Decision Making with Dominance Constraints in Two-Stage Stochastic Integer Programming

1. Front Matter

   Pages I-IX

   PDF

2. Introduction

   • Uwe Gotzes

   Pages 1-12

3. Increasing Convex Order Constraints Induced by Mixed-Integer Linear Recourse

   • Uwe Gotzes

   Pages 13-32

4. Competitive Risk-Averse Selling Price Determination for Electricity Retailers

   • Uwe Gotzes

   Pages 33-47

5. Decomposition Method

   • Uwe Gotzes

   Pages 49-59

6. Test Instances

   • Uwe Gotzes

   Pages 61-72

7. An Alternative Formulation for Optimization under Stochastic Dominance Constraints

   • Uwe Gotzes

   Pages 73-78

8. Back Matter

   Pages 79-91

   PDF

Two-stage stochastic programming models are considered as attractive tools for making optimal decisions under uncertainty. Traditionally, optimality is formalized by applying statistical parameters such as the expectation or the conditional value at risk to the distributions of objective values.

Uwe Gotzes analyzes an approach to account for risk aversion in two-stage models based upon partial orders on the set of real random variables. These stochastic orders enable the incorporation of the characteristics of whole distributions into the decision process. The profit or cost distributions must pass a benchmark test with a given acceptable distribution. Thus, additional objectives can be optimized. For this new class of stochastic optimization problems, results on structure and stability are proven and a tailored algorithm to tackle large problem instances is developed. The implications of the modelling background and numerical results from the application of the proposed algorithm are demonstrated with case studies from energy trading.

• Entscheidungsfindung
• Unsicherheit
• Zufallsvariablen
• linear optimization
• modeling
• partial orders
• programming
• stochastic orders

Dr. Uwe Gotzes completed his doctoral thesis at the Department of Mathematics at the University of Duisburg-Essen. He is a network planner at E.ON Gastransport.

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