Supply chain disruption risk models often separate risk prioritization from mitigation design, limiting their ability to support actionable decision-making under uncertainty. This study addresses this ...
Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated strong performance in solving low- and medium-dimensional expensive multi-objective optimization problems (EMOPs).
ABSTRACT: Multi-objective optimization remains a significant and realistic problem in engineering. A trade-off among conflicting objectives subject to equality and inequality constraints is known as ...
Multi-objective fuzzy Flexible Job shop Scheduling Problems (MofFJSPs) aim to optimize multiple fuzzy and conflicting objectives by finding the sequences of jobs and machines under realistic ...
In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...
1 School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou, China. 2 School of Science and Technology, Hunan University of Technology, Zhuzhou, China. To address the multicoupling ...
Abstract: dynamic multiobjective optimization (DMO) problems are prevalent in many practical applications and have garnered significant attention from both industry and academia, leading to the ...
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