Green Manufacturing Capacity Planning by Integrating Distributionally Robust Optimization and Generative AI
Green manufacturing has become a strategic priority for many firms seeking to address sustainability and social responsibility, while improving production efficiency and profitability. However, integrating green technologies and renewable energy unavoidably introduces climate-related randomness that affects both product demand and renewable energy generation, underscoring the need for coordinated planning of production capacity and renewable energy development. To address this challenge, we develop a comprehensive two-stage distributionally robust optimization (DRO) model for green manufacturing capacity planning in a multi-factory, multi-capacity, and multi-product setting, based on an ambiguity set constructed by a data-driven clustering technique that leverages historical data of different availabilities and qualities. To handle the computational challenges of practical instances, an effective generative AI network is integrated into an exact decomposition algorithm, through a novel encoding/decoding scheme designed to provide the AI model with structurally informative training data and to convert AI-generated outputs into algorithm-accessible formats. Experimental results on real-world instances demonstrate that the proposed DRO approach achieves strong economic performance and robust feasibility under demand and renewable generation uncertainty, while also significantly improving computational efficiency and solution consistency relative to the standard approaches. Furthermore, our results highlight the managerial value of integrating green technology adoption with coordinated capacity planning to better utilize renewable energy and align production efficiency with sustainability and corporate social responsibility objectives.