Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)

International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)

📍Biskra, Algeria🗓️ 13-14 April 2026
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Enhanced Hybrid Gaussian Quantum Particle Swarm Optimization with Adaptive Genetic Algorithm for Flexible Job Shop Scheduling

Authors
Mohamed Madassi1, *, Imed Eddine Talhi1, Sara Sabba2
1Faculty of New Technologies of Information and Communication, Abdelhamid Mehri University Constantine 2, Constantine, 25000, Algeria
2Faculty of New Technologies of Information and Communication, Laboratory of Data Science and Artificial Intelligence (LISIA), Abdelhamid Mehri University Constantine 2, Constantine, 25000, Algeria
*Corresponding author. Email: mohamed.madassi@univ-constantine2.dz
Corresponding Author
Mohamed Madassi
Available Online 24 June 2026.
DOI
10.2991/978-94-6239-711-8_38How to use a DOI?
Keywords
Flexible Job-Shop Scheduling; Quantum Particle Swarm Optimization; Adaptive Genetic Algorithm; Hybrid Metaheuristics; Combinatorial Optimization
Abstract

The Flexible Job Shop Problem (FJSP) is an extension of the classical job shop scheduling problem. It assigns each operation to a machine and sequences the operations on each machine with the aim of minimizing the maximum completion time of all operations. The FJSP is an NP-hard combinatorial optimization problem with critical applications in smart manufacturing, Industry 4.0, and digital twin-based production systems. This paper presents an enhanced version of the Hybrid Gaussian Quantum Particle Swarm Optimization with Adaptive Genetic Algorithm (HGQPSO-AGA) which introduced four key improvements: integration of Gaussian quantum genetic operators into the adaptive genetic algorithm phase, a greedy decoding strategy for intelligent schedule construction, multi-neighborhood local search for solution refinement, and path relinking for intensification. The computational experiment was conducted on 18 benchmark instances from the Kacem, Brandimarte, and Dauzère-Pérès datasets. The results demonstrate that the enhanced algorithm achieves substantial improvements, with an average makespan reduction of 31.7%. Statistical tests confirm the significance of these gains, with the enhanced HGQPSO-AGA achieving a perfect win record across all 90 experimental runs. These findings establish HGQPSO-AGA as a highly effective approach for solving complex scheduling problems.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
Series
Advances in Economics, Business and Management Research
Publication Date
24 June 2026
ISBN
978-94-6239-711-8
ISSN
2352-5428
DOI
10.2991/978-94-6239-711-8_38How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Mohamed Madassi
AU  - Imed Eddine Talhi
AU  - Sara Sabba
PY  - 2026
DA  - 2026/06/24
TI  - Enhanced Hybrid Gaussian Quantum Particle Swarm Optimization with Adaptive Genetic Algorithm for Flexible Job Shop Scheduling
BT  - Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
PB  - Atlantis Press
SP  - 409
EP  - 418
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-711-8_38
DO  - 10.2991/978-94-6239-711-8_38
ID  - Madassi2026
ER  -