Introduction to Optimization-Based Decision-Making

The growing importance of big data, artificial intelligence, data science, and machine learning requires a basic understanding of mathematical optimization for effective decision-making. Introduction to Optimization-Based Decision-Making provides an elementary introduction to the concepts involved, suitable for students from STEM fields, economics, and enterprise sciences, as well as the general reader.

Format: Hardback
Length: 241 pages
Publication date: 20 December 2021
Publisher: Taylor & Francis Ltd

The world faces significant and complex challenges, driven by the growing prevalence of massive data sets and the emergence of new and innovative approaches to address them, such as artificial intelligence, data science, machine learning, and others. In this evolving landscape, it has become increasingly crucial for academics and professionals from diverse disciplines to possess a fundamental understanding of the mathematical foundations of effective and optimized decision-making. Without this foundational knowledge, decision-makers risk being left behind by those who possess a deeper understanding of the models and methods that can best inform strategic and tactical decisions.

To meet this demand, Introduction to Optimization-Based Decision-Making offers a comprehensive and self-contained introduction to the core concepts involved in making decisions within an optimization-based environment. The text is designed to cater to post-secondary readers, or university students in the early stages of their academic journey. The prerequisites for this text are minimal, and necessary mathematical tools are provided as and when required. This lean approach is complemented by a problem-based orientation and a methodology of generalization/reduction, making it valuable for students from STEM fields, economics, enterprise sciences, social sciences, and humanities, as well as for the general reader interested in multi/trans-disciplinary approaches.

Key Features:
1. Collects and discusses the ideas underpinning decision-making through optimization tools in a simple and straightforward manner.
2. Suitable for an undergraduate course in optimization-based decision-making, or as a supplementary resource for courses in operations research and management science.
3. Self-contained coverage of traditional and more modern optimization models, without requiring a prior background in decision theory.

In conclusion, the world is facing unprecedented challenges, and the ability to make informed decisions based on data and optimization techniques is more critical than ever. Introduction to Optimization-Based Decision-Making provides a solid foundation for students and professionals seeking to excel in this field, equipping them with the necessary mathematical knowledge and problem-solving skills to navigate the complex landscape of decision-making in the 21st century.

Weight: 506g
Dimension: 219 x 278 x 21 (mm)
ISBN-13: 9781138712164