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Under the background of intelligent manufacturing, this paper aims to develop a model for person–job safe matching that optimizes safety with consideration of major equipment operator competency and task complexity. Safe matching cost is minimized in the developed model and is measured by the equipment downtime, production defect rate, and operator labor costs oriented by human factors. Human reliability is calculated with the goal of best value individual competency and best admit task complexity with a hierarchical structure. The 0-1 integer programming person– job matching model minimizes the human factor safety and wage costs and satisfies the requirements of the production order, budget and operator quantity requirement. An improved genetic algorithm is designed to solve the model. The computational results of the proposed model based on a case study for a large iron and steel company evidently demonstrated its effectiveness. A new integrated model provides more realistic matches for person–job assignment.

Exploring the Optimal Safety Person–job Matching Method of Major Equipment Based on Human Reliability