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.