Publications

A machine learning approach to solve the E-commerce box-sizing problem

Kandula Shanthan, Roy Debjit, Akartunali Kerem

Production and Operations Management Vol 34, pp. 1326-1345 (2025)

https://doi.org/10.1177/10591478241282249

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)

https://doi.org/10.1287/ijoc.2016.0712

Lagrangian-based heuristics for production planning with perishable products, scarce resources, and sequence-dependent setup times

Soler Willy A Oliveira, Santos Maristela O, Akartunalı Kerem

Journal of Heuristics Vol 31 (2025)

https://doi.org/10.1007/s10732-024-09539-w

Compact uncertainty sets for robust optimization based on bootstrapped Dirichlet process mixture model

Neofytou Andreas, Liu Bin, Akartunali Kerem

Optimization, pp. 1-44 (2025)

https://doi.org/10.1080/02331934.2025.2451812

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 (2025) (2025)

https://doi.org/10.1007/978-981-99-5491-9_7

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)

https://doi.org/10.1080/00207543.2023.2244844

More publications

Teaching

Classes currently taught include:

- Data Analytics in Practice

- Optimization for Analytics

- Programme and Project Management

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 and data analytics, 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.

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

Speaker

20/6/2023

Erasmus School of Economics, Erasmus University Rotterdam

Visiting researcher

19/6/2023

Production Planning under Uncertainty: Two Applications

Speaker

15/6/2023

Faculty of Economics and Business, KU Leuven

Visiting researcher

12/6/2023

Özyeğin University

Visiting researcher

29/5/2023

Ecole Nationale Supérieure des Mines, 13541 Gardanne

Visiting researcher

3/5/2023

More professional activities

Projects

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

SF6 Escape Prediction for NGET HV Switchgear

Stephen, Bruce (Principal Investigator) Akartunali, Kerem (Co-investigator) Brown, Blair David (Co-investigator) McArthur, Stephen (Co-investigator) Riccardi, Annalisa (Co-investigator) Stewart, Brian (Co-investigator)

01-Jan-2024 - 01-Jan-2025

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

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