Robust Discrete Optimization and Its Applications

1. Front Matter

   Pages i-xvi

   PDF

2. Approaches for Handling Uncertainty in Decision Making

   • Panos Kouvelis, Gang Yu

   Pages 1-25

3. A Robust Discrete Optimization Framework

   • Panos Kouvelis, Gang Yu

   Pages 26-73

4. Computational Complexity Results of Robust Discrete Optimization Problems

   • Panos Kouvelis, Gang Yu

   Pages 74-115

5. Easily Solvable Cases of Robust Discrete Optimization Problems

   • Panos Kouvelis, Gang Yu

   Pages 116-152

6. Algorithmic Developments for Difficult Robust Discrete Optimization Problems

   • Panos Kouvelis, Gang Yu

   Pages 153-192

7. Robust 1-Median Location Problems: Dynamic Aspects and Uncertainty

   • Panos Kouvelis, Gang Yu

   Pages 193-240

8. Robust Scheduling Problems

   • Panos Kouvelis, Gang Yu

   Pages 241-289

9. Robust Uncapacitated Network Design and International Sourcing Problems

   • Panos Kouvelis, Gang Yu

   Pages 290-332

10. Robust Discrete Optimization: Past Successes and Future Challenges

    • Panos Kouvelis, Gang Yu

    Pages 333-356

11. Back Matter

    Pages 357-357

    PDF

This book deals with decision making in environments of significant data un­ certainty, with particular emphasis on operations and production management applications. For such environments, we suggest the use of the robustness ap­ proach to decision making, which assumes inadequate knowledge of the decision maker about the random state of nature and develops a decision that hedges against the worst contingency that may arise. The main motivating factors for a decision maker to use the robustness approach are: • It does not ignore uncertainty and takes a proactive step in response to the fact that forecasted values of uncertain parameters will not occur in most environments; • It applies to decisions of unique, non-repetitive nature, which are common in many fast and dynamically changing environments; • It accounts for the risk averse nature of decision makers; and • It recognizes that even though decision environments are fraught with data uncertainties, decisions are evaluated ex post with the realized data. For all of the above reasons, robust decisions are dear to the heart of opera­ tional decision makers. This book takes a giant first step in presenting decision support tools and solution methods for generating robust decisions in a variety of interesting application environments. Robust Discrete Optimization is a comprehensive mathematical programming framework for robust decision making.

• Mathematica
• algorithms
• complexity
• computer
• computer science
• linear optimization
• operations research
• optimization
• production
• programming
• scheduling
• systems engineering

`....I recommend the book, which in large parts is easy to read, as a consistent and interesting entry into the field of robust optimization.'
OR Spektrum, 20:278 (1998)

• Olin School of Business, Washington University at St. Louis, St. Louis, USA

  Panos Kouvelis

• Center for Cybernetic Studies, The University of Texas, Austin, USA

  Gang Yu

Policies and ethics