Dr Hasan Turan

Hasan H. Turan is a Lecturer and the Research Lead at Capability Systems Centre, University of New South Wales (UNSW Canberra). Before joining UNSW Canberra, he worked as a post-doc research fellow at Qatar University, Mechanical and Industrial Engineering Department from 2015 to 2017. He obtained his Ph.D. and master’s degrees both in Industrial and Systems Engineering from Istanbul Technical University and North Carolina State University, respectively.

Research Activities

Dr. Turan's research interests revolve around the development and application of data-driven optimization algorithms and simulation models arising in different domains including service and maintenance logistics, defense applications (fleet management, workforce, and resource planning), energy capacity expansion, and telecommunications networks. He is currently focused on the integration of machine learning (e.g., reinforcement learning), artificial intelligence, and computational intelligence techniques (e.g., genetic algorithms) with simulation models (discrete event and system dynamics) to solve complex decision-making problems. 

He has directed and participated in several projects in the mentioned areas supported by the Department of Defence, Qatar National Research Fund, The Scientific and Technological Research Council of Turkey and Balassi Institute. 

Professional Activities

Dr. Turan was the guest editor for at the Annals of Operations Research journal to organize a special issue on the recent advances in simulation-based optimization, and he is an editorial board member of the Journal of Business Analytics. He was the convener of the 3rd and 4th IEEE Systems Modeling Conference, and he organized several special sessions devoted to simulation-based optimization in international conferences. He has also served as a Committee Member for many leading international conferences on modeling and simulation, information technology, and management science. 

Teaching Activities

Dr. Turan has taught a total of 15 different courses at different university levels (bachelor and master) including students from different backgrounds. He is interested in teaching courses on data science and decision analytics, computer simulation modeling, operations research (including linear, integer, dynamic, and stochastic dynamic programming), optimization methods (including heuristics and meta-heuristics). 

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I am actively looking for potential Ph.D. students who have good programming skills along with a prior understanding of optimization and simulation modeling. Students with excellent programming and communication skills will be highly appreciable.

Ph.D. scholarships ($35,000 per year) are available for high-achieving students (with H1/High Distinction in UG and/or Masters by Research) in engineering, mathematics, or relevant sciences under my supervision. If you are interested, contact me at h.turan@adfa.edu.au 

Future Ph.D. & Master by Research Students

Students who have good programming skills, along with a prior understanding of optimization and simulation modeling are encouraged to apply. Several types of scholarships are available; however, admission and scholarship applications are highly competitive. A self-assessment tool is available to get a general feel for your competitiveness. You need to meet the English language requirements. If you are eligible and interested in working on the listed Ph.D. projects below (or closely related) topics or keywords, please send me your CV, academic transcripts, the result of the self-assessment, and a research proposal (less than 10 pages). 

Your research proposal should highlight:

• Research Motivation,
• Research problems,
• Research Objectives and Questions
• Brief Review of the most relevant Literature,
• Proposed Methodology and
• Expected Outcome.

For more information, please refer to the following links on how and when to apply for a research degree. NB: You can seek my advice/help during the construction of your Research Proposal.

Possible Future Ph.D. Projects:

• Simulation-optimization algorithms and applications
• Integration of AI and machine learning techniques (e.g., deep learning) to simulation-optimization algorithms
• Logistics & Supply Chain Management (focus: spare part and maintenance logistics)
• Multi-model simulation techniques (e.g., agent-based + systems dynamics) and their applications