Application of a hybrid two-stage optimization framework for sustainable machining: a case study DOI

Muhammad Atif,

Faraz Junejo,

Imran Amin

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Language: Английский

Adaptive predator prey algorithm for many objective optimization DOI Creative Commons
Nikunj Mashru, Kanak Kalita, Lenka Čepová

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 12, 2025

Language: Английский

Citations

0
An evolutionary multitasking algorithm based on k-nearest neighbors pre-selection strategy for constrained multi-objective optimization DOI

Jiang Mengqi,

Xiaochuan Gao, Qianlong Dang

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127768 - 127768

Published: April 1, 2025

Language: Английский

Citations

0
Multi-Objective Optimization in Industry 5.0: Human-Centric AI Integration for Sustainable and Intelligent Manufacturing DOI Open Access
Shu‐Chuan Chen,

Hsien-Ming Chen,

Han-Kwang Chen

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(12), P. 2723 - 2723

Published: Dec. 2, 2024

The shift from Industry 4.0 to 5.0 represents a significant evolution toward sustainable, human-centric manufacturing. This paper explores how advanced multi-objective optimization techniques can integrate Artificial Intelligence (AI) with human insights enhance both sustainability and customization in We investigate specific methods, including genetic algorithms (GAs), Particle Swarm Optimization (PSO), reinforcement learning (RL), which are tailored balance efficiency, waste reduction, carbon footprint. Our proposed framework enables creativity interact AI-driven processes, embedding input into computational structure that adapts dynamically operational goals. By linking directly environmental impacts, such as reducing waste, energy consumption, emissions, this study establishes pathway environmentally sustainable production. research fills existing gaps by offering detailed, practical model harmonizes theoretical applications personalized manufacturing environments. In regard, it contributes the ongoing development of 5.0, emphasizing AI collaboration foster intelligent, adaptable, systems.

Language: Английский

Citations

1
Trends, Approaches, and Gaps in Scientific Workflow Scheduling: A Systematic Review DOI Creative Commons
Aurelio Vivas, Andrei Tchernykh, Harold Castro

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 182203 - 182231

Published: Jan. 1, 2024

Language: Английский

Citations

0
Application of a hybrid two-stage optimization framework for sustainable machining: a case study DOI

Muhammad Atif,

Faraz Junejo,

Imran Amin

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Language: Английский

Citations

0