Na Geng | Searcharxiv

arXiv2026

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.

math.OC↗

arXiv2022

Frequent Itemset-driven Search for Finding Minimum Node Separators in Complex Networks

Finding an optimal set of critical nodes in a complex network has been a long-standing problem in the fields of both artificial intelligence and operations research. Potential applications include epidemic control, network security, carbon emission monitoring, emergence response, drug design, and vulnerability assessment. In this work, we consider the problem of finding a minimal node separator whose removal separates a graph into multiple different connected components with fewer than a limited number of vertices in each component. To solve it, we propose a frequent itemset-driven search approach, which integrates the concept of frequent itemset mining in data mining into the well-known memetic search framework. Starting from a high-quality population built by the solution construction and population repair procedures, it iteratively employs the frequent itemset recombination operator (to generate promising offspring solution based on itemsets that frequently occur in high-quality solutions), tabu search-based simulated annealing (to find high-quality local optima), population repair procedure (to modify the population), and rank-based population management strategy (to guarantee a healthy population). Extensive evaluations on 50 widely used benchmark instances show that it significantly outperforms state-of-the-art algorithms. In particular, it discovers 29 new upper bounds and matches 18 previous best-known bounds. Finally, experimental analyses are performed to confirm the effectiveness of key algorithmic modules of the proposed method.

cs.NE↗