Abstract
Multi-objective optimization (MOO) is a crucial approach in the design and operation of production systems, where multiple conflicting objectives must be balanced to achieve optimal performance. This study investigates the application of multi-objective optimization techniques in production systems, focusing on objectives such as cost minimization, time efficiency, resource utilization, and product quality. Various optimization methods, including Pareto-based approaches, genetic algorithms, and fuzzy logic, are explored for their effectiveness in solving complex production optimization problems. The paper also discusses the challenges in applying MOO to real-world production systems and presents case studies demonstrating the practical applications of these techniques.
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