Decision Support Model for Tissue Paper Production Using Multiple Linear Regression and Linear Programming

Tissue paper production is an energy-intensive industry that requires a delicate balance between quality and cost due to high energy and resource consumption. This study aims to predict dry strength using a multiple linear regression model based on historical production data and to determine production parameters that meet quality targets at the lowest cost. The regression coefficients obtained were integrated into a linear programming-based optimization model to minimize the costs of dry strength chemical, enzyme, and refiner energy. The developed model provides production settings that meet quality targets while optimizing resource usage. Analysis results suggest that the proposed parameters could offer approximately 5% cost savings. The model is evaluated as an effective tool that supports operational decision-making, enhances efficiency, and strengthens sustainability when integrated into production processes.