Adaptive Neuro-Fuzzy Inference System Approach for Sustainability Assessment in Paper Industry

Subject Areas : Fuzzy sets theory

Salman Akhoundzadeh ¹ , Mansour Soufi ^{2 *} , Seyedeh Ameneh Sajjadi ³ , Mehdi Fadaei ⁴

1 - Department of Industrial Management, Ra.C., Islamic Azad University, Rasht, Iran
2 - Department of Industrial Management, Ra.C., Islamic Azad University, Rasht, Iran
3 - Department of Agronomy, Ra.C., Islamic Azad University, Rasht, Iran
4 - Department of Industrial Management, Ra.C., Islamic Azad University, Rasht, Iran

Received: 2025-09-13 Accepted : 2025-10-14 Published : 2025-10-21

Keywords: Sustainability, Paper Industry, Adaptive Neuro-Fuzzy Inference System (ANFIS), Green Production,

Abstract :

Paper industry, as one of the largest consumers of natural resources, faces increasing pressure to adopt sustainable practices that balance economic growth, environmental responsibility, and social well-being. Traditional evaluation methods often fall short in capturing the sector’s complex interdependencies and nonlinear dynamics. This study introduces a hybrid framework based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess the sustainability performance of Iranian paper manufacturing companies. A comprehensive set of 93 indicators in three sustainability indicators was identified through the literature review and expert consultation. Principal Component Analysis (PCA) was employed to refine these into 24 critical indicators, which were subsequently used to structure the ANFIS model in MATLAB. The model was trained and validated using survey data from industry professionals across multiple companies, with results demonstrating. In this study, Gaussian membership functions were selected for all input and output variables. Fuzzy rules were developed to link social, environmental, and economic sustainability dimensions, each with five membership functions. One main and three sub-models for social (5 inputs), environmental (10 inputs), and economic (9 inputs) sustainability. Data from 144 respondents across four companies, split into 70% training, 15% testing, and 15% validation, trained the model using a hybrid learning method, achieving acceptable error thresholds after 60 epochs. Validation confirmed reliability, with testing and validation errors of 0.00163 and 0.00097, respectively, and boundary tests showed logical responses. The model evaluated Setareh Shomal’s sustainability, scoring social (0.58), environmental (0.61), and economic (0.65), highlighting strengths in customer satisfaction and operational efficiency and weaknesses in energy consumption and employee safety. It is recommended that future organizational efforts prioritize improving working conditions and employee health, optimizing energy and water consumption, and reducing environmental pollutants, in order to achieve greater balance across the three core pillars of sustainability.

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