Multi-Objective Optimization for Resource Allocation in Intelligent Manufacturing

Abstract

As intelligent manufacturing expands globally, efficient resource allocation strategies are critical for optimizing production efficiency, costs, and quality.Although multi-objective optimization algorithms can handle conflicting objectives, traditional approaches struggle with complex manufacturing systems.This research proposes an optimization model integrating an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Fishbone layout for intelligent manufacturing resource allocation.The Fishbone-based model provides efficient decision support, while the enhanced NSGA-II improves solution efficiency and quality.Flexsim simulation demonstrates the practical value of the proposed method in optimizing resource allocation.This work extends the application of multi-objective optimization in intelligent manufacturing and offers a novel tool for resource allocation optimization in the manufacturing industry.