Operations Research: A Model-Based Approach

✍ Scribed by H. A. Eiselt, Carl-Louis Sandblom

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 527
• Series: Springer Texts In Business And Economics
• Edition: 3
• Category: Library

✦ Synopsis

This is the third edition of a textbook that has been used in a number of undergraduate courses and covers the standard models and techniques used in decision-making in organizations. The main emphasis of the book is on modelling business-related scenarios and the generation of decision alternatives. Fully solved examples from many areas are used to illustrate the main concepts without getting bogged down in technical details. The book presents an approach to operations research that is heavily based on modelling and makes extensive use of sensitivity analyses. It is the result of the authors’ many years of combined teaching experience.
The third edition includes new topics such as nonlinear programming and reliability theory, as well as additional material on multi-attribute decision-making. Each chapter includes a number of fully solved problems that allow students to practice or self-study. Additional problems are available on the book’s accompanying website.

✦ Table of Contents

Preface
Symbols and Definitions
Notation
Contents
1: Introduction to Operations Research
1.1 The Nature and History of Operations Research
1.2 The Main Elements of Operations Research
1.3 The Modeling Process
Reference
2: Linear Programming
2.1 Introduction to Linear Programming
2.2 Applications of Linear Programming
2.2.1 Production Planning
2.2.2 Diet Problems
2.2.3 Allocation Problems
2.2.4 Blending Problems
2.2.5 Transportation and Assignment Problems
2.2.6 Dynamic Production-Inventory Models
2.2.7 Employee Scheduling
2.2.8 Cutting Stock Problems
2.3 Graphical Representation and Solution
2.3.1 The Graphical Solution Method
2.3.2 Special Cases
2.4 Postoptimality Analyses
2.4.1 Graphical Sensitivity Analyses
2.4.2 Optimal Solution and Sensitivity Analyses on a Printout
2.5 Duality
References
3: Multiobjective Programming
3.1 Vector Optimization
3.2 Solution Approaches to Vector Optimization Problems
3.3 Goal Programming
4: Nonlinear Programming
4.1 Introduction
4.2 Applications of Nonlinear Programming
4.2.1 Forestry Logging
4.2.2 Maximizing Tax Revenues
4.2.3 Optimizing Inventory and Queuing Models
4.3 Properties of Nonlinear Programming Problems
4.4 Solution Methods for Nonlinear Optimization
4.4.1 Newton´s Method for Unconstrained Optimization
4.4.2 Constrained Optimization
References
5: Integer Linear Programming
5.1 Definitions and Basic Concepts
5.2 Applications of Integer Programming
5.2.1 Diet Problems Revisited
5.2.2 Land Use
5.2.3 Modeling Fixed Charges
5.2.4 Workload Balancing
5.2.5 Political Districting
5.3 Solution Methods for Integer Programming Problems
5.3.1 Cutting Plane Methods
5.3.2 Branch-and-Bound Methods
5.3.3 Heuristic Methods
6: Network Models
6.1 Definitions and Conventions
6.2 Social Network Analysis
6.3 Network Flow Problems
6.4 Shortest Path Problems
6.5 Spanning Tree Problems
6.6 Routing Problems
6.6.1 Arc Routing Problems
6.6.2 Node Routing Problems
References
7: Location Models
7.1 The Major Elements of Location Problems
7.2 Covering Problems
7.2.1 The Location Set Covering Problem
7.2.2 The Maximal Covering Location Problem
7.3 Center Problems
7.3.1 1-Center Problems
7.3.2 p-Center Problems
7.4 Median Problems
7.4.1 Minisum Problems in the Plane
7.4.2 Minisum Problems in Networks
7.5 Other Location Problems
References
8: Project Networks
8.1 The Critical Path Method
8.2 Project Acceleration
8.3 Project Planning with Resources
8.4 The PERT Method
Reference
9: Machine Scheduling
9.1 Basic Concepts of Machine Scheduling
9.2 Single-Machine Scheduling Models
9.3 Parallel Machine Scheduling Models
9.4 Dedicated Machine Scheduling Models
Reference
10: Decision Analysis
10.1 Introduction to Decision Analysis
10.2 Visualizations of Decision Problems
10.3 Decision Rules Under Uncertainty and Risk
10.4 Sensitivity Analyses
10.5 Decision Trees and the Value of Information
10.6 Utility Theory
11: Multiattribute Decision Making
11.1 The General Model and a Generic Solution Method
11.2 TOPSIS
11.3 The Analytic Hierarchy Process
References
12: Inventory Models
12.1 Basic Concepts in Inventory Planning
12.2 The Economic Order Quantity (EOQ) Model
12.3 The Economic Order Quantity with Positive Lead Time
12.4 The Economic Order Quantity with Backorders
12.5 The Economic Order Quantity with Quantity Discounts
12.6 The Production Lot Size Model
12.7 The Economic Order Quantity with Stochastic Lead Time Demand
12.7.1 A Model that Optimizes the Reorder Point
12.7.2 A Stochastic Model with Simultaneous Computation of Order Quantity and Reorder Point
12.8 Extensions of the Basic Inventory Models
Reference
13: Stochastic Processes and Markov Chains
13.1 Basic Ideas and Concepts
13.2 Steady-State Solutions
13.3 Decision Making with Markov Chains
14: Reliability Models
14.1 Fundamentals of Reliability Systems
14.2 Time Aspects of Reliability
14.3 Failure Rates and the Hazard Function
14.4 Redundancy and Standby Systems
14.5 Estimating Reliability
Reference
15: Waiting Line Models
15.1 Basic Queuing Models
15.2 Optimization in Queuing
16: Simulation
16.1 Introduction to Simulation
16.2 Random Numbers and Their Generation
16.3 Examples of Simulations
16.3.1 Simulation of a Waiting Line System
16.3.2 Simulation of an Inventory System
References
17: Heuristic Methods
Reference
Appendices
Appendix A: Vectors and Matrices
Appendix B: Systems of Simultaneous Linear Equations
Appendix C: Probability and Statistics
References
Index

✦ Subjects

Operations Research And Decision Theory; Operations Research; Management Science; Industrial Management