Dr.  Amir Ghasemi

Dr. Amir Ghasemi

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

Bio

Dr. Amir Ghasemi is a Research Fellow at the Institute of Applied Informatics and Formal Description Methods (AIFB), Karlsruhe Institute of Technology. His research focuses on designing and implementing intelligent agents to support and/or replace humans in decision-making using Simulation, Optimization, and Machine Learning techniques. Dr. Ghasemi's work spans various application domains, including manufacturing and supply chain systems. Prior to joining AIFB, he was a senior data scientist and data product owner at AkzoNobel headquarter in Amsterdam, Netherlands. He also served as a Research Associate at the CONFIRM Smart Manufacturing Centre in Ireland. Dr. Ghasemi earned his PhD in Industrial Engineering from the University of Limerick in 2021. He has published impactful articles in journals such as the Journal of Manufacturing Systems, Journal of Industrial Information Integration, Applied Soft Computing, and Computers & Operations Research. Dr. Ghasemi has been an active member of the Winter Simulation Conference since 2018.

Research Interests

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

Publications


2024
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
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
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