A Novel Hybrid Approach to Enhance Intelligence Integration in Small-Medium Enterprises
الموضوعات :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.
الکلمات المفتاحية: Fuzzy Inference, Intelligence Integration, Enterprise Engineering, Human Resource Allocation Sugeno, Gray Wolf Optimization Algorithm,
ملخص المقالة :
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.
[1] Aspelund, A. and Moen O. (2004) Internationalization of small high- tech firms: the role of information technology, Journal of Eeromarketing, 13(2-3): 85-105.
[2] Abor, J. and Quartey P. (2010) Issues in SME development in Ghana and South Africa. International research journal of finance and economics, 39(6): 215-228.
[3] Arrau, G. P. and Medina F. M. (2014) Human resource management in small and medium-sized vineyards in Chile, Ciencia e investigación agraria: revista latinoamericana de ciencias de la agricultura, 41(2): 141-151.
[4] Aveiro, D., Pergl R. and Valenta M. (2015) Advances in Enterprise Engineering IX, Proceedings of the 5th Enterprise Engineering Working Conference (EEWC), Springer.
[5] Aviso, K., A. Mayol, M., Promentilla, J. Santos, R. Tan, Ubando A. T. and Yu K. (2018) Allocating human resources in organizations operating under crisis conditions: A fuzzy input-output optimization modeling framework, Resources, Conservation and Recycling, 128: 250-258.
[6] Aslinezhad, M., and Malekijavan, A., and Abbasi, P. (2020) Adaptive neuro-fuzzy modeling of a soft finger-like actuator for cyber-physical industrial systems. Journal of Supercomputing. https://doi.org/10.1007/s11227-020-03370-3.
[7] Brown, J. H. and Watts J. (1992) Enterprise engineering: building 21st century organizations, The Journal of Strategic Information Systems, 1(5): 243-249.
[8] Bacon, N. and Hoque K. (2005) HRM in the SME sector: valuable employees and coercive networks, The International Journal of Human Resource Management, 16(11): 1976-1999.
[9] Baumann, P., Fündeling, C.U., and Trautmann, N. (2015) The resource- constrained project scheduling problem with work-content constraints. In Handbook on Project Management and Scheduling, 1, 533–544.
[10] Ballesteros-Pérez, P., Ting Phu, F.T, and Mora-Melià, D. (2019) Human Resource Allocation to Multiple Projects Based on Members’ Expertise, Group Heterogeneity, and Social Cohesion. Journal of Construction Engineering and Management 145(2). https://doi.org/10.1061/(ASCE)CO. 1943-7862.0001612.
[11] Cesta, A., Oddi A. and Smith S. F. (2002) A constraint-based method for project scheduling with time windows, Journal of Heuristics, 8(1): 109-136.
[12] Chiu, H. N. and Tsai D. M. (2002) An efficient search procedure for the resource-constrained multi-project scheduling problem with discounted cash flows, Construction Management and Economics, 20(1): 55-66.
[13] Davis, K. R., Stam A. and Grzybowski R. A. (1992) Resource constrained project scheduling with multiple objectives: A decision support approach, Computers and operations research, 19(7): 657-669.
[14] Deetz, S. (1996) Crossroads—Describing differences in approaches to organization science: Rethinking Burrell and Morgan and their legacy, Organization science, 7(2): 191-207.
[15] Dietz, J. and Hoogervorst J. (2007) Enterprise Ontology and Enterprise Architecture–how to let them evolve into effective complementary notions, GEAO Journal of Enterprise Architecture, 2(1): 121-149.
[16] Dabirian, SH., Abbaspour, S., Khanzadi, M., and Ahmadi, M. (2019) Dynamic modelling of human resource allocation in construction projects. International Journal of Construction Management. https://doi.org/10.1080/ 15623599.2019.1616411.
[17] Elmaghraby, S. E. (1977) Activity networks: Project planning and control by network models, John Wiley and Sons.
[18] Elango, M., Nachiappan S. and Tiwari M. K. (2011) Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms, Expert Systems with Applications, 38(6): 6486- 6491.
[19] Franck, B. and Neumann K. (1997) Resource Constrained Project Scheduling with Time Windows: Structural Questions and Priority Rule Methods, Inst, für Wirtschaftstheorie und Operations-Research.
[20] Fündeling, C.U., and Trautmann, N. (2010) A priority-rule method for project scheduling with work-content constraints. European Journal of Operational Research, 203(3), 568–574.
[21] Hartmann, S. (1998) A competitive genetic algorithm for resource‐ constrained project scheduling. Naval Research Logistics, 45(7), 733– 750.
[22] Heilmann, R. (2001) Resource–constrained project scheduling: a heuristic for the multi–mode case, OR-Spektrum, 23(3): 335-357.
[23] Hoogervorst, J. A. (2018) Foundations of Enterprise Governance and Enterprise Engineering: Presenting the Employee-Centric Theory of Organization, Springer.
[24] Kolisch, R., Sprecher, A., and Drexl, A. (1995) Characterization and generation of a general class of resource-constrained project scheduling problems. Management Science, 41(10), 1693–1703.
[25] Kolisch, R., and Sprecher, A. (1997) PSPLIB-a project scheduling problem library: OR software-ORSEP operations research softwarexchange program. European Journal of Operational Research, 96(1), 205–216.
[26] Kolisch, R., Schwindt, C., and Sprecher, A. (1999) Benchmark instances for project scheduling problems. In Project scheduling, Springer, 197– 212.
[27] Kolisch, R., Meyer, K., Mohr, R., Schwindt, C., and Urmann, M. (2003) Ablaufplanung fur die Leitstrukturoptimierung in der Pharmaforschung. Zeitschrift fur Betriebswirtschaft, 73(8),825–848.
[28] Kolisch, R. and Hartmann S. (2006) Experimental investigation of heuristics for resource-constrained project scheduling: An update, European journal of operational research, 174(1): 23-37.
[29] Kosanke, K., Vernadat F. and Zelm M. (1999) CIMOSA: enterprise engineering and integration, Computers in industry, 40(2-3): 83-97.
[30] Nudtasomboon, N. and Randhawa S. U. (1997) Resource-constrained project scheduling with renewable and non-renewable resources and time-resource tradeoffs, Computers and Industrial Engineering, 32(1): 227-242.
[31] Nübel, H. (2001) The resource renting problem subject to temporal constraints, OR-Spektrum, 23(3): 359-381.
[32] Naber, A., amd Kolisch, R. (2014) MIP models for resource-constrained project scheduling with flexible resource profiles. European Journal of Operational Research, 239(2), 335–348.
[33] Mu, L., and Kwong C.K. (2018) A multi-objective optimization model of component selection in enterprise information system integration, Computers and Industrial Engineering, 115:278-289.
[34] Marimuthu, P., Perumal, V., and Vijayakumar, V. (2019) OAFPM: optimized ANFIS using frequent pattern mining for activity recognition. Journal of Supercomputing 75: 5347–5366.
[35] Op’t Land, M. and Dietz J.L. (2012) Benefits of enterprise ontology in governing complex enterprise transformations, Enterprise Engineering Working Conference, Springer.
[36] Pérez Arrau, G. and Medina F.M. (2014) Administración de recursos humanos en pequeñas y medianas viñas en Chile, Ciencia e investigación agraria, 41(2): 141-151.
[37] Parker, D. (2016) Enterprise and competition, Handbook of Regulatory Impact Assessment: 240.
[38] Pool, I. A., Poell, R. F., Berings M. G. and Ten Cate O. (2016) Motives and activities for continuing professional development: An exploration of their relationships by integrating literature and interview data, Nurse education today, 38: 22-28.
[39] Rom, W. O., Tukel O. I. and Muscatello J. R. (2002) MRP in a job shop environment using a resource constrained project scheduling model, Omega, 30(4): 275-286.
[40] Rohaninejad, M., Tavakkoli-Moghaddam R. and Vahedi-Nouri B. (2015) Redundancy resource allocation for reliable project scheduling: A game-theoretical approach, Procedia Computer Science, 64: 265-273.
[41] Sarkis, J., Presley, A. and Liles D. H. (1995) The management of technology within an enterprise engineering framework, Computers and Industrial Engineering, 28(3): 497-511.
[42] Sessions, R. (2007) A comparison of the top four enterprise-architecture methodologies, Houston: ObjectWatch Inc.
[43] Stephen, M. and Elvis K. (2011) Influence of working capital management on firms profitability: a case of SMEs in Kenya, International Business Management, 5(5): 279-286.
[44] Suga, T. and Iijima J. (2017) Formal Specification of DEMO Process Model and Its Submodel, Enterprise Engineering Working Conference, Springer.
[45] Tukel, O. I. and Wasti S. N. (2001) Analysis of supplier buyer relationships using resource constrained project scheduling strategies, European Journal of Operational Research, 129(2): 271-276.
[46] Tavares, L. V. (2002) A review of the contribution of operational research to project management, European Journal of Operational Research, 136(1): 1-18.
[47] Tritschler, M., Naber, A., amd Kolisch, R. (2017) A hybrid metaheuristic for resource-constrained project scheduling with flexible resource profiles. European Journal of Operational Research, 262(1), 262–273.
[48] Vanhoucke, M. (2006) Work continuity constraints in project scheduling, Journal of Construction Engineering and Management, 132(1): 14-25.
[49] Vicente, M., Gama N. and Silva M. M. (2013) The value of ITIL in enterprise architecture, 2013 17th IEEE International Enterprise Distributed Object Computing Conference, IEEE.
[50] Wu, N., Bacon N. and Hoque K. (2014) The adoption of high performance work practices in small businesses: the influence of markets, business characteristics and HR expertise, The International Journal of Human Resource Management, 25(8): 1149-1169.
[51] Xiao, L. (2020) Optimal Allocation Model of Enterprise Human Resources Based on Particle Swarm Optimization. International Conference on Computer Information and Big Data Applications (CIBDA), China. DOI: 10.1109/CIBDA50819.2020.00063.
[52] Yu, L., Zhang, C., Yang, H. and Miao L. (2018) Novel methods for resource allocation in humanitarian logistics considering human suffering, Computers and Industrial Engineering, 19: 1-20.
[53] Yousefi, M. and Yousefi, M. (2019) Human resource allocation in an emergency department: A meta-model-based simulation optimization. Kybernetes 49(3): 779-796.
[54] Yan, F. (2020) Gauss interference ant colony algorithm-based optimization of UAV mission planning. Journal of Supercomputing 76: 1170–1179.
