Integrated Production–Distribution Planning for Paper Manufacturing Under Fuzzy Uncertainty

Abstract

The paper manufacturing industry faces significant challenges in coordinating production and distribution decisions under uncertain market conditions. This research presents an integrated production–distribution planning model for paper manufacturing that addresses demand uncertainty through fuzzy set theory. The model considers multiple paper grades, production facilities, warehouses, and customer zones while minimizing total supply chain costs. A hybrid intelligent algorithm combining genetic algorithms with fuzzy simulation is developed to solve the complex optimization problem. The approach handles fuzzy demand parameters using credibility theory and employs Monte Carlo simulation for fuzzy variable evaluation. Computational experiments demonstrate the effectiveness of the proposed methodology, achieving cost reductions of 12–18% compared to traditional deterministic approaches. The results indicate that the fuzzy–genetic algorithm approach provides robust solutions that perform well under various uncertainty scenarios, making it suitable for practical implementation in paper manufacturing supply chains.