential time algorithms, F Fabrics and Fall Fashions (case), – fair game, fathoming, , – , – fathoming tests, – , , feasibility test, feasible region boundary of, explanation of, , SUBJECT INDEX SUBJECT INDEX feasible solutions, , feasible solutions property, , feasible spanning trees, – Federal Aviation Administration (FAA), financial engineering, financial risk analysis, finite queue variation, – fixedcharge problem, – fixedtime incrementing, – fractional programming, – FrankWolfe algorithm, – Franz Edelman Awards for Achievement in Operations Research and the Management Science, Frontline Systems, , functional constraints duality and, explanation of, in ≥ form, – slack variables and, G game theory decision analysis vs., extensions and, – for games with mixed strategies, – graphical solution procedure for, – linear programming to solve, – overview of, solving simple games with, – twoperson, zerosum games and, – gamma distribution, n Gassco, Gaussian elimination, – , , General Motors Corporation, genetic algorithms basic, – basic concepts of, – explanation of, generating a child procedure and, – integer version of nonlinear programming and, – traveling salesman problem and, – geometric programming, GI/MI/s model, global maximum, – global minimum, , global optimization, – Goferbroke Co. problem, – , – . See also decision analysis Good Products Company example, – gradient algorithms, , gradient search procedure, – , Graphical Method and Sensitivity Analysis, , , graphical procedures game theory and, – linear programming and, – nonlinear programming and, – GRG Nonlinear, GUROBI, H hard constraints, , , – health care applications, – heuristic algorithms, , heuristic procedures, HewlettPackard (HP), , hillclimbing procedure, holding cost, Hungarian algorithm additional zero elements and, – background of, equivalent cost tables and, – summary of, hyperexponential distribution, – hyperplanes, , I IBM, identity matrix, – incumbent, independent demand, Indeval, indicating variables, – inequality constraints, infeasible solution, infinite game, infinite queues, – influence diagram, input cells, installation stock, , Institute for Operations Research and the Management Sciences (INFORMS), , integer programming (IP) applications of, – , – binary, – , – binary variables in model formulation and, – branchandbound algorithm and, – branchandbound technique and, – branchandcut approach and, – explanation of, incorporation of constraint programming and, – LP relaxation and, – , – , mixed, , , – problemsolving perspectives on, – prototype example of, – software for, integer solutions property, , , Intel Corporation, intensification, interarrival time, , , , InterContinental Hotels Group (IHG), Interfaces, interiorpoint algorithm in augmented form, , centering scheme for implementing concept in, example of, overview of, – projected gradient to implement concepts and and, – relevance of gradient for concepts and and, – summary of, – interiorpoint approach background of, – key solution concept and, , postoptimality analysis and, simplex method vs., – to solve linear programming problems, – interior points, internal service systems, International Federation of Operational Research Societies (IFORS), interrelated activity scheduling, inventory explanation of, replenishment of, – scientific management of, – inventory models components of, – deterministic continuousreview, – deterministic multiechelon, – deterministic periodicreview, – stochastic continuousreview, – inventory policy examples of, – in stochastic continuousreview model, – in stochastic singleperiod model, – strategies to improve, – inventory systems computerized, management of, multiechelon, – serial multiechelon, inverse transformation method, – investment analysis, – IOR Tutorial, – IP programming. See integer programming (IP) iteration, , , – , – , , – iterative algorithms, , , J Jackson networks, – Job Shop Company problem, – justintime (JIT) inventory management, , – K KarushKuhnTucker conditions. See KKT conditions KeyCorp, KKT conditions application of, for constrained optimization, – explanation of, for quadratic programming, – known constant, known constraints, K out of N constraints, – L Lagrange multipliers, , , , Lagrangian function, large linear programming models. See also linear programming models computer implementation of simplex method and, example of, – explanation of, – interiorpoint algorithms and, LINGO modeling language and, – modeling languages for, – lead time, learningcurve effect, LGO, , LINDO explanation of, , for integer programming, for large linear programming models, – , for linear programming, – use of, – LINDO API, , LINDO Systems, Inc., linear complementarity problem, , linear fractional programming, linear functions, piecewise, – linearly constrained optimization, SUBJECT INDEX SUBJECT INDEX linear programming additivity and, – allowable range and, applications of, – assumptions of, – certainty and, crashing decisions and, – divisibility and, dual simplex method and, – examples of, – , – game theory and, – goal of, – interiorpoint algorithm and, – optimal policies and, – overview of, – parametric, – postoptimality analysis and, – proportionality and, – software for, – terminology for, – under uncertainty, – (See also uncertainty) upper bound technique and, – linear programming models basic information about, – Excel Solver to solve, – explanation of, – forms of, – method to formulate large, – parameters and, spreadsheet use for, – standard form of, symbols use in, – terminology for solutions of, – linear programming problems dual problem in, formulation of, – , , – network optimization models as, simplex method to solve, , – (See also simplex method) LINGO example using, – explanation of, for integer programming, for linear programming, – for nonlinear programming, stochastic programming and, use of, – links, Little's formula, , L.L. Bean, Inc., local improvement procedure, , local maximum, local minimum, local optima Excel Solver to find, – nonlinear programming problems with multiple, – systematic approach to finding, – local search procedure, longrun profit maximization, LP relaxation, – , , – , – , M management information systems, , manufacturing systems, – marginal cost analysis, – Markov chains explanation of, – steadystate probabilities and, Markov decision process explanation of, linear programming and, – model for, – prototype example of, – , – Markovian property, , Massachusetts Institute of Technology (MIT), material requirements planning (MRP), – mathematical models advantages of, deriving solutions from, – explanation of, formulation of, – linear programming, – pitfalls of, retrospective test of, validation of, matrices explanation of, properties of, – transition, , types of, – vectors and, – matrix form dual problem and primal problem in, , notation in, sensitivity analysis and, simplex method and property revealed by, – simplex method in, , – matrix multiplication, maxflow mincut theorem, – maximization form, primal problem in, , – maximum flow problem algorithm for, – applications of, – augmenting path algorithm for, – Excel to formulate and solve, explanation of, finding augmenting path and, – minimum cost flow problem and, – Seervada Park problem and, – maximum likelihood criterion, – maximum payoff criterion, M/D/s model, M/Ek/s model, – Memorial SloanKettering Cancer Center (MSKCC), Merrill Lynch, , metaheuristics development of, examples of, – explanation of, genetic algorithms and, – nature of, – simulated annealing and, – subtour reversal algorithm and, – tabu search and, – traveling salesman problem and, – M/G/ model, , – , midpoint rule, Midwest Independent Transmission System Operator, Inc. (MISO), military simulation applications, minimax criterion, , minimax theorem, , minimization, simplex method and, – minimization form, dual problem in, , minimum cost flow problem applications of, – example of, – Excel to formulate and solve, – explanation of, – , formulation of, – special cases of, – minimum cover, minimum ratio test, , minimum spanning tree problem algorithm for, applications of, – explanation of, , – Seervada Park problem and, – tabu search and, – mixed congruential method, – mixed integer programming (MIP). See also integer programming (IP) applications of, , , branchandbound algorithm for, – explanation of, mixed strategies, games with, – , M/M/ queueing system, , M/M/s/K model, – M/M/s model application of, – , birthanddeath process and, – explanation of, , – finite calling population variation of, – finite queue variation of, – multipleserver case and, – singleserver case and, – model validation, modified simplex method, – Moneyball (Lewis), – Money in Motion (case), – move selection rule, , MPL (Mathematical Programming Language) for convex programming, , example using, – explanation of, , , for integer programming, for large linear programming models, , multiple optimal solutions, , – multivariable unconstrained optimization explanation of, , gradient search procedure and, – Newton’s method and, – mutiplicative congruential method, mutually exclusive alternatives, , , N negative righthand sides, net flow, , Netherlands Railways, net present value, , network design, minimum spanning tree problem and, network optimization models maximum flow problem and, – minimum cost flow problem and, – minimum spanning tree problem and, – network simplex method and, – to optimize project timecost tradeoff, – overview of, – prototype example of, – shortestpath problem and, – networks components of, connected, SUBJECT INDEX nonlinear programming complementarity, – convex programming, , – explanation of, fractional, – geometric, graphical illustration of, – KKT conditions for constrained optimization and, – linearly constrained optimization and, with multiple local optima, – multivariable unconstrained optimization and, – nonconvex programming, , – onevariable unconstrained optimization and, – portfolio selection with risky securities problem, – productmix with price elasticity problem, – quadratic programming and, – , – sample applications of, – separable programming, – , – simulated annealing and, – transportation problem with volume discounts on shipping costs, , unconstrained optimization, – nonnegativity constraints, , , nonpositivity constraints, nonpreemptive priorities, nonpreemptive priorities model, – nonzerosum game, Nori & Leets Co. problem, – normal distribution, , – normal distribution table, – nperson game, null matrix, null vector, O objective cells, objective function deterministic dynamic programming and, explanation of, , , in large linear programming problem, – OR model formulation and, simplex method and, slopeintercept form of, objective function coefficients allowable range for, – percent rule for simultaneous changes in, – , – simultaneous changes in, – objectives, in problem definition, SUBJECT INDEX directed, explanation of, flows in, project, – queueing, – residual, terminology of, – timecost tradeoff optimization and, – undirected, , network simplex method BF solutions and feasible spanning trees and, – completing process in, – explanation of, , leaving basic variable and, – minimum cost flow problem and, selecting and entering basic variables and, – upperbound technique and, – newsvendor problem, Newton's method explanation of, of multivariable unconstrained optimization, – onevariable unconstrained optimization and, – quasi, nextevent incrementing, – no backlogging, nodes in decision trees, demand, , , dummy demand, explanation of, , supply, transshipment, , nonbasic arcs, nonbasic variables, , , – , nonconvex programming challenges related to, – Evolutionary Solver and, Excel Solver to find local optima and, – explanation of, , multiple local optima and, – systematic approach to finding local optima and, – noncooperative game, nonexponential distributions involving queueing models hyperexponential distribution and, – M/D/s, M/Ek/s, – M/G/ , – phasetype distribution and, – without Poisson input, – percent rule for simultaneous changes in objective function coefficients, – , – for simultaneous changes in righthand sides, onevariable unconstrained optimization bisection method and, – explanation of, – Newton's method and, – Operation Desert Storm, operations research modeling approach conclusions related to, defining the problem and gathering data in, – deriving solutions from, – implementation of, – mathematical model formulation in, – model application in, – model testing in, – operations research (OR) analytics and, – applications of, described in vignettes, – impact of, nature of, – origins of, – team in, , OPLCPLEX Development System, optimality principle, optimality test for basic feasible solution, , for cornerpoint feasible solution, , sensitivity analysis and, simplex method and, , – optimal policies, in Markov decision process, – optimal solutions CPF solutions and, – example of, explanation of, , iteration and, – multiple, – search for, optimization classical methods of, – combinatorial, constrained, , – , – global, – robust, – with simulation and ASPE's Solver, – unconstrained, – , – , – Optimization Programming Language (OPL), – optimizing, satisficing vs., OR. See operations research (OR) OR Courseware Analytic Solver Platform for Education, Excel files, explanation of, IOR Tutorial, – LINGO/LINDO files, MPL/Solvers, OR Tutor, updates, use of, – order quantity Q, OT Tutor, output cells, , overall measure of performance, overbooking model, – P Pacific Lumber Company (PALCO), P & T Company problem, – . See also transportation problem parameter analysis report twoway, – use of, – , parameter cell, – parameters explanation of, of linear programming model, parameter table, , , , parametric linear programming explanation of, – , for systemic changes in bi parameters, – for systemic changes in cj parameters, – path augmenting, critical, – directed, – undirected, – payoff, payoff table, – , , , performance, overall measure of, perishable products, – . See also stochastic single period model for perishable products PERT, , PERT/CPM, phasetype distributions, – piecewise linear functions, – pivot column, pivot number, pivot row, planned shortages, EOQ model with, – Poisson distribution, SUBJECT INDEX SUBJECT INDEX Poisson input explanation of, , models without, – Poisson input process, , , Poisson process, – policy decision, political campaign problem, – PollaczekKhintchine formula, , polynomials, polynomial time algorithms, – portfolio selection, with risky security, – positive semidefinite matrix, posterior probabilities, – , postoptimality analysis combining simplex method with interiorpoint approach for, Excel and, – explanation of, , , parametric linear programming and, – reoptimization and, sensitivity analysis and, – shadow prices and, – use of, predictive analytics, preemptive priorities, , – preemptive priorities model, prescriptive analytics, pricedemand curve, price elasticity, productmix problem with, – primaldual relationships. See also duality theory; dual problem; primal problem complementary basic solutions and, – explanation of, relationships between complementary basic solutions and, – primaldual table, primal feasible solution, , primal problem applications of, – economic interpretation of, explanation of, in maximization for, – in maximization form, , – relationship between dual problem and, – summary of relationship between dual problem and, – principle of optimality, prior distribution, – prioritydiscipline queueing models example of, – explanation of, nonpreemptive priorities model and, – preemptive priorities model and, singleserver variation of, – types of, – prior probabilities, , probabilistic dynamic programming examples of, – explanation of, – probability distribution explanation of, – generation of random observations from, – probability tree, problem definition, Procter & Gamble (P&G), product demand, – production and distribution network design, productmix problem explanation of, , with price elasticity, – products perishable, – stable, profit function, , profit maximization, longrun, profits, goal of satisfactory, project deadlines, – project networks, – proportionality auxiliary binary variables and, – explanation of, as linear programming assumption, – pseudorandom numbers, pure strategies, , Q quadratic approximation, , quadratic programming explanation of, – , – KKT conditions for, – modified simplex method and, – software options for, – quantity discounts, with EOQ model, – quasiNewton methods, queue, , queue discipline, , queueing models basic structure of, – birthanddeath process and, – M/M/s, – nonexponential distributions and, – priority discipline, – queueing networks explanation of, – infinite queues in series and, – Jackson networks and, – Queueing Simulator, – queueing systems classes of, – design and operation of, – , explanation of, exponential distribution and, – queueing theory applications of, , – background of, explanation of, prototype example of, terminology and notation for, – R R, Q policy (reorderpoint, orderquantity policy), radiation therapy, twophase method and, – radiation therapy example illustration of, – primaldual form and, simplex method and, – RAND() function (Excel), , random digits table, random integer numbers converted to uniform random numbers, explanation of, generation of, probability distributions and, randomized policy, – random number generation computers for, congruential methods for, – simulation and, random number generators, random numbers categories of, characteristics of, – explanation of, move selection rule and, uniform, , , random observations from probability distribution explanation of, generation of, – range names, , range of uncertainty, rate in = rate out principle, – recursive relationship, , Reducing InProcess (case), – regional planning problem, – relaxation explanation of, inventory and, , – LP, – , , – , – Reliable Construction Co. problem, – . See also timecost tradeoffs reoptimization in postoptimality analysis, sensitivity analysis and, reorder point, , – replicability, reproducibility, residual capacities, , residual network, , resourceallocation problems, , results cell, retrospective test, revenue, revenue management in airline industry, – background of, – capacitycontrolled discount fares and, – considerations for models used in, – explanation of, overbooking model and, – reverse arc, revised simplex method applications of, explanation of, – Rijkswaterstaat (Netherlands) study, , – riskaverse, riskneutral, risk seekers, robust optimization explanation of, – extension of, with independent parameters, – recourse and, stochastic programming and, row reduction, row vector, Russell's approximation method, , S saddle point, – salvage value, , Samsung Electronics Corp., Sasol, SUBJECT INDEX SUBJECT INDEX satisficing, SaveIt Company problem, – Savvy Stock Selection (case), – scheduling employment levels problem, – scientific inventory management, Sears, Roebuck and Company, Seervada Park problem algorithm for shortestpath problem and, – maximum flow problem and, – minimum spanning tree problem and, – overview of, – sensibleoddbizarre method (SOB), – sensitive parameters explanation of, , sensitivity analysis to identify, sensitivity analysis application of, , – with Bayes' decision rule, – changes in bi and, – changes in coefficients of basic variable and, – changes in coefficients of nonbasic variable and, – duality theory and, , – example of, – explanation of, , , introduction of new constraint and, – introduction of new variable and, in postoptimality analysis, , , – procedure for, – , – purpose of, sensitivity report to perform, – on spreadsheets, – , – types of, sensitivity reports, – separable programming explanation of, – , – extensions of, – key property of, – reformulation as linear programming problem and, – sequences of numbers, sequentialapproximation algorithms, – sequential linear approximation algorithm (FrankWolfe), – sequential unconstrained algorithms, sequential unconstrained minimization technique. See SUMT serial multiechelon system assumptions for, – model for, – serial twoechelon model, – servers, service industry simulation applications, service level, , service time, – , , , set covering problems, set partitioning problems, shadow price duality theory and, , explanation of, – sensitivity analysis and, shipment dispatch, – shipping costs, , Shipping Wood to Market (case), shortage cost, shortestpath problem algorithm for, applications for, – Excel to formulate and solve, – minimum cost flow problem and, overview of, Seervada Park, – simple discrete distributions, simplex method. See also dual simplex method; network simplex method algebra of, – basic feasible solutions in, – , – , – computer implementation of, – CPF solutions and, , – , , , , , – direction of movement and, – duality and, – , equality constraints and, – examples in, – , – explanation of, , , – extensions to augmented form of problem and, – functional constraints in ≥ form and, – geometric concepts in, – interiorpoint approach and, – key solution concepts in, – in matrix form, , – maximum flow problem and, method to set up, – minimization in, – modified, – negative righthand sides and, no feasible solutions and, – optimality test and, , – postoptimality analysis and, – property revealed by matrix form of, – revised, – summary of, – in tabular form, – terminology for, – tie breaking in, – for transportation problem, – twophase method in, – use of, with variables allowed to be negative, – simplex tableau, , , , – , simulated annealing basic concepts of, – basic simulated annealing algorithm and, – nonlinear programming and, – traveling salesman problem and, – simulated annealing algorithm, – simulation continuous, discreteevent, examples of, – explanation of, – fixedtime incrementing and, – nextevent incrementing and, – optimization with, – in OR studies, – random number generation and, – random observation generation from probability distribution and, – software for, – , – spreadsheets for, – steps in OR research studies based on applying, – simulation applications distribution system design and operation, financial risk analysis, health care, – innovative new, inventory system management, manufacturing systems design and operation, – military, project completion deadline, – queuing systems design and operation, service industry, simulation models checking accuracy of, explanation of, formulation of, – planning simulations for, – preparing recommendations based on, simulation run for, – software for, – testing validity of, sink, site selection, – slack variables, , , , slopeintercept form, of objective function, SOB (sensibleoddbizarre method), – social service systems, soft constraints, , software linear programming, – nonlinear programming, – , – operations research background and development of, for simulation, – , – for solving BIP models, solid waste reclamation problem, – solutions. See also basic feasible (BF) solutions; optimal solutions cornerpoint feasible, feasible, infeasible, optimal, , , suboptimal, Solver (Excel). See also Analytic Solver Platform for Education (ASPE) application of, description of, – to find local optima, – for integer programming, for linear programming, sensitivity analysis and, source, Southern Confederation of Kibbutzim problem, – Southwestern Airways example, – spanning trees explanation of, – , feasible, , minimum, – spreadsheets ASPE's Solver and, – formulating linear programming models on, – sensitivity analysis on, – , – software for, Solver use and, – stable products, – stable solution, stagecoach problem, – stages, in dynamic programming problems, standard form, for linear programming model, state of nature, states, in dynamic programming problems, stationary, deterministic policy, statistic cells, SUBJECT INDEX SUBJECT INDEX StatoilHydro, steadystate condition, , , steepest ascent/mildest descent approach, stochastic continuousreview model assumptions of, example of, explanation of, – order quantity Q and, reorder point R and, – stochastic inventory model, stochastic process, stochastic programming with recourse applications of, – example of, – explanation of, – stochastic single period model for perishable products analysis of, – application of, – , – assumptions of, – example of, – explanation of, – optimal policy and, – types of perishable products and, – stock portfolios, – strong duality property, structural constraints. See functional constraints submatrices, suboptimal solutions, subtour reversal, – subtour reversal algorithm, – SULUM, SUMT example of, – explanation of, , – summary of, superoptimal basic solution, Supersuds Corporation example, – supply chain, supply chain management. See deterministic multiechelon inventory models for supply chain management supply node, surplus variable, – Swift & Company, symbols, use in linear programming models, – symmetry property, system service rate, – T table lookup approach, tabular form, simplex method in, – tabu list, tabu search basic tabu search algorithm and, – explanation of, minimum spanning tree problem with constraints and, – traveling salesman problem and, – Taco Bell Corporation, tasks, , teams, , technological coefficients, time advance methods, timecost tradeoffs crashing decisions and, – critical path and, – for individual activities, – network model and, project networks and, – prototype example of, – Time Inc., transient condition, , transition matrix, , transition probabilities, transportation problem basic feasible (BF) solutions and, – with dummy destination, – with dummy source, – Excel to formulate and solve, – explanation of, generalizations of, minimum cost flow problem and, model of, – prototype example of, – streamlined simplex method for, – with volume discounts on shipping costs, , transportation service systems, transportation simplex method application of, – drawback of, explanation of, features of example of, – initialization of, – iteration for, – optimality test for, – set up for, – summary of, transportation simplex tableau, , – transpose operation, transshipment node, , transshipment problem, minimum cost flow problem and, traveling salesman problem example of, – genetic algorithms and, – simulated annealing and, – tabu search and, – trend charts, twobin system, twoperson constantsum game, zerosum games explanation of, – formulation of, – twophase method explanation of, – use of, – U unbounded Z, , uncertainty chance constraints and, – overview of, – robust optimization and, – sensitivity analysis and, – sensitivity analysis application and, – sensitivity analysis on spreadsheets and, – stochastic programming with recourse and, – unconstrained optimization explanation of, – multivariable, – , onevariable, – , – undirected arcs, – undirected networks, , undirected path, – uniform random numbers, , , Union Airways problem, – United Airlines, unstable solution, upper bound technique example of, – explanation of, – network simplex method and, – utility function (U/M) for money M, – utility theory application of, – equivalent lottery method and, – estimating U/M and, – overview of, – utility functions for money and, – utilization factor, – , V value of game, variables artificial, binary, , , – with bound on negative values allowed, decision, , , , , indicating, negative, – in network simplex method, – with no bound on negative values allowed, – nonbasic, , , – , slack, , , , surplus, – variancereducing techniques, vectors of basic variables, explanation of, – Vogel's approximation method, – W waiting cost, warmup period, Waste Management, Inc., Welch's, Inc., Westinghouse Science and Technology Center, whatif analysis, winning in Las Vegas problem, – Winter Simulation Conference, World Health Council problem, – Worldwide Corporation problem, – Wyndor Glass Co. problem additivity assumption and, – approach to, – background of, certainty assumption and, chance constraints and, , complementary basic solutions for, conclusions about, , , constraint boundary equations for, – constraints in, CPF solutions for, , , – divisibility assumption and, dual simplex method and, – formulation of mathematical model for, – graphical solution to, – interiorpoint algorithm and, LINDO and LINGO use and, – SUBJECT INDEX SUBJECT INDEX nonlinear programming and, – , – primal and dual problems for, , proportionality assumption and, – sensitivity analysis and, – , – , – , – simplex method and, – , , – , – , , , – , , spreadsheets for, – , – stochastic programming and, – uncertainty and, , X Xerox Corporation, Y yes/no decisions, , , , Z zero elements, – AUTHOR INDEX A Abbink, E., n AbellanPerpiñan, J. M., Acharya, D., Achterberg, A., Ahmed, S., n, Ahn, S., Ahrens J. H., n Ahuja, R. K., n Akgun, V., Alden, H., n Alden, J. M., , n, Alexopoulos, C., Allan, R., Allen, S. J., n Almroth, M., Altschuler, S., , n Ambs, K., Anderson, E. T., n Anderson, P. L., Andrews, B., Angelis, D. P., n Appa, G. L., Argüello, M., n, Armacost, A. P., Aron, I. D., Asmussen, S., Assad, A. A., Aumann, R. J., Avis, D., n Avriel, M., n, n Axsäter, S., n, Azaiez, M. N., B Bagchi, S., Baker, K. R., Banks, J., Baptiste, P., Barabba, V., Barkman, M., n Barnes, E. R., n Barnhart, C., Barnum, M. P., n Batavia, D., , n Bayes, T., – , n, – , , , , , , Bazarra, M. S., , , Beis, D. A., Benjamin, A. T., n BenKhedher, N., Bennett, J., , n Benson, R. F., BenTal, A., Berk, G. L., n Berkey, B. G., n Bertsimas, D., , , n, , Best, M. J., Best, W. D., Bielza, C., n Bier, V. M., Billington, C., Birge, J. R., Bixby, A., , n Bland, R., n Blatt, J. A., n Bleichrodt, H., Bleuel, W. H., Blyakher, S., n Board, J., Bohm, W., Bollapragada, S., Bookbinder, J. H., Boucherie, R. J., Bowen, D. A., n Boyd, S., Braklow, J. W., Brennan, M., n Brenner, D. A., Brigandi, A. J., , Brinkley, P. A., AUTHOR INDEX Page numbers followed by n indicate footnotes. AUTHOR INDEX Brown, D. B., Brown, G. G., Brown, S. M., Buckley, S., Bunday, B. D., n Burman, M., Burns, L. D., , n, Busch, I. K., n Byers, S., C Cahn, M. F., Cai, X., CaixetaFilho, J. V., Callioni, G., Camm, J. D., n Canbolat, B., Cao, B., n Caramanis, C., Carlson, B., n Carlson, W., n Carr, W. D., Carson, J. S., II, Case, R., n Cavalier, T. M., n Chalermkraivuth, K. C., Chandrasekaran, S., Chao, X., n Chatterjee, K., Chelst, K., Chen, E. J., n Chen, H., , n Cheng, R., Chinneck, J. W., n Chiu, H.W.C., Choi, T.M., Chorman, T. E., n Chu, L. Y., n Cioppa, T. M., Clark, M. C., Clemen, R. T., Clerkx, M., , Coello, C., Cohen, M., Cooke, F., Copeland, D., n Corner, J. L., Cosares, S., Costy, T., , n, Cottle, R. W., n Coveyou, R. R., n Crane, B., Cremmery, R., Cunningham, J., Cwilich, S.,
كلمة سر فك الضغط : booksworld.net The Unzip Password : booksworld.net أتمنى أن تستفيدوا من محتوى الموضوع وأن ينال إعجابكم رابط من موقع عالم الكتب لتنزيل كتاب Introduction to Operations Research رابط مباشر لتنزيل كتاب Introduction to Operations Research

