A Novel Hybrid Approach to Enhance Intelligence Integration in Small-Medium Enterprises

Subject Areas : Simulation Based Optimization

Hamidreza Seifi ¹ , Kaveh Mohammad Cyrus ² , Naser Shams Gharneh ³

1 - PhD student, Department of Industrial Engineering and Systems management, Amirkabir University of Technology, Tehran, Iran.
2 - Assistant Professor, Department of Industrial Engineering and Systems management, Amirkabir University of Technology, Tehran, Iran.
3 - Associate Professor, Department of Industrial Engineering and Systems management, Amirkabir University of Technology, Tehran, Iran.

Received: 2021-02-14 Accepted : 2021-11-04 Published : 2021-04-01

Keywords: Fuzzy Inference, Intelligence Integration, Enterprise Engineering, Human Resource Allocation Sugeno, Gray Wolf Optimization Algorithm,

Abstract :

This research aims to enhance intelligence integration in small-medium enterprises. The approach is included an integrated structural model and an optimal human resource allocation (HRA) using a novel intelligence method. To optimize the current organizational structure used enterprise engineering (EE) in IT and Business parts (Using ITIL and COBIT standards, BSC and KAP model, respectively), and statistics method like as regression test is used. Also, continuous HRA is simulated using HRA mathematical program, Gray Wolf Optimization Algorithm (GWO), and Sugeno Fuzzy Inference (SFI) model to add a self-regulating attribute to the GWO algorithm. Results showed that the EE was issued a new optimal and integrated structure in IT and business parts and the examination of the PMBOK approved it, too. Also, results of the novel self-regulating algorithm using data from previous researches and by the top five proposed methods in the researches (Includes: SGA, PRS, SRS, MIP, HM) based on three methods of evaluating the quality of solutions (GA-FSGS, MP-FSGS, GA-SGS) showed that increase of Ω from 15,000 to 25,000. Also, it showed that HM and SGA performed better than other previous cases even in the larger B100 and B200 datasets.

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