Dr. Amir Ghasemi

Dr. Amir Ghasemi

  • Karlsruhe Institute of Technology
    Institute AIFB
    KIT-Campus South
    Kaiserstr. 89
    D-76133 Karlsruhe

Bio

Dr. Amir Ghasemi ist wissenschaftlicher Mitarbeiter am Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie. Seine Forschung konzentriert sich auf den Entwurf und die Implementierung von intelligenten Agenten, die den Menschen bei der Entscheidungsfindung durch Simulation, Optimierung und maschinelles Lernen unterstützen und/oder ersetzen. Dr. Ghasemis Arbeit erstreckt sich über verschiedene Anwendungsbereiche, darunter Fertigungs- und Lieferkettensysteme. Bevor er ans AIFB kam, war er Senior Data Scientist und Data Product Owner bei AkzoNobel am Hauptsitz in Amsterdam, Niederlande. Außerdem war er als wissenschaftlicher Mitarbeiter am CONFIRM Smart Manufacturing Centre in Irland tätig. Dr. Ghasemi promovierte 2021 in Wirtschaftsingenieurwesen an der Universität von Limerick. Er hat relevante Artikel in Fachzeitschriften wie dem Journal of Manufacturing Systems, Journal of Industrial Information Integration, Applied Soft Computing und Computers & Operations Research veröffentlicht. Dr. Ghasemi ist seit 2018 ein aktives Mitglied der Winter Simulation Conference.

Forschungsinteressen

• Data-driven Decision Making • Simulation Optimization • Machine Learning • Smart Manufacturing Systems • Supply Chain Digitalization

Online-Präsenz

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Publikationen


2024
A machine learning-based simulation metamodeling method for dynamic scheduling in smart manufacturing systems
Nejati, E.; Ghaedy-Heidary, E.; Ghasemi, A.; Torabi, S. A.
2024. Computers & Industrial Engineering, 196, 110507. doi:10.1016/j.cie.2024.110507
Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap
Ghasemi, A.; Farajzadeh, F.; Heavey, C.; Fowler, J.; Papadopoulos, C. T.
2024. Journal of Industrial Information Integration, 39, Article no: 100599. doi:10.1016/j.jii.2024.100599
A simulation optimization framework to solve Stochastic Flexible Job-Shop Scheduling Problems—Case: Semiconductor manufacturing
Ghaedy-Heidary, E.; Nejati, E.; Ghasemi, A.; Torabi, S. A.
2024. Computers & Operations Research, 163, Article no: 106508. doi:10.1016/j.cor.2023.106508
2023
Deep Learning Enabling Digital Twin Applications in Production Scheduling: Case of Flexible Job Shop Manufacturing Environment
Ghasemi, A.; Yeganeh, Y. T.; Matta, A.; Kabak, K. E.; Heavey, C.
2023. Proceedings of the Winter Simulation Conference (WSC 2023), 2148–2159, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/wsc60868.2023.10407811
Digital Twins for Operations and Supply Chain Management from Data Engineering Point of View
Ghasemi, A.; Azzouz, R.; Heavey, C.
2023. SSRN Electronic Journal. doi:10.2139/ssrn.4436575
Optimizing Portfolio Selection Strategy for Purchasing and Supply in Healthcare Considering Circular Economy Principles - Case: Isolation Gowns
Ghasemi, A.; DiVito, L.; Ingen-Housz, Z.; Torabi, S. A.
2023. SSRN Electronic Journal. doi:10.2139/ssrn.4320927
2022
Demonstration of the Feasibility of Real Time Application of Machine Learning to Production Scheduling
Ghasemi, A.; Kabak, K. E.; Heavey, C.
2022. 2022 Winter Simulation Conference (WSC), Singapore, 11-14 December 2022, 3406–3417, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/WSC57314.2022.10015436
2021
An Evaluation of Strategies for Job Mix Selection in Job Shop Production Environments - Case: A Photolithography Workstation
Ghasemi, A.; Heavey, C.
2021. Winter Simulation Conference (WSC 2021). Ed.: S. Kim, 12 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/WSC52266.2021.9715478
Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling Problems
Ghasemi, A.; Ashoori, A.; Heavey, C.
2021. Applied Soft Computing, 106, Article no: 107309. doi:10.1016/j.asoc.2021.107309
2020
Optimizing capacity allocation in semiconductor manufacturing photolithography area – Case study: Robert Bosch
Ghasemi, A.; Azzouz, R.; Laipple, G.; Kabak, K. E.; Heavey, C.
2020. Journal of Manufacturing Systems, 54, 123–137. doi:10.1016/j.jmsy.2019.11.012
2018
Implementing a new genetic algorithm to solve the capacity allocation problem in the photolithography area
Ghasemi, A.; Heavey, C.; Kabak, K. E.
2018. Proceedings of the Winter Simulation Conference (WSC 2018), 3696–3707, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/WSC.2018.8632204
A review of simulation-optimization methods with applications to semiconductor operational problems
Ghasemi, A.; Heavey, C.; Laipple, G.
2018. Proceedings of the Winter Simulation Conference (WSC 2018), 3672–3683, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/WSC.2018.8632486
A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system
Beheshtinia, M. A.; Ghasemi, A.
2018. Engineering Optimization, 50 (9), 1415–1433. doi:10.1080/0305215X.2017.1400546
Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company)
Beheshtinia, M. A.; Ghasemi, A.; Farokhnia, M.
2018. Journal of Modelling in Management, 13 (1), 27–49. doi:10.1108/JM2-10-2016-0094