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2022 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Decision Optimization with IBM ILOG CPLEX Optimization Studio

A Hands-On Introduction to Modeling with the Optimization Programming Language (OPL)

verfasst von: Prof. Stefan Nickel, Prof. Dr. Claudius Steinhardt, Dr. Hans Schlenker, Dr. Wolfgang Burkart

Verlag: Springer Berlin Heidelberg

Buchreihe : Graduate Texts in Operations Research

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

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).

Inhaltsverzeichnis

Frontmatter

Lessons

Frontmatter
1. Introduction
Abstract
Big Data, Artificial Intelligence, Business Analytics, Data Science—every major company today is dealing with these or related topics in order to remain competitive in the "digital world" or even to implement completely new, disruptive business models. All of these topics are connected to companies looking for new ways to profitably exploit the immense amounts of data available for management decisions and to proceed as "intelligently" and in a way that is as automated as possible. This affects all levels of decision-making—from strategic to operational—as well as all corporate and business functions, whether general management, finance, marketing, or operations.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
2. IBM ILOG CPLEX Optimization Studio—A primer
Abstract
This chapter introduces the Integrated Development Environment (IDE), which is central to model development and which, in short, is referred to as Studio. It explains how to install CPLEX Optimization Studio on a computer, how to start the Studio, which basic functionalities and components are available there, how to create projects, add optimization models, and have them solved.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
3. Setting up a model
Abstract
As we pointed out in Chapter 2, optimization problems are formulated in model files (file extension .mod). In these files, the optimization problems are represented by corresponding mathematical models using IBM ILOG’s programming language OPL. The resulting program code consists of a sequence of OPL statements (a.k.a. OPL commands) in text format that can be entered comfortably with the help of the Studio as a development environment (cf. Chapter 2).
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
4. Data structures and related OPL language elements
Abstract
Often, a problem contains parameters that are related to each other because they all refer to the same type of entities in the real world (e.g., production cost of different bicycle types) or represent attributes of a common entity (e.g., contribution margin and demand quantity of bicycle type U-A). By combining them into data structures, the modeling effort can be significantly reduced and the readability of the program code improved. In addition, these data structures can be used to easily build generic formulations.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
5. Introduction to IBM ILOG Script
Abstract
OPL is a declarative modeling language used to define optimization models. As we have shown in the previous chapters, data types, input and output data, as well as decision variables, are defined and related to each other: e.g., by decision expressions, the objective function, or constraints. In principle, no algorithms need to be formulated for a model to work.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
6. Modeling with tuples
Abstract
The language elements described in the previous chapters can be used to build small to medium-sized OPL models that also allow generic parameterizations. In practical applications, however, very large amounts of data with complex interrelations must often be processed.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
7. Separating model and data
Abstract
The previous chapters mostly included model parameters in the form of placeholders that were declared and then initialized with specific values at the beginning of the respective model file.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
8. Selected features of OPL and CPLEX Optimization Studio
Abstract
After introducing the basic language elements of OPL in the previous chapters, the focus is now on three specific features of OPL and the CPLEX Optimization Studio: (i) working with logical operators, (ii) implementing piecewise-defined functions, and (iii) analyzing infeasible models including approaches to correction.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
9. Selected features of IBM ILOG Script
Abstract
Building on the basics of IBM ILOG Script dealt with in Chapter 5, this chapter presents a selection of advanced applications of IBM ILOG Script to illustrate best practices. On the one hand, they concern possibilities for the input and output of data—e.g., for reading and writing files.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
10. Flow control with ILOG Script
Abstract
Chapters 5 and 9 explained how IBM ILOG Script can be used in model files, especially to perform data preparation and postprocessing tasks. However, IBM ILOG Script’s application possibilities go considerably further.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart

Application studies

Frontmatter
11. Covering location planning
Abstract
To boost the popularity of RideEasy's bikes, the company’s regional director for North America, Mr. Rent, wants to set up a station-based bike-sharing system in San Francisco. To this end, he has divided the downtown area into six districts eligible for the construction of stations (maximum of one station per district, see Fig. 11.1).
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
12. Fleet sizing
Abstract
Lamp 'n Bulb manufactures lamps for various applications and makes daily deliveries to its customers in the region, including to RideEasy. As part of its cost reduction projects, Lamp 'n Bulb is reconsidering the size of its delivery truck fleet. To this end, the logistics department has set up the distribution plan given in Table 12.1, which indicates how many deliveries are to be made per day.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
13. Location planning
Abstract
RideEasy opened four direct-to-consumer retail stores in Manhattan last year. Since delivering to these stores from Brooklyn turned out to be very cost-intensive, the company has decided to set up its own delivery warehouse in Manhattan and is looking for a suitable location.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
14. Transportation planning
Abstract
The bicycles that RideEasy produces are transported between the various production and customer retail locations in North America in containers specifically designed for this purpose.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
15. Revenue Management
Abstract
RideEasy is planning to change the fare structure for its bike-sharing stations in San Francisco, and to do so properly, the company analyzed data on rentals over the past few months.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
16. Lot sizing
Abstract
The demand for bicycles in the four stores in Manhattan has increased to such an extent that RideEasy has decided to launch a high-quality special edition CycleMe specifically targeted at the New York market.
Stefan Nickel, Claudius Steinhardt, Hans Schlenker, Wolfgang Burkart
Backmatter
Metadaten
Titel
Decision Optimization with IBM ILOG CPLEX Optimization Studio
verfasst von
Prof. Stefan Nickel
Prof. Dr. Claudius Steinhardt
Dr. Hans Schlenker
Dr. Wolfgang Burkart
Copyright-Jahr
2022
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-65481-1
Print ISBN
978-3-662-65480-4
DOI
https://doi.org/10.1007/978-3-662-65481-1

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