Professor Kerem Akartunali

Management Science


Personal statement

Kerem Akartunali is Professor in the Department of Management Science at Strathclyde Business School, where he joined as John Anderson Research Lecturer (JARL) in Optimization in 2010, and also a Visiting Professor at Institute of Mathematics and Computer Science (ICMC-USP) of University of São Paulo since 2017.  After completing his Ph.D. in 2007 at the University of Wisconsin-Madison with a specialization in optimization, he worked on airline planning and scheduling problems as a postdoctoral research fellow in the Department of Mathematics and Statistics at the University of Melbourne in collaboration with Constraint Technologies International, an Australian software company.  Kerem’s research expertise lies primarily in integer programming, robust and network optimization, and their applications, in particular in lot-sizing/production planning, transportation scheduling/planning, health applications such as radiation treatment planning and nurse rostering, and energy applications such as offshore windfarm installation.

Kerem's research has been funded by various bodies/organizations including Engineering and Physical Sciences Research Council (EPSRC), Scottish Funding Council (SFC), Capita and US Air Force Office of Scientific Research.  He has been an active member of Mathematical Optimization Society and INFORMS since 2003. He was the elected chair of the OR Group of Scotland (the regional branch of OR Society) betwen 2012-2016.  He is currently a Full Member of the EPSRC Peer Review College, and also a member of the OR Society Research Panel, and EURO Working Group in Lotsizing Board.  Kerem regularly reviews articles for many prestigious journals such as European Journal of Operational Research, IIE Transactions, INFORMS Journal on Computing, Mathematical Programming, Naval Research Logistics and Operations Research.

Kerem has worked with many organizations, including First Milk, Glasgow City Council, NHS, Scottish Power, Scottish Southern Energy and Technip, in a variety of projects ranging from short-term consultancy/knowledge exchange to long-term research partnerships.

In his spare time, Kerem likes to go hillwalking.

You can also visit his personal website or check his Google Scholar profile.

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Area of Expertise

My expertise spans the following areas and applications:

  • Operational Research,
  • Mathematical Optimization,
  • Integer and Combinatorial Optimization,
  • Robust Optimization,
  • Network Optimization,
  • Production Planning,
  • Transportation and Logistics Planning/Scheduling,
  • Radiation Treatment Planning Optimization,
  • Offshore Windfarm Installation Logistics
  • Social Networks

Prize And Awards

Best Reviewer Award 2020
Postdoctoral Research Fellowship (University of Melbourne, Australia)

More prizes and awards

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Local cuts and two-period convex hull closures for big-bucket lot-sizing problems
Akartunali Kerem, Fragkos Ioannis, Miller Andrew J, Wu Tao
INFORMS Journal on Computing Vol 28, pp. 766-780 (2016)
Modeling and exact solution approaches for the distance-based critical node and edge detection problems
Alozie Glory, Akartunali Kerem, Arulselvan Ashwin
Optimization Essentials Theory, Tools, and Applications (2024) (2024)
Special issue on collaborative production and maintenance in the environment of big data and industry 4.0
Liu Bin, Akartunali Kerem, Dauzère-Pérès Stéphane, Wu Shaomin
International Journal of Production Research Vol 61, pp. 8236-8237 (2023)
Linear approximations to improve lower bounds of a physician scheduling problem in emergency rooms
Devesse Valdemar Abrão P A, Akartunali Kerem, Arantes Márcio da S, Toledo Claudio F M
Journal of the Operational Research Society Vol 74, pp. 888-904 (2023)
Formulations and theoretical analysis of the one-dimensional multi-period cutting stock problem with setup cost
Silva Eduardo M, Melega Gislaine M, Akartunali Kerem, de Araujo Silvio A
European Journal of Operational Research Vol 304, pp. 443-460 (2023)
Dynamic lot sizing with stochastic demand timing
Akartunali Kerem, Dauzère-Pérès Stéphane
European Journal of Operational Research Vol 302, pp. 221-229 (2022)

More publications

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I have been extensively involved with teaching activities in various levels from undergraduate to Masters since I was a PhD student.  I was exposed to course development as early as the last year of my PhD studies, and I have received extensive teaching training during my PhD studies as well as during my employments.  I have used my research in a number of teaching activities, and have employed a range of state-of-the-art technologies (such as access grid and virtual workspace) in various courses.

I like to teach classes in the broad area of operational research/management science, in particular in optimization techniques and software, OR modelling and applying these theories to real world problems.

In light of my previous role as SBS Faculty Digital Education Director, I have been extensively involved in strategically planning, design and development of the next generation of online learning, including MOOCs, distance learning degrees and blended learning courses.

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Research Interests

My broad research area is operations research, with a focus in integer, network and robust optimization, and their applications in practice. Since my PhD thesis, I have been working on production planning and lot-sizing problems, as well as a number of optimization applications, in particular large-scale transportation problems (such as airline scheduling and vessel crew scheduling), and health applications (such as radiation treatment planning, nurse rostering and home care routing).

Professional Activities

Production Planning under Uncertainty: Robust and Stochastic Approaches
Erasmus School of Economics, Erasmus University Rotterdam
Visiting researcher
Production Planning under Uncertainty: Two Applications
Faculty of Economics and Business, KU Leuven
Visiting researcher
Özyeğin University
Visiting researcher
Ecole Nationale Supérieure des Mines, 13541 Gardanne
Visiting researcher

More professional activities


Multi-Item Production Planning: Theory, Computation and Practice
Akartunali, Kerem (Principal Investigator)
"Production planning problems arise naturally in the context of the manufacturing companies, where decisions are to be made regarding when to produce and what to produce while considering interactions between different time periods (for example through inventories) and between different items (for example through shared machine/labor capacities). Due to its high savings potential and being such a key component of the manufacturing decision making process, production planning has been an active area of research for more than 50 years. Moreover, in the current economic climate and global competitive environment, UK manufacturing industries face the crucial choice of turning these challenges into opportunities. Although a wide body of academic and practical research is devoted to the topic, realistic industrial problems remain very challenging even with the tremendous advancements in the computer technologies, and therefore novel approaches are needed to maximize the potential benefits attainable.

Mathematical models are instrumental to study production planning problems, where decisions and limitations of the real system can be represented by a mathematical system of variables and equations. Although a significant proportion of the previous research was devoted to mathematical studies and attained important results, almost all of these previous studies are focused on non-realistic and simplistic problems. The first part of this project aims to address this gap, developing novel mathematical theory for subproblems present in realistic problems.

Recent computational technologies such as highly parallel computer infrastructures and GPU computing present immense opportunities for tackling problems that could not be attempted before. Moreover, such technologies are becoming available to the general public including small companies much faster and cheaper than in the past. Therefore, the second part of this project aims to develop computational models (based on the theoretical results of the previous part) appropriate for such infrastructures and test these extensively for various production planning problems, including some obtained from the industry.

Finally, the project will address specific production planning issues of the food and drink industry, which is the largest manufacturing sector in the UK with over 500,000 people employed. Identified as a key growth sector in Scotland by the Government Economic Strategy, food and drink industry has been able to maintain the growth of exports even in the challenging economic climate, and has significant growth potential due to emerging markets. Production planning in this important industry has been neglected in the past, and this project will address this by working with an industrial partner in this area, developing customized tools for their challenges with the help of the theoretical and computational results of the previous parts."
01-Jan-2014 - 31-Jan-2015
Multi-Level Robust Optimization: Theory, Algorithms and Practice
Akartunali, Kerem (Principal Investigator) Barlow, Euan (Co-investigator)
20-Jan-2018 - 19-Jan-2021
Care & Equity - Logistics UAS Scotland Phase 3 (CAELUS 2) (Future Flight Challenge)
Fossati, Marco (Principal Investigator) Akartunali, Kerem (Co-investigator) Cashmore, Michael (Co-investigator) Irvine, James (Co-investigator) MacBryde, Jillian (Co-investigator) Maddock, Christie (Co-investigator) Patelli, Edoardo (Co-investigator) Tapinos, Efstathios (Co-investigator) Vasile, Massimiliano (Co-investigator)
01-Jan-2022 - 31-Jan-2024
Urban and rural UAS-enabled health-care over Scotland CAELUS (ex URANOS)
Fossati, Marco (Principal Investigator) Akartunali, Kerem (Co-investigator) Burt, Graeme (Co-investigator) Cashmore, Michael (Co-investigator) Maddock, Christie (Co-investigator) Patelli, Edoardo (Co-investigator) Tapinos, Efstathios (Co-investigator) Vasile, Massimiliano (Co-investigator)
01-Jan-2020 - 31-Jan-2022
Assessment of the current practices and local needs for developing smart antifouling strategies towards energy-efficient fishing boats in Turkey
Demirel, Yigit Kemal (Principal Investigator) Tezdogan, Tahsin (Co-investigator) Turan, Osman (Co-investigator) Akartunali, Kerem (Principal Investigator) Song, Soonseok (Researcher)
In Turkey, there are 13,000 artisanal fishing boats contributing to the income of at least 40,000 low-income fishermen and their families living in vulnerable communities. Typically, these fishing boats are of traditional design and inefficient in terms of fuel consumption. They may waste up to 44% of their fuel due to biofouling (organisms growing on boats), which also requires costly hull cleaning and antifouling procedures. Inadequate design and antifouling strategies aggravate the profitability and cause environmental problems such as increased Green-House Gas emissions and transportation of harmful non-indigenous species, while negatively affecting the expected growth in the fisheries sector as opposed to Turkey’s Vision 2023 targets for fisheries.
This project aims to investigate the current antifouling practices and identify the local needs for developing smart antifouling strategies towards energy-efficient fishing boats. This will be achieved by carrying out a joint inter-disciplinary pilot work with partners in Turkey with a focus on the technical feasibility, economic viability and environmental perspectives.
This will help the local fisheries to understand the importance of the optimum antifouling strategies, and hence to reduce unnecessary expenses and increase the profitability while reducing the environmental footprint. This project will establish partnerships for future GCRF calls.
01-Jan-2019 - 31-Jan-2020
Threshold Concepts across Knowledge Levels – Insights from How These Occur within Autistic People
Dorfler, Viktor (Principal Investigator) Harrington, Susan (Post Grad Student) Akartunali, Kerem (Research Co-investigator)
01-Jan-2018 - 31-Jan-2022

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Professor Kerem Akartunali
Management Science

Tel: 548 4542