Alp Akcay

About

Alp Akcay is an Associate Professor of Industrial Engineering at Northeastern University in Boston, USA. Prof. Akcay uses techniques from stochastic operations research, machine learning, and simulation to design and control smart manufacturing systems and supply chains. Prior to joining Northeastern, he was an Associate Professor at Eindhoven University of Technology where he has closely collaborated with semiconductor companies such as NXP, Nexperia, and ASML. His research resulted in data-driven production planning algorithms for semiconductor wafer fabs, predictive maintenance models for lithography systems, and obsolescence management tools for the electronics components of capital goods. His research has been published in journals such as Operations Research, IISE Transactions, Manufacturing and Service Operations Management, Production and Operations Management, European Journal of Operations Research, and IEEE Transactions on Semiconductor Manufacturing. Prof. Akcay is currently an Associate Editor of the Journal of Simulation and coordinator of the Manufacturing & Industry 4.0 track at the Winter Simulation Conference.

Selected Publications

1. Biomanufacturing harvest optimization with small data (with B. Wang*, W. Xie, T. Martagan, and van Ravenstein), 2024, Production and Operations Management, in press, https://doi.org/10.1177/10591478241270130 (Open Access).

2. Spare parts recommendation for corrective maintenance of capital goods considering demand dependency (with I. Dursun, A. Grishina, and G-J. van Houtum), 2024, European Journal of Operational Research, https://doi.org/10.1016/j.ejor.2024.04.024 (Open Access).

3. How good must failure predictions be to make local spare parts stock superfluous? (with I. Dursun, and G-J. van Houtum), 2024, International Journal of Production Economics, https://doi.org/10.1016/j.ijpe.2023.109060 (Open Access).

4. Dispatching AGVs with battery constraints using deep reinforcement learning (with N. Singh*, Q-V. Dang, T. Martagan, and I. Adan), 2024, Computers & Industrial Engineering, 187, 109678, https://doi.org/10.1016/j.cie.2023.109678 (Open Access).

5. After-sales services during an asset’s lifetime: Collaborative planning of system upgrades (with F. Sloothaak, G-J. van Houtum, and M. van der Heijden), 2023, Service Science, 15(3), https://doi.org/10.1287/serv.2023.0318 (pdf).

6. Scheduling a real-world photolithography area with constraint programming (with P. Deenen* and W. Nuijten), 2023, IEEE Transactions on Semiconductor Manufacturing, 4(36), https://doi.org/10.1109/TSM.2023.3304517 (pdf).

7. Integrated maintenance and production scheduling for unrelated parallel machines with setup times (with M. Geurtsen, and J. Adan), 2023, Flexible Services and Manufacturing Journal, https://doi.org/10.1007/s10696-023-09511-z (Open Access).

8. Policies for the dynamic traveling maintainer problem with alerts (with P. da Costa* et al.), 2023, European Journal of Operational Research, 305(3), https://doi.org/10.1016/j.ejor.2022.06.044 (Open Access).

9. Integrated planning of asset-use and drydocking for a fleet of maritime assets (with M. Dilaver and G-J. van Houtum), 2023, International Journal of Production Economics, 256, 108720, https://doi.org/10.1016/j.ijpe.2022.108720 (Open Access).

10. SCRE: Special cargo relation extraction using representation learning (with V. Reshadat, K. Zervanou, Y. Zhang, and E. de Jong), 2023,  Neural Computing and Applications, 35, https://doi.org/10.1007/s00521-023-08704-9 (Open Access).  

11. A data-driven aggregate modeling approach for predicting cycle times and WIP levels (with P. Deenen*, J. Middlehuis*, and I. Adan), 2023,  Flexible Services and Manufacturing Journal, https://doi.org/10.1007/s10696-023-09501-1 (Open Access).  

12. A metaheuristic for AGV Scheduling with Battery Constraints (with N. Singh*, Q-V. Dang, I. Adan, T. Martagan), 2022, European Journal of Operational Research, 298(3), https://doi.org/10.1016/j.ejor.2021.08.008 (Open Access) 

13. An alert-assisted inspection policy for a production process with imperfect condition signals, 2022, European Journal of Operational Research, 298(2), https://doi.org/10.1016/j.ejor.2021.05.051 (Open Access).

14. Data pooling for multiple single-component systems under population heterogeneity (with I. Dursun* and G-J. van Houtum), 2022, International Journal of Production Economics, 250, 108665, https://doi.org/10.1016/j.ijpe.2022.108665 (Open Access).

15. Age-based maintenance under population heterogeneity: optimal exploration and exploitation (with I. Dursun* and G-J. van Houtum), 2022, European Journal of Operational Research, 301(3), https://doi.org/10.1016/j.ejor.2021.11.038 (Open Access).

16. Setting reserve prices in second-price auctions with unobserved bids (with J. Rhuggenaath, Y. Zhang, and U. Kaymak), 2022, INFORMS Journal on Computing, 34(6), https://doi.org/10.1287/ijoc.2022.1199 (pdf). 

17. Maximizing revenue for publishers using header bidding and ad exchange auctions (with J. Rhuggenaath et al.), 2021, Operations Research Letters, 49(2), https://doi.org/10.1016/j.orl.2021.01.008 (Open Access). 

18. Optimal production decisions in biopharmaceutical fill and finish operations (with T. Martagan, M. Koek*, and I. Adan), 2021, IISE Transactions, 2021, 53(2), https://doi.org/10.1080/24725854.2020.1770902 (Open Access). 

• Featured article in the Industrial Engineer Magazine.

19. Machine tools with hidden defects: Optimal usage for maximum lifetime value (with E. Topan and G-J. van Houtum), 2021, IISE Transactions, 53(1), https://doi.org/10.1080/24725854.2020.1739786 (Open Access). 

• Best Application Paper in the 2021 IISE Transactions Focus Issues on Data Science, Quality, and Reliability – Honorable Mention.

20. Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning (with P. da Costa et al.), 2021, Springer Nature Computer Science, 2: 388, https://doi.org/10.1007/s42979-021-00779-2 (Open Access). 

21. Stochastic simulation under input uncertainty: A review (with C.G. Corlu and W. Xie), 2020, Operations Research Perspectives, 7, 100162, pages 1-16, https://doi.org/10.1016/j.orp.2020.100162 (Open Access).

22. Remaining useful lifetime prediction via deep domain adaptation (with P. da Costa, Y. Zhang, and U. Kaymak), 2020, Reliability Engineering and System Safety, 195, 106682, https://doi.org/10.1016/j.ress.2019.106682 (pdf). 

23. Optimizing class-constrained wafer-to-order allocation in semiconductor back-end production (with P. Deenen and J. Adan), 2020, Journal of Manufacturing Systems, 57, https://doi.org/10.1016/j.jmsy.2020.07.022 (pdf).

24. A group decision-making approach for risk-based selection of pharmaceutical product shipment lanes (with S. Faghih Roohi, Y. Zhang, and E. De Jong), 2020, International Journal of Production Economics, 229, 107774, https://doi.org/10.1016/j.ijpe.2020.107774 (pdf). 

25. Attention long short-term memory network for remaining useful lifetime predictions of turbofan engine degradation (with P. da Costa, Y. Zhang, and U. Kaymak), 2019, International Journal of Prognostics and Health Management (Special Issue on Deep Learning and Emerging Analytics), 10 (034), https://doi.org/10.36001/ijphm.2019.v10i4.2623 (Open Access).

26. Optimal display-ad allocation with guaranteed contracts and supply-side platforms (with J. Rhuggenaath*, Y. Zhang, and U. Kaymak), 2019, Computers & Industrial Engineering, 137, 106071, https://doi.org/10.1016/j.cie.2019.106071 (pdf). 

27. The benefits of state aggregation with extreme-point weighting for assemble-to-order systems (with  E. Nadar, A. Scheller-Wolf, and M. Akan), 2018, Operations Research, 66(4), pages 1040–1057, https://doi.org/10.1287/opre.2017.1710 (pdf).

28. Input uncertainty in stochastic simulations in the presence of discrete input variables (with B. Biller), 2018, Journal of Simulation, 12(4), pages 295–306, https://doi.org/10.1057/s41273-017-0051-3 (pdf). 

29. Simulation of inventory systems with unknown input models: A data-driven approach (with C.G. Corlu), 2017, International Journal of Production Research, 55(19), pp. 5826–5840, https://doi.org/10.1080/00207543.2017.1343503 (pdf). 

    • Finalist in INFORMS Minority Issues Forum Best Paper Competition (2017).

30. Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework (with G. Ertek and G. Buyukozkan), 2012, Expert Systems with Applications, 39(9), https://doi.org/10.1016/j.eswa.2012.01.059 (pdf). 

31. Improved inventory targets in the presence of historical demand data (with B. Biller and S. Tayur), 2011, Manufacturing & Service Operations Management, 13(3), https://doi.org/10.1287/msom.1100.0320 (pdf). 

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