Dr Mehdi Rajabi Asadabadi

Mehdi Rajabi Asadabadi


Research School of Management

Research Fellow
Phone number
+61 2 612 57322
Room 1119B, Copland Bld (24)
Research areas

Decision Support Systems; Business Intelligence; Project Management.


Dr. Mehdi Rajabi Asadabadi is a Researcher at the Australian National University. As a researcher, Mehdi looks at expanding knowledge on project and operations management. He does this by introducing real-world problems to the literature and providing novel solutions to them. More particularly, Mehdi is interested to develop and apply innovative operational research tools and techniques in order to address complex real-world problems. Mehdi has worked on common problems in government projects spanning from requirement specification to project benefits management. He also looks at the role of data mining in enhancing decision quality in organisations. For example, Mehdi suggests that automated text mining techniques can be combined with traditional techniques, sometimes from other disciplines, to extract and utilise information and support effective decision making. Mehdi’s research has been published in a number of top-ranked academic journals including European Journal of Operational ResearchDecision Support SystemsKnowledge Based Systems, and Business and Industrial Marketing.

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Research publications

Asadabadi, M. R., Zwikael, O. (2020). Ambiguous Proposal Selection Problem in Large Scale Projects. Decision Support Systems. https://doi.org/10.1016/j.dss.2020.113359

Asadabadi, M. R., Saberi, M., Zwikael, O., & Chang, E. (2020). Ambiguous requirements: A semi-automated approach to identify and clarify ambiguity in large-scale projects. Computer & Industrial Engineering. https://doi.org/10.1016/j.cie.2020.106828

Asadabadi, M. R., Chang, E. Sharpe, K. (2020). Requirement ambiguity and fuzziness in large-scale projects: The problem and potential solutions.  Applied Soft Computing. https://doi.org/10.1016/j.asoc.2020.106148

Asadabadi, M. R., Zwikael, O. (2019). Integrating Risk into Estimations of Project Activities' Time and Cost: A Stratified Approach. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2019.11.018

Asadabadi, M. R., Chang, E. Zwikael, O. Saberi, M. Sharpe, K. (2019). Hidden Fuzzy Information: Requirement Specification in Large Scale Projects. Journal of Fuzzy Sets and Systems. https://doi.org/10.1016/j.fss.2019.06.017

Asadabadi, M. R., Sharpe, K. (2019). The ambiguity dilemma in government procurement processes. Business and Industrial Marketing. https://doi.org/10.1108/JBIM-05-2018-0157. 

Asadabadi, M. R (2018). The stratified multi criteria decision making method. Knowledge Based Systems. 162, 115-123. https://doi.org/10.1016/j.knosys.2018.07.002

Asadabadi, M. R., Saberi, M., & Chang, E. (2018). The concept of stratification and future applications. Applied Soft Computing, 66, 292-296. https://doi.org/10.1016/j.asoc.2018.02.035

Nawaz, F. M., Asadabadi, M. R., Janjua, NK., Hussain, O., & Chang, E. (2018), Saberi, M. Cloud Service Selection Using a Markov Chain and the Best-Worst Method. Knowledge Based Systems, 159, 120-131. https://doi.org/10.1016/j.knosys.2018.06.010

Asadabadi, M. R., Saberi, M., & Chang, E. (2018). Targets of unequal importance using the concept of stratification in a big data environment. International Journal of Fuzzy Systems. 20(4), 1373-1384. https://doi.org/10.1007/s40815-017-0430-y

Aboutorab, H., Saberi, M., Asadabadi, M. R., Hussain, O., & Chang, E. (2018). ZBWM: The Z-number extension of best worst method and its application for supplier development. Expert Systems with Applications, 107, 115-125. https://doi.org/10.1016/j.eswa.2018.04.015

Asadabadi, M. R., (2017). A customer-based supplier selection process that combines quality function deployment, the analytic network process and a Markov chain. European Journal of Operational Research. 263(3), 1049-1062. https://doi.org/10.1016/j.ejor.2017.06.006

Asadabadi, M. R. (2017). A developed slope order index (SOI) for scheduling of “m” jobs on one processor. Computational management science, 14 (2), 281-291. https://doi.org/10.1007/s10287-017-0276-7

Asadabadi, M. R. (2016). A Markovian-QFD approach in addressing changing customer needs. International Journal of Quality and Reliability Management. 33(8), 1062-1075. https://doi.org/10.1108/IJQRM-07-2014-0091

Asadabadi, M. R. (2015). A revision on EOQ/JIT indifference points. International Journal of Industrial Engineering Computations, 6(3), 305-314.2. https://doi.org/10.5267/j.ijiec.2015.4.001

Asadabadi, M. R. (2015). A hybrid QFD-based approach in addressing supplier selection problem in product improvement process. International Journal of Industrial Engineering Computations, 5(4), 543-560. https://doi.org/10.5267/j.ijiec.2014.7.005


Merikhi, E., Rajabi Asadabadi, M., & Zwikael, O. (2020). An Effective Risk Mitigation Plan: A Benefits-Oriented Model. In Academy of Management Proceedings (Vol. 2020, No. 1, p. 19106). Briarcliff Manor, NY 10510: Academy of Management.

Asadabadi, M. R. (2019). Crowdsourcing to Address Requirement Ambiguity in Large Scale Projects. The 7th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2019-Doctoral Consortium track). 

Saberi, M. Asadabadi, M. R. Saberi, Z. Chang., E (2018). A Customer Oriented Assortment Selection in the Big Data Environment. The International Conference on e-Business Engineering (ICEBE). 

Asadabadi, M. R. (2018). Integrating natural language processing and fuzzy logic to address contract ambiguity. INFORMS Business Analytics Conference (poster). 

Asadabadi, M. R., Saberi, M., & Chang, E. (2018). Application of crowdsourcing to resolve ambiguity in the procurement process. INFORMS International Conference. https://www.informs.org/Publications/International-Meeting-Proceedings

Asadabadi, M. R., Saberi, M., & Chang, E. (2017). Logistic informatic modelling using concept of stratification (CST). In Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on (pp. 1-7). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2017.8015510

Asadabadi, M. R., Saberi, M., & Chang, E. (2017, August). A fuzzy game-based framework to address ambiguities in performance-based contracting. In Proceedings of the International Conference on Web Intelligence (pp. 1214-1217). ACM. https://doi.org/10.1145/3106426.3110323

Asadabadi, M. R., (2017). Artificial Intelligence in Contracting. The International Joint Conference on Artificial Intelligence (IJCAI 2017)

Research grants and awards


2019, Research to Impact Innovation Fellowship Grant, supported by Canberra Innovation Network.


2017, Study Canberra Award, supported by ACT government, Awarded

2015, University International Postgraduate Award