Decision Optimization with IBM ILOG CPLEX Optimization Studio: A Hands-On Introduction to Modeling with the Optimization Programming Language (OPL) (Graduate Texts in Operations Research)

✍ Scribed by Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 279
• Category: Library

✦ Synopsis

This textbook offers a comprehensive, up-to-date introduction to the Optimization Programming Language (OPL). Embedded in the IBM ILOG CPLEX Optimization Studio with its solver engine CPLEX, OPL has been popular for years not only for academic and scientific purposes, but also among practitioners who need to model and solve large-scale real-world business optimization problems. The book covers the recent features of the software and includes ten consecutive tutorials, each with additional exercises, as well as several comprehensive application studies.

The book is specifically designed for advanced undergraduate and graduate courses in e.g. management science, operations research, computer science, mathematics, mathematical economics, and industrial engineering. It can also serve as self-study material for practitioners whose work involves the modeling and optimization of planning and decision problems and who need a sound introduction to the software.

Solutions to the exercises as well as the source codes from the textbook are available for download (weblink included).

✦ Table of Contents

Preface
Table of contents
List of abbreviations
Part 1 Lessons
1 Introduction
1.1 Modeling and solving planning and decision-making problems
1.2 IBM ILOG CPLEX Optimization Studio
1.3 Introductory example
1.4 Content covered in the book
2 IBM ILOG CPLEX Optimization Studio—A primer
2.1 Licenses and installation
2.2 Starting the Studio
2.3 First steps
2.3.1 Importing a sample project
2.3.2 The components of a project
2.3.3 Solving a model instance
2.3.4 Studio components
2.3.5 Analysis of models, results, and the solution process
2.3.6 Creating a new project and importing existing projects
2.4 Studio Help and other resources
3 Setting up a model
3.1 Declaration and initialization of model parameters
3.1.1 Declaration
3.1.2 Initialization
3.2 Declaration of decision variables
3.3 Objective function
3.4 Constraints
3.5 Solution of Case Study 1
3.6 Exercises
4 Data structures and related OPL language elements
4.1 Ranges
4.2 Arrays
4.2.1 Declaration and explicit initialization of arrays and array access
4.2.2 Arrays of decision variables
4.2.3 Multidimensional arrays
4.2.4 Further possibilities for initializing arrays
4.2.5 Placeholders and filters
4.3 Sets
4.3.1 Declaration and explicit initialization of sets
4.3.2 Further options for initializing sets
4.3.3 Index sets
4.3.4 Ordered sets
4.3.5 Set operations and functions for sets
4.4 Aggregate operators
4.4.1 Summation
4.4.2 Other aggregate operators
4.5 The forall-quantifier
4.6 Solution of Case Study 2
4.7 Exercises
5 Introduction to IBM ILOG Script
5.1 Structure and basic language elements of IBM ILOG Script
5.2 OPL data elements and IBM ILOG script variables
5.3 Other language elements
5.3.1 Control structures
5.3.2 Mathematical operators and functions
5.3.3 Properties and functions of ranges, arrays, and sets
5.3.4 String processing
5.3.5 Definition of customized functions
5.4 Solution of Case Study 4
5.5 Exercises
6 Modeling with tuples
6.1 Tuple
6.1.1 Definition, declaration, and initialization
6.1.2 Linking tuples
6.1.3 Tuples in IBM ILOG Script
6.2 Tuple sets
6.2.1 Initialization
6.2.2 Decision variables
6.2.3 Key attributes
6.2.4 Linking tuple sets
6.2.5 Tuple sets in IBM ILOG Script
6.3 Efficient modeling with tuple sets
6.3.1 Slicing
6.3.2 Efficient access structures
6.3.3 Efficient and inefficient modeling of linked tuple sets
6.4 Solution of Case Study 4: Efficient modeling of sparse matrices by tuple sets
6.4.1 Modeling by a matrix
6.4.2 Modeling by a tuple set
6.4.3 Runtime comparison between matrix and tuple modeling for large data sets
6.5 Exercises
7 Separating model and data
7.1 Internal and external data initialization
7.2 Explicit initialization in data files
7.2.1 Basic model parameters
7.2.2 Arrays
7.2.3 Sets
7.2.4 Tuples
7.3 Connecting spreadsheet files
7.4 Solution of Case Study 5
7.5 Exercises
8 Selected features of OPL and CPLEX Optimization Studio
8.1 Modeling with logical operations
8.1.1 Logical operators in OPL
8.1.2 Modeling with logical operators
8.1.3 Example of using "==" as a logical versus relational operator
8.1.4 Conversion of logical operations into linear constraints
Linking binary variables
Linking binary variables and "real" constraints
8.1.5 Counting sums
8.2 Piecewise linear functions
8.2.1 Special case: step functions
8.2.2 General piecewise linear functions
Continuous functions
Functions with jump points
8.2.3 Modeling piecewise linear functions by linear constraints
General linearization techniques
Special cases
8.3 Conflicts and relaxations
8.3.1 Output in the Conflicts tab
8.3.2 Output in the Relaxations tab, Engine log tab, Solutions tab, and Problem browser
8.4 Solution of Case Study 6
8.5 Exercises
9 Selected features of IBM ILOG Script
9.1 Input and output with IBM ILOG Script
9.1.1 Reading from and writing to text files
9.1.2 Reading from CSV files
9.2 Pre- and Postprocessing with IBM ILOG Script
9.3 Runtime measurement and CPLEX settings
9.4 Solution of Case Study 7
9.5 Exercises
10 Flow control with ILOG Script
10.1 Overview and use cases
10.2 Accessing model and data files
10.3 Solution of Case Study 8
10.4 Reference to further implementation examples
10.5 Exercises
Part 2 Application studies
11 Covering location planning
Proposed solution
12 Fleet sizing
Proposed solution
13 Location planning
Proposed solution
14 Transportation planning
Proposed solution
15 Revenue Management
Proposed solution
16 Lot sizing
Proposed solution
Bibliography
Index