Alp Akcay

Associate Professor, Mechanical and Industrial Engineering

Contact

a.akcay@northeastern.edu
360 Huntington Ave
Boston, MA 02115

Office

201 SN

Research Focus

Simulation-based optimization, digital twins, data analytics, predictive maintenance, semiconductor manufacturing and supply chains

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.

Education

PhD, Operations Management and Manufacturing, Carnegie Mellon University

Professional Affiliations

Institute for Operations Research and Management Sciences (INFORMS)
INFORMS QSR (Quality, Statistics, and Reliability) Society
I-SIM (INFORMS Simulation Society)

Research Overview

Simulation-based optimization, digital twins, data analytics, predictive maintenance, semiconductor manufacturing and supply chains

Department Research Areas

Selected Publications

Journal papers

Interpretable chip-quality classification with signal and feature selection in wire bonding (with C. Rosman* and K. Schelthoff), 2025, Flexible Services and Manufacturing Journal, in press, https://doi.org/10.1007/s10696-025-09621-w (Open Access).
Biomanufacturing harvest optimization with small data (with B. Wang*, W. Xie, T. Martagan, and van Ravenstein), 2024, Production and Operations Management, 33(12), https://doi.org/10.1177/10591478241270130 (Open Access).
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, 318(1), https://doi.org/10.1016/j.ejor.2024.04.024 (Open Access).
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, 267, 109060, https://doi.org/10.1016/j.ijpe.2023.109060 (Open Access).
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).
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).
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).
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).
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).
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).
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).
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).
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)
- 2025 EURO Award for the Best EJOR Paper
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).
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).
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).
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).
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).
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 ISE Magazine.

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.

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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).

Conference proceedings

Simulating Front-End Semiconductor Supply Chains to Assess Master Plans under Uncertainty: A Case Study (with A. Sieders, C. Rosman, and C. Drent), 2025, Proceedings of the Winter Simulation Conference (WSC 2025), accepted.
Shaping Tomorrow’s Factories: A Panel on Simulation-Driven Manufacturing (with C. Laroque, R. Rencher, G. Shao, R. Uzsoy, and N. Valkhoff), 2025, Proceedings of the Winter Simulation Conference (WSC 2025), accepted.
An Integrated Layout and Resource Planning Approach for Assembly Lines (with S. Al Habboush, Q-V. Dang, and I. Adan), 2025, Proceedings of the IFAC Conference on Manufacturing Modelling, Management and Control (IFAC MIM2025), accepted.
Leveraging Machine Signals For Device-Level Quality Detection and Automatic Root Cause Analysis In Semiconductor Wire Bonding (with K. Braakman*, D.M. Knotter, and I. Adan), 2024, Proceedings of the Winter Simulation Conference (WSC 2024), https://doi.org/10.1109/WSC63780.2024.10838790.
Aggregated Simulation Modeling To Assess Product-Specific Safety Stock Targets During Market Up- And Downswing: A Case Study (with C. Rosman*, E. Weijers*, K. Schelthoff, W. van Jaarsveld, and I. Adan), 2024, Proceedings of the Winter Simulation Conference (WSC 2024), https://doi.org/10.1109/WSC63780.2024.10838984.
Maintenance and operations of manufacturing digital twins (with S. Biller, B.P. Gan, C. Laroque, and G. Shao), 2023. Proceedings of the Winter Simulation Conference (WSC 2023), https://doi.org/10.1109/WSC60868.2023.10407831.
Simulation-based AGV management with a linear dispatching rule (with N. Singh*, J. Didden*, T. Martagan, and I. Adan), 2023, Proceedings of the Winter Simulation Conference (WSC 2023), https://doi.org/10.1109/WSC60868.2023.10408344.
On-demand and model-driven case building based on distributed data sources (with M. van der Pas*, R. Dijkman, I. Adan, and J. Walker), 2023, International Conference on Case-Based Reasoning (ICCBR 2023), Lecture Notes in Computer Science, 14141, https://doi.org/10.1007/978-3-031-40177-0_5.
Integrated planning of usage-based maintenance and load-sharing under resource dependence (with M. Dilaver* and G-J. van Houtum), 2023, Proceedings of the European Conference on Safety And Reliability (ESREL 2023), https://doi.org/10.3850/978-981-18-8071-1_P318-cd.
Autonomous scheduling in semiconductor back-end manufacturing (J. Adan*, J. Fowler, M.P. Albers*, and M. Geurtsen*), 2022, Proceedings of the Winter Simulation Conference (WSC 2022), https://doi.org/10.1109/WSC57314.2022.10015252.
Relation representation learning for special cargo ontology (with V. Reshadat et al.), 2021, Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI 2021), https://doi.org/10.1109/SSCI50451.2021.9660108.
Learning 2-opt Local Search from Heuristics as Expert Demonstrations (with P. da Costa*, Y. Zhang, U. Kaymak), 2021, Proceedings of the International Joint Conference on Neural Networks, https://doi.org/10.1109/IJCNN52387.2021.9533697.
Low-regret algorithms for strategic buyers with unknown valuations in repeated posted-price auctions (with J. Rhuggenaath*, P. da Costa*, Y. Zhang, and U. Kaymak), 2020, European Conference on Machine Learning and Principles (ECML 2020). Lecture Notes in Artificial Intelligence, 12458, https://doi.org/10.1007/978-3-030-67661-2_25.
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning (with P. da Costa*, J. Rhuggenaath*, Y. Zhang), 2020, Asian Conference on Machine Learning (ACML 2020) – Proceedings of Machine Learning Research, 129, http://proceedings.mlr.press/v129/costa20a/costa20a.pdf.
Dynamic pricing using Thompson Sampling with fuzzy events (with J. Rhuggenaath*, P. da Costa, Y. Zhang, U. Kaymak), 2020, Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020). Communications in Computer and Information Science, 1237, https://doi.org/10.1007/978-3-030-50146-4_48.
A PSO-based algorithm for reserve price optimization in online ad auctions (with J. Rhuggenaath*, Y. Zhang, and U. Kaymak), 2019. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2019), https://doi.org/10.1109/CEC.2019.8789915.
A heuristic policy for dynamic pricing and demand learning with limited price changes and censored demand (with J. Rhuggenaath*, P. da Costa*, Y. Zhang, U. Kaymak), 2019, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), https://doi.org/10.1109/SMC.2019.8914590.
Wafer-to-order allocation in semiconductor back-end production (with P. Deenen*, J. Adan*, J. Stokkermans, I. Adan), 2019, Proceedings of the Winter Simulation Conference (WSC 2019), https://doi.org/10.1109/WSC40007.2019.9004863.
Fuzzy logic based pricing combined with adaptive search for reserve price optimization in online ad auctions (with J. Rhuggenaath*, Y. Zhang, U. Kaymak), 2019, Proceedings of the IEEE International Conference on Fuzzy Systems, https://doi.org/10.1109/FUZZ-IEEE.2019.8858975.
Stochastic simulation model development for biopharmaceutical production process risk analysis and stability control (with B. Wang*, W. Xie, T. Martagan, C.G. Corlu), 2019, Proceedings of the Winter Simulation Conference (WSC 2019), https://doi.org/10.1109/WSC40007.2019.9004778.
Data-driven policy on feasibility determination for the train shunting problem (with P. da Costa*, J. Rhuggenaath*, Y. Zhang, W-J. Lee, U. Kaymak), 2019, European Conference on Machine Learning and Principles (ECML2019). Lecture Notes in Artificial Intelligence, 11908, https://doi.org/10.1007/978-3-030-46133-1_43.
Optimizing reserve prices for publishers in online ad auctions (with J. Rhuggenaath*, Y. Zhang, U. Kaymak), 2019, Proceedings of the IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr 2019), https://doi.org/10.1109/CIFEr.2019.8759123.
Learning fuzzy decision trees using integer programming (with J. Rhuggenaath*, Y. Zhang, U. Kaymak, S. Verwer), 2018, Proceedings of the IEEE International Conference on Fuzzy Systems, https://doi.org/10.1109/FUZZ-IEEE.2018.8491636.
Re-enactment simulation for buffer size optimization in semiconductor back-end production (with J. Adan*, S. Sneijders*, I. Adan), 2018, Proceedings of the Winter Simulation Conference (WSC 2018), https://doi.org/10.1109/WSC.2018.8632251.
A hybrid genetic algorithm for parallel machine scheduling at semiconductor back-end production (with J. Adan*, J. Stokkermans, R. van den Dobbelsteen), 2018, Proceedings of International Conference on Automated Planning and Scheduling (ICAPS 2018), 28(1), https://ojs.aaai.org/index.php/ICAPS/article/view/13913.
Risk assessment in pharmaceutical supply chains under unknown input-model parameters (with T. Martagan, C.G. Corlu), 2018, Proceedings of the Winter Simulation Conference (WSC 2018), https://doi.org/10.1109/WSC.2018.8632314.
Simulation-based production planning for engineer-to-order systems with random yield (with T. Martagan), 2017, Proceedings of the Winter Simulation Conference (WSC 2017), https://doi.org/10.1109/WSC.2017.8248045.
Stochastic simulation under input uncertainty for contract manufacturer selection in pharmaceutical industry (with T. Martagan) Proceedings of the Winter Simulation Conference (WSC 2016), https://doi.org/10.1109/WSC.2016.7822270.
A simulation-based support tool for data-driven decision making: Operational testing for dependence modeling (with B. Biller, C.G. Corlu, S. Tayur), 2014, Proceedings of the Winter Simulation Conference (WSC 2014), https://doi.org/10.1109/WSC.2014.7019950.
Quantifying input uncertainty in an assemble-to-order system simulation with correlated input variables of mixed types (with B. Biller), 2014, Proceedings of the Winter Simulation Conference (WSC 2014), https://doi.org/10.1109/WSC.2014.7020057.
Near optimality guarantees for data-driven newsvendor with temporally dependent demand: A Monte Carlo approach (with B. Biller and S. Tayur), 2013, Proceedings of the Winter Simulation Conference (WSC 2013), https://doi.org/10.1109/WSC.2013.6721636.
A simulation-based approach to capturing autocorrelated demand parameter uncertainty in inventory management (with B. Biller, and S. Tayur), 2012, Proceedings of the Winter Simulation Conference (WSC 2012), https://doi.org/10.1109/WSC.2012.6465035.

Book chapters

Real-time data-driven maintenance logistics: a public-private collaboration. (with W. van Jaarsveld et al.), 2024. Commit2Data – Big Data Analytics and Applications, Schloss Dagstuhl–Leibniz-Zentrum für Informatik, https://doi.org/10.4230/OASIcs.Commit2Data.5.
A digital platform for heterogeneous fleet management in manufacturing intralogistics (with N. Singh*, Q-V. Dang, I. Adan, and E.A. Thijssen*), 2024, Handbook on Digital Business Ecosystems in Manufacturing}, Edward Elgar Publishing, https://doi.org/10.4337/9781035301003.00030.
Knowledge Modeling and Incident Analysis for Special Cargo (with V. Reshadat et al.), 2021, Technologies and Applications for Big Data Value}, Springer, https://doi.org/10.1007/978-3-030-78307-5_23.
Success stories on real pilots (with R. Socorro et al.), 2018, The MANTIS Book: Cyber Physical System Based Proactive Collaborative Maintenance, River Publishers, https://www.riverpublishers.com/pdf/ebook/chapter/RP_9788793609846C7.pdf.
Business drivers of a Collaborative, Proactive Maintenance solution (with E. Jantunen et al.), 2018, The MANTIS Book: Cyber Physical System Based Proactive Collaborative Maintenance, River Publishers, https://www.riverpublishers.com/pdf/ebook/chapter/RP_9788793609846C2.pdf.
Stochastic Input Model Selection (with B. Biller), 2013, Encyclopedia of Operations Research and Management Science, 3rd edition, Springer, https://doi.org/10.1007/978-1-4419-1153-7_1182.

Faculty

Jul 23, 2025

Akcay and Martagan received 2025 EURO Award

MIE Associate Professors Alp Akcay and Tugce Martagan were awarded the 2025 EURO Award for the Best EJOR Paper. This award recognizes the most outstanding paper published in the European Journal of Operational Research (EJOR), one of the leading journals in the field of operations research and decision sciences.

Faculty

Dec 11, 2024

New Faculty Spotlight: Alp Akcay

Alp Akcay joins the mechanical and industrial engineering department in January 2025 as an associate professor.

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