Mixed integer (0–1) fractional programming for decision support in paper production industry

• The study provides an overview on the use of management tools in paper production.
• Impact of raw material composition and its treatment has not been modelled so far.
• This real-life application has been used for more than 20 years in paper industry.
• A recent upgrade gave rise to a mixed integer (0–1) fractional programming problem.
• An effective and efficient non-nested solution procedure was developed.

This paper presents an effective and efficient method for solving a special class of mixed integer fractional programming (FP) problems. We take a classical reformulation approach for continuous FP as a starting point and extend it for solving a more general class of mixed integer (0–1) fractional programming problems.To stress the practical relevance of the research we focus on a real-life application in paper production industry. The constantly advancing physical knowledge of large scale pulp and paper production did have a substantial impact on an existing DSS in which mixed integer (0–1) fractional programming is introduced. We show that the motivation to solve a real-life fractional programming problem can provide the basis for a new approach in a new context that has an added value of its own, even outside the given application area. We describe the main characteristics of the DSS, the necessity to develop a non-iterative solution procedure and demonstrate both the effectiveness and efficiency of the proposed approach from practical data sets.