Prof. Armand Baboli
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Academic Career Length
16Years (2011-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 September 2020 in Applied Soft Computing
Journal Article
MMMehdi Mahmoodjanloo
Mehdi MahmoodjanlooRTReza Tavakkoli-Moghaddam
Reza Tavakkoli-MoghaddamArmand BaboliABAli Bozorgi-Amiri
Ali Bozorgi-Amiri14 April 2011 in The International Journal of Advanced Manufacturing Technology
Journal Article
Armand BaboliJFJulien Fondrevelle
Julien Fondrevelle
RTReza Tavakkoli-Moghaddam
Reza Tavakkoli-MoghaddamAMAli Mehrabi
Ali Mehrabi
6 July 2021 in International Journal of Production Research
Journal Article
MMMehdi Mahmoodjanloo
Mehdi MahmoodjanlooRTReza Tavakkoli-Moghaddam
Reza Tavakkoli-MoghaddamArmand BaboliABAli Bozorgi-Amiri
Ali Bozorgi-AmiriThe recent trend of globalisation of the economy has been accelerated thanks to emerging new communication technologies. This forces some companies to be adapted to rapidly changing market requirements utilising a multi-factory production network. Job scheduling in such a distributed manufacturing system, is significantly complicated especially in the presence of dynamic events. Furthermore, production systems need to be flexible to timely react to the imposed changes. Hence, reconfigurable machine tools (RMTs) can be used as a resource for flexibility in manufacturing systems. This paper deals with a distributed job-shop rescheduling problem, in which the facilities benefit from reconfigurable machines. Firstly, the problem is mathematically formulated to minimise total weighted lateness in a static state. Then, the dynamic version is extent based on a designed conceptual framework of rescheduling module to update the current schedule. Since the problem is NP-hard, a self-adaptive hybrid equilibrium optimiser algorithm is proposed. The experiments show that the proposed EO algorithm is extremely efficient. Finally, a simulation-optimisation model is developed to evaluate the performance of the manufacturing system facing stochastic arriving jobs. The obtained results show that the production system can be very flexible relying on its distributed facilities and reconfigurable machines.
1 November 2020 in Computers & Operations Research
Journal Article
MMMehdi Mahmoodjanloo
Mehdi MahmoodjanlooRTReza Tavakkoli-Moghaddam
Reza Tavakkoli-MoghaddamArmand BaboliAJAtefeh Jamiri
Atefeh Jamiri
4 July 2023 in Journal of Manufacturing Systems
Journal Article
BEBehnam Einabadi
Behnam EinabadiMMMehdi Mahmoodjanloo
Mehdi MahmoodjanlooArmand BaboliEREva Rother
Eva Rother
22 April 2012 in The International Journal of Advanced Manufacturing Technology
Journal Article
About Me
Armand Baboli is a Full Professor in Industrial Engineering at INSA Lyon, France, and a member of the LIRIS laboratory (CNRS UMR 5205). His research focuses on the design and optimization of intelligent, robust, and adaptive production systems and supply chains, with a strong emphasis on Industry 4.0. In this context, he develops a new approach based on Cognitive AI-driven decision-making systems, integrating artificial intelligence, data science, optimization, and simulation, with applications in the automotive and aerospace industries.
He leads and has led several major long-term industrial research chairs, including with Volvo Group–Renault Trucks (2019–2027) on Smart Sales and Operations Planning and Execution (SS&OPE); with Fiat Powertrain Technologies (2015–2026) on the transformation of production systems towards Industry 4.0; and with Dassault Falcon Jet (USA) (2014–2026) on smart supply chains. He also contributes to large-scale European projects on AI-driven production planning and resilient manufacturing networks.
Professor Baboli has supervised more than 20 PhD students and authored over 130 scientific publications. He is the Head of the Executive Master in Industrial Engineering and the Smart Production Competency Center at INSA Lyon. He founded the Smart Factory platform to support research, innovation, and education in intelligent manufacturing systems. He has received several awards, including the National Scientific Excellence Award.