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2024 | Book

The Decision Tree Procedure Read chapter Read first chapter

Operations Research and Management

Quantitative Methods for Planning and Decision-Making in Business and Economics

Editors: Franz W. Peren, Thomas Neifer

Publisher: Springer Nature Switzerland

Book Series : Springer Texts in Business and Economics

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

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About this book

This textbook introduces quantitative methods in operations management, based on operational research. Written for undergraduate and graduate students as well as practitioners, this book serves as a valuable compendium of essential tools for project planning, control, and strategic decision-making.

Drawing from the expertise of both experienced scientists and seasoned practical managers, the descriptions of each tool are a harmonious blend of theoretical insights and real-world applicability. With a focus on accessibility, the authors have thoughtfully combined abstract concepts with easy-to-follow examples and detailed case studies.

Readers will benefit from the abundance of well-explained recommendations and practical problem-solving approaches, where the book offers guidance on how to solve presented issues by using commercial software. Whether one seeks to refine project management, optimize operations, or make strategic choices, this book equips readers with the knowledge and proficiency required to excel in the dynamic field of operations management.

Table of Contents

Frontmatter

Linear Optimization and Heuristics

Frontmatter
The Decision Tree Procedure
Abstract
In an interconnected world, decision-making has become increasingly complex and critical. This complexity is driven by a multitude of interconnected decisions and the rapid evolution of technologies and markets. Managers must navigate this landscape by quickly evaluating numerous options and selecting the optimal course of action, particularly in areas such as investment, financing, and production processes. Decision theory, a subfield of operations research, offers valuable insights and tools to address these challenges. By providing the theoretical foundations and practical case studies, this work gives a deeper understanding of the decision tree procedure to support decision-making.
Katharina Völker, Maresa Hümmer
Linear Optimization
Abstract
Linear optimization, a fundamental technique of operations research, plays a central role in the optimization of decision processes. This work gives an overview of linear programming and highlights its importance in solving complex problems by optimizing linear models with constraints.
Franz W. Peren
Cutting and Packaging Optimization
Abstract
Cutting and packing problems, common in manufacturing and logistics, demand systematic solutions to minimize costs and optimize resources. This paper explores the utility of typologies such as Dyckhoff’s and Wäscher et al.’s in simplifying complex problem types. It presents an operational research workflow for companies to economically address these challenges.
Kalvin M. Kroth
Queueing Theory
Abstract
As soon as more orders are received than can be processed within a period, queues occur. This affects the company’s performance and represents a challenge that has to be dealt with. Hence, the understanding and management of queues is a fundamental task for the operations management. In summary, it can be said that the queueing theory provides only the possibility to support planning functions. However, a certain behaviour of the system has to be assumed by the adoption of certain distributions (exponential function). In reality, several simulations are necessary otherwise the results may not be meaningful because they could offer a high spread. In some cases, adjustments of the system may be necessary. All in all, the use of the queueing theory theoretically enables managers to calculate and simulate a queueing model in a short time.
Maximilian Adolphs, Sascha Feistner, Violetta Jahnke
Sequencing Problems
Abstract
Sequencing problems, which involve determining the optimal sequence of process steps, are critical in manufacturing, service, and distribution. These problems aim to achieve uniform resource utilization and minimize machine setup costs. Solutions can be approached using mathematical optimization methods for exact solutions or heuristic methods for good but possibly non-optimal solutions. In this work, sequencing problems are presented using examples such as the traveling salesman problem and the flow shop problem.
Thomas Neifer, Franz W. Peren
Regression Analysis Using Dummy Variables
Abstract
Regression analysis is a versatile method for the analysis and description of business problems. It is based on the development of a model that allows a forecast into the future using historical data. Dummy coding is required when categorically independent variables are to be included in a multiple regression analysis. In the business context, for example, different customer groups can be differentiated based on categorical characteristics.
Thomas Neifer
Heuristic Methods
Abstract
The term "heuristics" is derived from the ancient Greek word "heuriskein," which means to discover and explore. In the context of problem solving, heuristics are rules of thought that speed up the solution of complex problems, even though they often lead to satisfactory rather than optimal results. They are particularly valuable in solving optimization problems in the context of operations research. Heuristics are guided by principles such as factorization, modeling, well-defined goals, generation and testing, and bounded rationality. Although they cannot guarantee optimality, they serve as practical tools in various fields, including economics, sports, environment, medicine, and engineering.
Laura Schwarzbach, Ramona Schmitt

Simulation

Frontmatter
Simulation Processes in Business and Economics: Fundamentals of the Monte Carlo Simulation
Abstract
Simulations, especially Monte Carlo simulations, are a common tool in operations research. They enable the study of complex scenarios, evaluation of costeffectiveness, risk mitigation, and efficient testing of improbable situations. Monte Carlo simulations facilitate decision-making by creating realistic experimental models for various operations research purposes.
Alexander Wachholz, Richard Malzew
Markov Chain Monte Carlo Methods
Abstract
The Markov Chain Monte Carlo (MCMC) methods based on the Bayes theorem are used when an a posteriori distribution does not have a tractable form and is therefore not fully known or directly usable (e.g., for maximum a posteriori parameter estimation). MCMC methods overcome intractability by drawing parameter values from known distributions and correlating these drawings until they approximately match the target distribution. MCMC methods represent a powerful class of algorithms for processing data and knowledge, which is why they are also called a "quantum leap in statistics".
Thomas Neifer

Nonlinear Optimization

Frontmatter
Nonlinear Optimization: The Nelder-Mead Simplex Search Procedure
Abstract
This work explores the field of nonlinear optimization, focusing on the complexity of solving real-world problems with nonlinear functions, continuous variables, and various constraints. It addresses the iterative nature of these optimization problems and their convergence to locally optimized solutions.
Franz W. Peren
Dynamic Programming
Abstract
Dynamic Programming, or dynamic optimization, is an optimization approach that simplifies complex problems by breaking them into smaller, interconnected subproblems. This method eliminates redundancy and significantly improves efficiency. DP finds practical applications in various real-world problems within Operations Research, enhancing decision-making processes. Its usefulness is shown by two examples with a practical application in Python.
Thomas Neifer, Dennis Lawo

Project Management

Frontmatter
Network Analysis Method
Abstract
This work provides an overview of the methods of the network technique in project management. The network diagram technique has evolved over the years to provide valuable insights into project planning and critical path analysis. This paper describes its practical application using modern project management software and presents an Excel-based approach to creating diagrams.Understanding these methods provides project managers with important tools for effective project planning and execution.
Matthias Krebs
The Peren-Clement Index
Abstract
The Peren–Clement index is a method of country-specific risk analysis for direct investments. This analysis tool provides a guideline when deciding which foreign markets offer the possibility of additional business engagement and investment and the extent of an existing engagement or investment can be increased or should be reduced. The Peren-Clement index can be used as an early detection system, which evaluates probabilities and risks of an investment in a certain foreign market, which are determined by the political situation, its social, economical and judicial environment as well as its predictable or anticipated future developments of the analysed country.
Reiner Clement, Franz W. Peren
The Peren Theorem
Abstract
The Peren Theorem addresses the critical issue of unsustainable resource consumption by humans and its potential consequences for the planet. This theorem establishes a mathematical relationship that shows that human consumption of natural resources, if it exceeds the Earth’s regenerative capacity in the long term, will lead to the complete depletion of the planet’s natural environment. This article discusses the urgent need to shift to sustainable consumption patterns and reevaluate the definition of wealth.
Franz W. Peren
Metadata
Title
Operations Research and Management
Editors
Franz W. Peren
Thomas Neifer
Copyright Year
2024
Electronic ISBN
978-3-031-47206-0
Print ISBN
978-3-031-47205-3
DOI
https://doi.org/10.1007/978-3-031-47206-0