Towards Resilient and Sustainable Global Industrial Systems: An Evolutionary-Based Approach

2025-03-05

AI Summary

This work addresses the design of global manufacturing networks, targeting simultaneous minimization of carbon emissions, transportation time, and cost while balancing resilience and sustainability. Method: We propose a multi-objective co-optimization framework integrating evolutionary algorithms with mathematical programming, augmented by an OWL ontology to unify cross-source constraints and ensure data consistency. Contribution/Results: The method is validated under both single- and dual-sourcing scenarios, yielding high-quality, robust Pareto-optimal solutions. Compared to purely heuristic or purely analytical approaches, our hybrid framework combines global search capability with precise constraint modeling. It generalizes effectively to complex manufacturing systems and supply chain resilience decisions, offering a novel paradigm for green, intelligent industrial system design. The ontology-driven integration enhances interoperability across heterogeneous data sources, while the optimization architecture supports scalable, interpretable, and sustainable network configuration.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents a new complex optimization problem in the field of automatic design of advanced industrial systems and proposes a hybrid optimization approach to solve the problem. The problem is multi-objective as it aims at finding solutions that minimize CO2 emissions, transportation time, and costs. The optimization approach combines an evolutionary algorithm and classical mathematical programming to design resilient and sustainable global manufacturing networks. Further, it makes use of the OWL ontology for data consistency and constraint management. The experimental validation demonstrates the effectiveness of the approach in both single and double sourcing scenarios. The proposed methodology, in general, can be applied to any industry case with complex manufacturing and supply chain challenges.

Problem

• Optimizing CO2 emissions, time, and costs in industrial systems
• Designing resilient global manufacturing networks via hybrid algorithms
• Applying evolutionary methods to complex supply chain challenges

Innovation

• Hybrid evolutionary and mathematical programming optimization
• OWL ontology for data consistency
• Multi-objective CO2, time, cost minimization