Ali Emrouznejad is a Professor and Chair in Business Analytics at Aston Business School, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining and big data. He holds an MSc in applied mathematics and received his PhD in operational research and systems from Warwick Business School, UK. Having got PhD in the area of DEA, in 1998, he has joined to the development team of Performance Indicators (PI) in Higher Education (HE) at HEFCE (Higher Education Funding Council for England). The PI in HE has been widely published and it is now an annual publication of HESA (Higher Education Statistical Agency). He has also collaborated on research project titled “assessing cost efficiencies in higher education” funded by the Department for Education and Skills (DfES). He worked together with WHO-Africa (World Health Organization) on several projects to improve efficiency and productivity of hospitals and healthcare systems in several African countries including: Angola, Zambia and Kenya. His most research projects are on “Analysis of efficiencies and productivity evolution in manufacturing industries with CO2 emissions” funded by Royal Academy of Engineering and “Measuring efficiency of small-scale sugarcane growers in Africa” funded by British Council.
Areas of Expertise (5)
Ferdowsi University of Mashhad: BSc, Applied Mathematics
Ferdowsi University of Mashhad: MSc, Mathematics
Coventry University: PGC, Learning and Teaching in Higher Education
Warwick Business School: PhD, Industrial and Business Studies 1998
- Operational Research Society : Member
- Performance Improvement Management Software : Co-Founder
Novel metaheuristic based on multiverse theory for optimization problems in emerging systemsApplied Intelligence
Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to different CPS systems. In this paper, we propose a new meta-heuristic algorithm based on Multiverse Theory, named MVA, that can solve NP-hard optimization problems such as non-linear and multi-level programming problems as well as applied optimization problems for CPS systems.
A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companiesJournal of Management Science and Engineering
Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation.
An Optimized Queue Management System to Improve Patient Flow in the Absence of Appointment SystemInternational Journal of Health Care Quality assurance
The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, Pakistan, in the absence of appointment systems; as well as, constructing a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.
The Impact of Smart Meter Installation on Attitude Change Towards Energy Consumption Behavior Among Northern Ireland HouseholdsJournal of Global Information Management (JGIM)
The continuous development of energy management systems, coupled with a growing population, and increasing energy consumption, highlights the necessity to develop a deep understanding of household energy consumption behavior and interventions that facilitate behavioral change. Using a data mining segmentation technique, 2,505 Northern Ireland households were segmented into four distinctive profiles, based on their energy consumption patterns, socio-demographic, and dwelling characteristics.
Bank Stock Performance During the COVID-19 Crisis: Does Efficiency Explain Why Islamic Banks Fared Relatively Better?SSRN
In this paper, we evaluate stock performance of Islamic banks relative to their conventional counterparts during the initial phase of the COVID-19 crisis (from December 31, 2019 to March 31, 2020). Using a total of 426 banks from 48 countries, we find that stock returns of Islamic banks were about 10% – 13 % higher than those of conventional banks, after controlling for a host of bank- and country-level variables. We provide an explanation for the superior stock performance of Islamic banks by assigning a special role for the levels of efficiency.