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

Abderrazzak SABRI

Ph.D. student
Member of the research themes:
Contact information:

Revue Internationale avec Comité de Lecture

2023
International journal with review committee
DOI
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

2022
International conference with review committee
DOI
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
2021
International conference with review committee
DOI
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

Ph.D. topic: "Machine learning for production scheduling"

2022

We propose as aim of this thesis to simultaneously schedule production tasks and machine maintenance activities in order to optimize both the storage costs and the delay penalties according to a Just In Time (JIT) approach. Often three classical research fields are investigated for this type of problems. The first is the complexity analysis of solving algorithms. The second is the formulation of algorithms giving the optimal solution. If the computational time is exorbitant, the third field could be investigated. It consists in using heuristics, meta-heuristics or the integration of different resolution methods to give approximate solutions. We will extend these fields to new algorithms based on deep learning and reinforcement learning. We favor learning-based algorithms that can effectively integrate optimization methods to find an efficient tradeoff between computational time and solution quality. However, the integrated schemes of learning and optimization methods are not obvious and easy to design and develop. Finally, the open questions that we need to answer are for example: which integration and hybridization scheme for which configuration of the studied production system? We also intend to improve the existing solving methods in terms of computational time and solution quality.

OLOGMAESTRO (ELSAT2020)

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 (...)