Decision optimization with IBM ILOG CPLEX Optimization Studio [electronic resource] : a hands-on introduction to modeling with the Optimization Programming Language (OPL) / Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart.

Intro
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