Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Resources Allocation in Higher Education based on System Dynamics and Genetic Algorithms

Print
PDF
International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 77 - Number 10
Year of Publication: 2013
Authors:
Sherif E. Hussein
Mahmoud Abo El-Nasr
10.5120/13434-1136

Sherif E Hussein and Mahmoud Abo El-Nasr. Article: Resources Allocation in Higher Education based on System Dynamics and Genetic Algorithms. International Journal of Computer Applications 77(10):40-48, September 2013. Full text available. BibTeX

@article{key:article,
	author = {Sherif E. Hussein and Mahmoud Abo El-Nasr},
	title = {Article: Resources Allocation in Higher Education based on System Dynamics and Genetic Algorithms},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {77},
	number = {10},
	pages = {40-48},
	month = {September},
	note = {Full text available}
}

Abstract

Market economy simulation is used to understand economic phenomena and to analyze social systems. Simulation is also used as a method for conducting virtual experiments or to test hypotheses in the real market. System Dynamics simulation was used here in order to understand the relationships between different design factors that emerged in the behavior of the education quality model. Education quality control is considered a difficult task, as few policy-makers have adequate tools to aid their understanding of how various policy formulations affect this complex, socio-technical system. Thus, the model of each factor was kept simple and complexity arose from the interaction between those factors. The research also compared between normal quality management for budget distribution and optimized budget distribution and their effect on quality.

References

  • Atilgan, C. , & McCullen, P. (2011). Improving supply chain performance through auditing: a change management perspective. Supply Chain Management: An International Journal, 16, 11 – 19.
  • Harris, J. W. , & Baggett, J. M. (1992). Quality Quest in the Academic Process, Samford University, Birmingham, AL, and GOAL/QPC, Methuen, MA.
  • Williams, P. (1993). Total quality management: some thoughts. Higher Education, 25, No. 3, 373-5.
  • Mehralizadeh, Y. , & Safaeemoghaddam, M. (2010). The applicability of quality management systems and models to higher education: A new perspective. The TQM Journal, 22, 175 – 187.
  • Tribus, M. (1986). TQM in education: the theory and how to put it to work, in Quality Goes to School. Readings on Quality Management in Education, American Association of School, 61, No. 5, 404-6.
  • Ardi, R. , Hidayatno, A. ,& Zagloel, T. (2012). Investigating relationships among quality dimensions in higher education. Quality Assurance in Education , 20, 408 – 428.
  • Schmidt, T. (2009). Strategic Project Management Made Simple: Practical Tools for Leaders and Teams, Wiley.
  • Abukari, A. , & Corner, T. (2010). Delivering higher education to meet local needs in a developing context: the quality dilemmas?. Quality Assurance in Education, 18, 191 – 208.
  • Bai, C. , Sarkis, J. , Wei, X. , & Koh, L. (2012). Evaluating ecological sustainable performance measures for supply chain management. Supply Chain Management: An International Journal, 17, 78 – 92.
  • Rodney, W. T. , Defee, C. C. , Randall, W. S. , & Williams, B. (2011). Assessing the managerial relevance of contemporary supply chain management research. International Journal of Physical Distribution & Logistics Management, 41, 655 – 667.
  • Gazizova, A. (2012). From Turkey to Russia with love: a comparative study of higher education policy strategies in light of ongoing reforms. European Journal of Higher Education, 2, Issue 2-3, 198-204.
  • Mayo, D. D. , & Wichman, K. E. (2003). Tutorial on business and market modeling to aid strategic decision making: system dynamics in perspective and selecting appropriate analysis approaches, Proceedings of the 2003 Winter Simulation Conference, 2, Dec. 7-10, pp. 1569 – 1577.
  • Forrester, J. W. (ed) (2004). MIT System Dynamics Group Literature Collection DVD, System Dynamics Society, Albany, NY, USA.
  • An, L. , Jeng, J. J. , Ettl, M. , & Chung, J. Y. (2004). A system dynamics framework for senseand- respond systems, IEEE International Conference on E-Commerce Technology for Dynamic E-Business, 6 – 13.
  • Wilensky, U. (1999). NetLogo. http://ccl. northwestern. edu/netlogo/. Center for Connected.
  • Berkel, S. V. , Turi, D. , Pruteanu, A. , & Dulman, S. (2012). Automatic discovery of algorithms for multi-agent systems. The Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, Philadelphia, Pennsylvania, USA. July 7-11, 337-344.
  • Sherwood, D. (2002). Seeing the Forest for the Trees: A Manager's Guide to Applying Systems Thinking, Nicholas Brealey.
  • Arab Organization for Quality Assurance in Education. (n. d. ). Retrieved from http://www. arqaane. org
  • Kennedy, M. (2009). A Review of System Dynamics Models of Educational Policy Issues. The 2009 International Conference of the System Dynamics Society. Albuquerque, NM.
  • Hussein, S. (2010). Education Quality Control Based on System Dynamics and Evolutionary Computation, Modeling Simulation and Optimization - Focus on Applications, Shkelzen Cakaj (Ed. ), InTech, DOI: 10. 5772/8963.
  • Floreano, D. (2008). Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, The MIT Press.
  • Barski, T. (2006). The Assurance of Quality of Education in Context the Higher Education Reforming Process (Bologna Process), Microwave & Telecommunication Technology, 16th International Crimean Conference, 1, 65 – 67.
  • Coate L. E. (1991). Implementing total quality management in a university setting. Total Quality Management in Higher Education, New-Directions-for- Institutional- Research, 71, Autumn, 27-38.
  • Kasperska, E. , Mateja-Losa, E. , & Slota, D. (2006). Comparison of Simulation and Optimization Possibilities for Languages: DYNAMO and COSMIC & COSMOS – on a Base of the Chosen Models, Springer Berlin / Heidelberg.
  • Michael, R. K. , & Sower, V. E. (1997). A comprehensive model for implementing total quality management in higher education. Benchmarking for Quality Management & Technology, 4, No. 2, 104-120.
  • Motwani, J. , & Kumar, A. (1997). The need for implementing total quality management in education. International Journal of Educational Management, 11, No. 3, 131–135.
  • Producing Indicators of Institutional Quality in Ontario Universities and Colleges: Options for Producing, Managing and Displaying Comparative Data (Educational Policy Institute, July 2008).
  • Pidd, M. (1994). An introduction to computer simulation, Proceedings of the 1994 Winter Simulation Conference, 7 – 14, Orlando, Florida, United States.
  • Robert, G. S. (2007). Verification and validation of simulation models. Winter Simulation Conference 2007, 124-137.
  • Shannon, R. E. (1998). Introduction to the art and science of simulation, Proceedings of the 1998 Winter Simulation Conference, 1, 7-14, USA.
  • Taylor, W. A. , & Hill, F. M. (1992). Implementing TQM in higher education. International Journal of Educational Management, 5, No. 5, 4-9.
  • The Arab Administrative Development Organization. (30 Nov. 2012). Retrieved from http://www. arado. org. eg
  • Tian, H. (2008). A New Resource Management and Scheduling Model in Grid Computing Based on a Hybrid Genetic Algorithm. International Colloquium on Computing, Communication, Control, and Management, 113-117.
  • Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.
  • Zeng, M (2012). Based on NetLogo Simulation for Credit Risk Management. Advances in Computer Science and Engineering,141, 395-401.