Integrated and optimal allocation of human resources in normal and critical conditions using a new hybrid Metahuristic-fuzzy method

This study aimed to solve the problem of human resource allocation in an integrated and optimal way under normal and critical conditions using a new integrated metaheuristic-fuzzy method. The solution method has included a mathematical model of the allocation problem, a combination of the GWO metaheuristic algorithm, and the Sugeno fuzzy inference model. In this research, Sugeno fuzzy inference model has been used in the task rate adjustment layer to add the ability to self-regulating the parameters to the optimization algorithm. After the preparation of the newly developed algorithm, the problem of human resource allocation before and after the crisis and the time of the crisis has been solved with this solution algorithm through the data of previous prominent researches. Comparison of the results of this study with the results of the top 5 methods in previous studies (SGA, PRS, SRS, MIP, HM) based on three methods of evaluating the quality of solutions (GA-FSGS, MP-FSGS, GA-SGS) showed that the increase of Ω from 15000 It has improved the HM and SGA values ​​to 25,000 compared to previous studies in the B100 and B200 datasets. It was also found that the proposed method has better results and higher solution quality compared to the previous solution methods and the quality of their solutions.