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AI-Driven Metaheuristic Optimization for Sustainable and Adaptive Facility Layouts in Smart Manufacturing

Year: 2025

Type: article

Abstract: This paper proposes an adaptive facility layout framework leveraging AI-driven metaheuristic algorithms to optimize energy-efficient and sustainable manufacturing layouts. The study integrates big data analytics, machine learning, and heuristic optimization techniques to minimize operational costs while ensuring environmental and social sustainability. A hybrid Firefly Algorithm and Chaotic Simulated Annealing approach is developed to generate optimized layout solutions under stochastic demand conditions. The proposed model is validated using real-world manufacturing scenarios, demonstrating its capability to enhance productivity, reduce carbon footprints, and maintain high safety standards. The results show significant improvements in layout efficiency, adaptability, and sustainability, making the framework suitable for modern smart manufacturing facilities.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15254733

Authors: Ukurta Tarigan, Sukaria Sinulingga, Sutarman, Meilita Tryana Sembiring

Institution: Universitas Sumatera Utara

Language: English

Topic: Advanced Manufacturing and Logistics Optimization

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production

Open Access status: green

OpenAlex ID: W6968084023

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