Laboratoire de Génie Informatique et d’Automatique de l’Artois

Abderrazzak SABRI

Ph.D. student
(Left the LGI2A in 2025)
Member of the research themes:

Revue Internationale avec Comité de Lecture

International journal with review committee
Abderrazzak SABRI -- Hamid ALLAOUI -- Omar SOUISSI
Reinforcement learning and stochastic dynamic programming for jointly scheduling jobs and preventive maintenance on a single machine to minimise earliness-tardiness
International Journal of Production Research, pp 1-15, Vol. 0, No. 0, Taylor & Francis, 03/2023

Conférence Internationale avec Comité de Lecture

International conference with review committee
Abderrazzak SABRI -- Hamid ALLAOUI -- Omar SOUISSI
Reinforcement Learning for the Just-In-Time Job- Shop Scheduling Problem
9th International Conference on Control, Decision and Information Technologies, CoDIT'23, Rome, Italy, July 03-06, 07/2023
International conference with review committee
Abderrazzak SABRI -- Hamid ALLAOUI -- Omar SOUISSI
Adaptive Large Neighborhood Search for the Just-In-Time Job-shop Scheduling Problem
2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022, pp 1-6, Lisbon, Portugal, 13-15 July 2022, 07/2022
International conference with review committee
Abderrazzak SABRI -- Hamid ALLAOUI -- Omar SOUISSI
Stochastic Dynamic Programming for Earliness-Tardiness Single Machine Scheduling with Maintenance Considerations
Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, APMS 2021, pp 269--276, Springer International Publishing, 08/2021

Author of the Ph.D. thesis "Reinforcement Learning for Simultaneous Scheduling of Production and Maintenance"

2022 - 2023

At the heart of industrial efficiency lies the complex yet indispensable process of job-shop scheduling optimization. The focus of this doctoral thesis is to delve into this subject, particularly emphasizing the importance of preventive maintenance as a mechanism to minimize machine breakdowns and promote smooth Just-In-Time (JIT) efficiency.

The exploration of the intersection between classical optimization methods and Artificial Intelligence (AI), specifically reinforcement learning (RL), forms a central theme of the research. The objective is not only to harness the potential of these cutting-edge AI technologies but also to compare their efficacy against traditional methods in solving complex optimization problems.

The research journey begins with a focus on a single machine system, understanding the interplay between scheduling resumable jobs and preventive maintenance, and the impact of such considerations on reducing machine breakdowns and promoting JIT efficiency. Two algorithms were investigated. The first is based on a stochastic dynamic programming and the second is based on RL.

Moving beyond a single machine, the thesis expands to consider more complex settings like job-shop scheduling, taking into account the unique challenges of JIT delivery and operation-specific due dates. In this context, methodologies like Adaptive Large Neighborhood Search (ALNS) are studied, offering promising approaches for these NP-Hard problems.

Moreover, the proposed research proposes a comprehensive study on RL-based methodologies, demonstrating their robustness and effectiveness in optimizing job-shop scheduling while considering preventive maintenance in a JIT context. These findings contribute significant advancements to the field, providing a roadmap for future research focused on enhancing the performance and competitiveness of industrial production systems.


2015 - 2022

Optimisation des Opérations en Logistique et en Maintenance des Systèmes de Transport

Summary :

Une bonne politique de maintenance des systèmes de transport joue un rôle important pour garantir, à la fois, la fluidité des flux de transport (marchandises, personnes) et la réduction des coûts d’exploitation. Ceci est d’autant plus vrai que les systèmes de transport actuels sont de plus en plus complexes et requièrent donc une maintenance techniquement plus difficile (...)