Leila Moslemi Naeni is a senior lecturer of Project Management at the School of Built Environment, Faculty of Design, Architecture and Building at UTS. After completing Master of Industrial Engineering at Sharif University of Technology in Iran, she worked in project management for engineering and construction projects. She spent 6 years in the engineering and construction industry.
Leila is completed her PhD in Computer Science at the University of Newcastle. In her PhD study, she developed sophisticated network theory-based algorithms and innovative mathematical approaches to analyse the 'big data' from a new perspective. This innovative research and its applications are published in a leading journal and research conferences.
Leila's research has won several awards for applied research in project management and computer science. She won the Project Management Research Award (PMAA) of 2016 from Australia Project Management Institute (AIPM) - NSW. Leila is an active member of Project Management Institute since 2012 and holds Project Management Professional (PMP) certificate from 2013.
Leila is also a regular reviewer for the top tier journals of
- International Journal of Project Management
- Journal of Construction Engineering and Management
- Project Management Journal
- Journal of Memetic Computing
Can supervise: YES
Leila Moslemi Naeni research in project management has two broad directions. The first is the project performance evaluation for complex projects that involve a high degree of uncertainty. Her second area of research focuses on improving project management practices using data analytics.
- Mohsen Salimi (Thesis: A Fuzzy Approach to Earned Value Management, 2016 - 2017)
- Dr Saeed Shalbafan (Thesis: Gaming, Simulation and Decision Making in Project Portfolio Management, 2013 - 2018)
- Jeff Scales (Thesis: Project scheduling with System Dynamics, a case of overtime induced fatigue, 2016 - 2020)
- Darryl Walker (Thesis: Outcomes-based decision support for investments in social infrastructure, 2017 - 2022)
- Nastaran Tavakoli (Thesis: A Multi-Objective Optimisation Model in PPP Project, 2019 - 2022)
- PhD research projects are available. Contact me if you are interested and have a research experience.
She is an experienced project management lecturer, whose teaching focuses on developing the skills that are needed to manage complex projects and deliver the results.
Yousefi, N, Sobhani, A, Naeni, LM & Currie, KR 2019, 'Using statistical control charts: To monitor duration-based performance of project', Journal of Modern Project Management, vol. 6, no. 3, pp. 88-103.View/Download from: UTS OPUS or Publisher's site
© 2019 Editora Mundos Sociais. All Rights Reserved. Monitoring of project performance is a crucial task of project managers that significantly affect the project success or failure. Earned Value Management (EVM) is a well-known tool to evaluate project performance and an effective technique for identifying delays and proposing appropriate corrective actions. The original EVM analysis is a monetary-based method and it can be misleading in the evaluation of the project schedule performance and estimation of the project duration. Earned Duration Management (EDM) is a more recent method that introduces metrics for the project schedule performance evaluation and improves EVM analysis. In this paper, we apply statistical control charts on EDM indices to better investigate the variations of project schedule performance. Control charts are decision support tools to detect the out of control performance. Usually, project performance measurements are auto-correlated and not following the normal distribution. Hence, in this paper, a two-step adjustment framework is proposed to make the control charts applicable to non-normal and auto-correlated measurements. The case study project illustrates how the new method can be implemented in practice. The numerical results conclude that employing a control chart method along with analyzing the actual values of EDM indices increases the capability of project management teams to detect cost and schedule problems on time.
Moslemi Naeni, L, Craig, H, Berretta, R & Moscato, P 2016, 'A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays', PLoS One, vol. 11, no. 8.View/Download from: UTS OPUS or Publisher's site
In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays.
Salehipour, A, Moslemi Naeni, L, Khanbabaei, R & Javaheri, A 2016, 'Lessons Learned from Applying the Individuals Control Charts to Monitoring Autocorrelated Project Performance Data', Journal of Construction Engineering and Management, vol. 142, no. 5.View/Download from: UTS OPUS or Publisher's site
The well-known earned value technique measures and evaluates project performance. It uncovers schedule and cost deviations from the baseline plan. However, it is not established to determine acceptable levels of deviations from the baseline. This study applies the Shewhart individuals control charts to overcome this limitation; the charts monitor trends in the project performance behavior and allow them to be detected before the project deviates much from the baseline plan. The study statistically monitors several well-known earned value indexes of a construction project, where the data are autocorrelated and nonnormally distributed. The investigated case showed that the individuals control charts enhance capability of the earned value technique. The authors concluded that implementing the developed tool together with the traditional tools noticeably improves the project controlling scheme and provides more information on project progress. The study extends a previous study in which only independent data were investigated.
Naeni, LM, Shadrokh, S & Salehipour, A 2014, 'A fuzzy approach for the earned value management (vol 29, pg 764, 2011)', INTERNATIONAL JOURNAL OF PROJECT MANAGEMENT, vol. 32, no. 4, pp. 709-716.View/Download from: Publisher's site
Naeni, LM, Shadrokh, S & Salehipour, A 2014, 'Erratum to 'A fuzzy approach for the earned value management' [Int. J. Proj. Manag. 29 (2011) 764-772]', International Journal of Project Management, vol. 32, no. 4, p. 717.View/Download from: Publisher's site
Aliverdi, R, Naeni, LM & Salehipour, A 2013, 'Monitoring project duration and cost in a construction project by applying statistical quality control charts', INTERNATIONAL JOURNAL OF PROJECT MANAGEMENT, vol. 31, no. 3, pp. 411-423.View/Download from: UTS OPUS or Publisher's site
Salehipour, A, Modarres, M & Naeni, LM 2013, 'An efficient hybrid meta-heuristic for aircraft landing problem', COMPUTERS & OPERATIONS RESEARCH, vol. 40, no. 1, pp. 207-213.View/Download from: UTS OPUS or Publisher's site
Naeni, LM & Salehipour, A 2011, 'Evaluating fuzzy earned value indices and estimates by applying alpha cuts', EXPERT SYSTEMS WITH APPLICATIONS, vol. 38, no. 7, pp. 8193-8198.View/Download from: UTS OPUS or Publisher's site
Naeni, LM, Shadrokh, S & Salehipour, A 2011, 'A fuzzy approach for the earned value management', INTERNATIONAL JOURNAL OF PROJECT MANAGEMENT, vol. 29, no. 6, pp. 764-772.View/Download from: UTS OPUS or Publisher's site
Salehipour A, Kazemipoor H & Moslemi Naeni, L 2011, 'Locating workstations in tandem automated guided vehicle systems', The International Journal of Advanced Manufacturing Technology.View/Download from: UTS OPUS or Publisher's site
This paper presents a new solution framework to locate the workstations in the tandem automated guided vehicle (AGV) systems. So far, the research has focused on minimizing the total flow or minimizing the total AGV transitions in each zone. In this paper, we focus on minimizing total cumulative flow among workstations. This objective allocates workstations to an AGV route such that total waiting time of workstations to be supplied by the AGV is minimized. We develop a property which simplifies the available mathematical formulation of the problem. We also develop a heuristic algorithm for the problem. Computational results show that our heuristic could yield very high-quality solutions and in many cases optimal solutions.
Naeni, LM & Salehipour, A 2019, 'A New Mathematical Model for the Traveling Repairman Problem', IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1384-1387.View/Download from: Publisher's site
© 2019 IEEE. We propose a new mixed-integer program for the traveling repairman problem (TRP). The model benefits from the position-based variables. We aim to utilize only available solvers for optimizing the model. We test the proposed model by solving 70 randomly generated instances, ranging from 10 to 50 vertices, from the literature by CPLEX, and comparing its solutions with those of available models from the literature. We show that our model delivers the largest number of best solutions and that in a shorter time.
Naeni, LM & Salehipour, A 2019, 'Investigating a Breast Cancer Gene Expression Data Using a Novel Clustering Approach', IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1038-1042.View/Download from: Publisher's site
© 2019 IEEE. Historically, breast cancer has been perceived as a disease with varying histological and clinical features. Breast cancer tumor classification is important in disease prognosis and prediction because different breast tumors respond differently to different treatments and have different survival rates. Gene expression profiling studies have increasingly been motivated in the past decades to develop a good classification of breast cancer in molecular subtypes, which can improve the standard clinical assessments by providing extra prognostic information. In this research, one of the most comprehensive breast cancer gene expression datasets is analyzed by applying a novel clustering approach to predict the breast cancer subtypes. The novel unsupervised clustering approach initially model the gene expression data as a network and employ a community detection method to identify network clusters. This method utilizes an efficient problem specific metaheuristic algorithm to optimize the modularity value and identify clusters of breast cancer samples with similar characteristics that presents different subtypes of breast cancer. To assess the significant of the newly defined breast cancer subtypes, we compared our findings with three breast cancer subtyping methods.
Moslemi Naeni, L & Salehipour, A 2017, 'An extension of fuzzy earned value management model for uncertain and complex projects', International Research Network on Organizing by Projects, Boston.View/Download from: UTS OPUS
Moslemi Naeni, L, Berretta, R & Moscato, P 2014, 'MA-Net: A reliable memetic algorithm for community detection by modularity optimization', Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1, Asia Pacific Symposium on Intelligent and Evolutionary Systems, Springer International Publishing, Singapore, pp. 311-323.View/Download from: UTS OPUS or Publisher's site
The information that can be transformed in knowledge from data in challenging real-world problems follows the accelerated rate of the advancement of technology in many different fields from biology to sociology. Complex networks are a useful representation of many problems in these domains One of the most important and challenging problems in network analysis lies in detecting community structures. This area of algorithmic research has attracted great attention due to its possible application in many fields. In this study we propose the MA-Net, memetic algorithm to detect communities in network by optimizing modularity value which is fast and reliable in the sense that it consistently produces sound solutions. Experiments using well-known real-world benchmark networks indicate that in comparison with other state-of-the-art algorithms, MA-Net has an outstanding performance on detecting communities.
Moslemi Naeni, L, de Vries, N, Reis, R, S Arefin, A, Berretta, R & Moscato, P 2014, 'Identifying Communities of Trust and Confidence in the Charity and Not-for-profit Sector: A Memetic Algorithm Approach', Big Data and Cloud Computing (BdCloud), 2014 IEEE Fourth International Conference on, IEEE International Conference on Big Data and Cloud Computing (BdCloud), IEEE, Sydney, Australia, pp. 500-507.View/Download from: UTS OPUS or Publisher's site
In this study we analyse complete networks
derived from field survey and market research through
proposing an efficient methodology based on proximity graphs
and clustering techniques enhanced with a new community
detection algorithm. The specific context is the charity and NotFor-Profit
sector in Australia and consumer behaviours within
this context. To investigate the performance of this methodology
we conduct experiments on the network extracted from a dataset
that contains responses of 1,550 individual Australians to 43
questions in a quantitative survey conducted on behalf of the
Australian Charities and Not-for-Profits Commission to study
the public trust and confidence in Australian charities. Here, we
generate the distance matrix by computing the Spearman
correlation coefficient as a similarity metric among individuals.
Then, several types of k-Nearest Neighbour (kNN) graphs were
calculated from the distance matrix and the new community
detection algorithm detected groups of consumers by optimizing
a quality function called “modularity”. Comparison of obtained
results with the results of the BGLL algorithm, a heuristic given
by the publicly available package Gephi and the MST-kNN
algorithm, a graph-based approach to compute clusters that has
several applications in bioinformatics and finance, reveals that
our methodology is effective in partitioning of complete graphs
and detecting communities. The combined results indicate that
behavioural models that investigate trust in charities may need
to be aware of intrinsic differences among subgroups as revealed
by our analysis.
- School of Electrical Engineering and Computer Science, The University of Newcastle, Newcastle
- Garvan Institute of Medical Research, Sydney
- School of Business Administration, Oakland University, USA
- School of Industrial Engineering, Rutgers University, USA
- Department of Manufacturing Engineering, Virginia State Univversity, USA
- School of Industrial Engineering, Sharif University of Technology