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 |  موضوع: كتاب Introduction to Operations Research السبت 06 نوفمبر 2021, 10:55 pm | |
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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 Interior-Point 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 Interior-Point 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 Shortest-Path 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 Trade-Off
. 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 Branch-and-Bound Technique and Its Application to Binary
Integer Programming
. A Branch-and-Bound Algorithm for Mixed Integer
Programming
. The Branch-and-Cut 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
. One-Variable Unconstrained Optimization
. Multivariable Unconstrained Optimization
. The Karush-Kuhn-Tucker (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 Two-Person, Zero-Sum 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 Birth-and-Death Process
. Queueing Models Based on the Birth-and-Death Process
. Queueing Models Involving Nonexponential Distributions
. Priority-Discipline Queueing Models
. Queueing Networks
. The Application of Queueing Theory
. Conclusions
Selected References
Learning Aids for This Chapter on Our Website
Problems
Case . Reducing In-Process Inventory
Preview of an Added Case on Our Website
Case . Queueing Quandary
CHAPTER
Inventory Theory
. Examples
. Components of Inventory Models
. Deterministic Continuous-Review Models
. A Deterministic Periodic-Review Model
. Deterministic Multiechelon Inventory Models for Supply
Chain Management
. A Stochastic Continuous-Review Model
. A Stochastic Single-Period 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 In-Process 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 Single-Period Model
for Perishable Products
Problems
SUPPLEMENT TO CHAPTER
Stochastic Periodic-Review 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
Variance-Reducing 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 Time-Cost Trade-Offs
. 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 Waiting-Cost 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 Constant-Level Model
. Incorporating Seasonal Effects into Forecasting Methods
. An Exponential Smoothing Method for a Linear Trend Model
. Forecasting Errors
. Box-Jenkins 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
. Chapman-Kolmogorov Equations
. Classification of States of a Markov Chain
. Long-Run 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,
artificial-variable 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, – ,
branch-and-bound technique for, –
branch-and-cut approach for, –
example of, –
explanation of,
software options for,
binary variables
auxiliary, , , – SUBJECT INDEX
binary representation of general integer variables and,
–
either-or constraints and, –
explanation of, ,
fixed-charge problem and, –
formulation techniques with, –
functions with N possible values and, –
K out of N constraints and, –
binding constraints,
bi parameters, –
birth-and-death process
analysis of, –
assumptions of, –
explanation of,
queueing models based on, –
results for, –
bisection method, –
bounding, – ,
Brainy Business (case), –
branch-and-bound algorithm, –
branch-and-bound technique
bounding and, –
branching and, –
explanation of, –
fathoming and, –
options available for, –
branch-and-cut 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), –
capacity-controlled 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 In-Process, –
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, –
chi-square distribution,
cj parameters, systemic changes in, –
coin-flipping 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
all-different constraints and, –
background of,
element constraints and, –
nature of, –
potential of, –
research in, –
constraints
binding,
chance, , –
complementarity, ,
dual, –
either-or, – ,
equality, , – ,
explanation of,
functional, , – , ,
global,
hard, , –
inequality,
introduction of new, –
known,
K out of N, –
in linear programming model,
nonnegativity, , ,
nonpositivity,
redundant,
soft, ,
upper-bound, ,
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,
Frank-Wolfe algorithm for, –
software options for, –
SUMT and, –
convex sets,
cooperative game,
corner-point feasible (CPF) solutions
adjacent, , , –
augmented, –
explanation of, –
integer programming and,
optimality test and, ,
optimal solutions and, –
properties of, –
simplex method and, , – , , , , ,
–
corner-point solution, ,
cost assumption,
cost-benefit - trade-off problems, , ,
cost of ordering,
cost tables, equivalent, –
County Hospital problem, , – , – . See
also queueing models
CPF solutions. See corner-point 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 time-cost trade-offs, –
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, ,
decision-support 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,
dependent-demand products,
derivative, of definite integral,
descendants,
Descriptive analytics,
determining reject allowances problem, –
deterministic continuous-review models
demand for products and, –
EOQ model with planned shortages and, –
EOQ model with quantity discounts and, –
Excel and,
explanation of, –
illustration of, –
just-in-time 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 two-echelon model, –
deterministic periodic-review models
algorithm for, –
example of, –
explanation of,
Deutsche Post DHL,
directed arcs,
directed networks,
directed path, –
discount factor,
discount rate,
discrete-event 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,
primal-dual 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,
either-or 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, –
shortest-path 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, –
fixed-charge problem, –
fixed-time incrementing, –
fractional programming, –
Frank-Wolfe 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, –
two-person, zero-sum 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,
Hewlett-Packard (HP), ,
hill-climbing 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, –
branch-and-bound algorithm and, –
branch-and-bound technique and, –
branch-and-cut approach and, –
explanation of,
incorporation of constraint programming and, – LP relaxation and, – , – ,
mixed, , , –
problem-solving perspectives on, –
prototype example of, –
software for,
integer solutions property, , ,
Intel Corporation,
intensification,
interarrival time, , , ,
InterContinental Hotels Group (IHG),
Interfaces,
interior-point 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, –
interior-point 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 continuous-review, –
deterministic multiechelon, –
deterministic periodic-review, –
stochastic continuous-review, –
inventory policy
examples of, –
in stochastic continuous-review model, –
in stochastic single-period 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, –
just-in-time (JIT) inventory management, , –
K
Karush-Kuhn-Tucker 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, –
interior-point algorithms and,
LINGO modeling language and, –
modeling languages for, –
lead time,
learning-curve 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, –
interior-point 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,
long-run profit maximization,
LP relaxation, – , , – , – ,
M
management information systems, ,
manufacturing systems, –
marginal cost analysis, –
Markov chains
explanation of, –
steady-state 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,
max-flow min-cut 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 Sloan-Kettering Cancer Center (MSKCC),
Merrill Lynch, ,
metaheuristics
development of,
examples of, –
explanation of,
genetic algorithms and, –
nature of, –
simulated annealing and, –
sub-tour 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, , ,
branch-and-bound algorithm for, –
explanation of,
mixed strategies, games with, – ,
M/M/ queueing system, ,
M/M/s/K model, –
M/M/s model
application of, – ,
birth-and-death process and, –
explanation of, , –
finite calling population variation of, –
finite queue variation of, –
multiple-server case and, –
single-server 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 right-hand 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 time-cost trade-off, –
overview of, –
prototype example of, –
shortest-path 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, , –
one-variable unconstrained optimization and, –
portfolio selection with risky securities problem, –
product-mix 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, –
nonzero-sum game,
Nori & Leets Co. problem, –
normal distribution, , –
normal distribution table, –
n-person 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,
slope-intercept 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, –
time-cost trade-off 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, –
one-variable unconstrained optimization and, –
quasi-,
next-event 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/ , –
phase-type distribution and, –
without Poisson input, – percent rule
for simultaneous changes in objective function
coefficients, – , –
for simultaneous changes in right-hand sides,
one-variable 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, ,
OPL-CPLEX Development System,
optimality principle,
optimality test
for basic feasible solution, ,
for corner-point 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
two-way, –
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,
phase-type 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, –
Pollaczek-Khintchine formula, ,
polynomials,
polynomial time algorithms, –
portfolio selection, with risky security, –
positive semidefinite matrix,
posterior probabilities, – ,
postoptimality analysis
combining simplex method with interior-point 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,
price-demand curve,
price elasticity, product-mix problem with, –
primal-dual relationships. See also duality theory; dual
problem; primal problem
complementary basic solutions and, –
explanation of,
relationships between complementary basic solutions
and, –
primal-dual 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, –
priority-discipline queueing models
example of, –
explanation of,
nonpreemptive priorities model and, –
preemptive priorities model and,
single-server 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,
product-mix problem
explanation of, ,
with price elasticity, –
products
perishable, –
stable,
profit function, ,
profit maximization, long-run,
profits, goal of satisfactory,
project deadlines, –
project networks, –
proportionality
auxiliary binary variables and, –
explanation of,
as linear programming assumption, –
pseudo-random 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, –
quasi-Newton methods,
queue, ,
queue discipline, ,
queueing models
basic structure of, –
birth-and-death 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 (reorder-point, order-quantity policy),
radiation therapy, two-phase method and, –
radiation therapy example
illustration of, –
primal-dual 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 In-Process (case), –
regional planning problem, –
relaxation
explanation of,
inventory and, , –
LP, – , , – , –
Reliable Construction Co. problem, – . See also
time-cost trade-offs
reoptimization
in postoptimality analysis,
sensitivity analysis and,
reorder point, , –
replicability,
reproducibility,
residual capacities, ,
residual network, ,
resource-allocation problems, ,
results cell,
retrospective test,
revenue,
revenue management
in airline industry, –
background of, –
capacity-controlled 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, , –
risk-averse,
risk-neutral,
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,
Save-It Company problem, –
Savvy Stock Selection (case), –
scheduling employment levels problem, –
scientific inventory management,
Sears, Roebuck and Company,
Seervada Park problem
algorithm for shortest-path problem and, –
maximum flow problem and, –
minimum spanning tree problem and, –
overview of, –
sensible-odd-bizarre 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,
sequential-approximation algorithms, –
sequential linear approximation algorithm (Frank-Wolfe),
–
sequential unconstrained algorithms,
sequential unconstrained minimization technique. See
SUMT
serial multiechelon system
assumptions for, –
model for, –
serial two-echelon 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,
shortest-path 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, –
interior-point approach and, –
key solution concepts in, –
in matrix form, , –
maximum flow problem and,
method to set up, –
minimization in, –
modified, –
negative right-hand 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, –
two-phase 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,
discrete-event,
examples of, –
explanation of, –
fixed-time incrementing and, –
next-event 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, , , ,
slope-intercept form, of objective function,
SOB (sensible-odd-bizarre 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
corner-point 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,
steady-state condition, , ,
steepest ascent/mildest descent approach,
stochastic continuous-review 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,
sub-tour reversal, –
sub-tour 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,
time-cost trade-offs
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,
two-bin system,
two-person
constant-sum game,
zero-sum games
explanation of, –
formulation of, –
two-phase 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, – ,
one-variable, – , –
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, –
variance-reducing techniques,
vectors
of basic variables,
explanation of, –
Vogel's approximation method, –
W
waiting cost,
warm-up period,
Waste Management, Inc.,
Welch's, Inc.,
Westinghouse Science and Technology Center,
what-if 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, –
interior-point 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
Abellan-Perpiñan, 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
Ben-Khedher, N.,
Bennett, J., , n
Benson, R. F.,
Ben-Tal, 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.,
Caixeta-Filho, 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.,
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