Towards Resilient and Sustainable Global Industrial Systems: An Evolutionary-Based Approach
This addresses the need for sustainable and efficient manufacturing networks in industries, though it appears incremental as it builds on existing optimization methods.
The paper tackles the problem of designing resilient and sustainable global industrial systems by minimizing CO2 emissions, transportation time, and costs, proposing a hybrid optimization approach that combines an evolutionary algorithm and mathematical programming, with experimental validation showing effectiveness in sourcing scenarios.
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.