Research on Multi-objective Optimal Allocation Model of Manufacturing Resources Based on Nonlinear Algorithm

In the manufacturing industry, the best method for allocating manufacturing resources was determined as key in enhancing productivity and cutting costs. However, the complexity and vagueness of a manufacturing system make it a huge difficulty for traditional linear optimization methods to surmount. Multi-objective evolutionary algorithms work nondominant among classical linear optimization approaches to solve complex and easily adjustable optimization problems by their nonlinear model-building algorithms: additionally, they exhibit highly enhanced solution count. Examples of the adaptive selection multi-objective evolutionary algorithm studied in this work include the aggregation density MOEA as applied to the optimal allocation of manufacturing resources. The experiments disclose that the algorithm is more efficient and better optimal in arriving at the multi-objective optimization problems of manufacturing resources that will provide new ideas and technical support to the intelligence of the manufacturing sector.