Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization

Madani Bezoui; Abdulaziz Turki Almaktoom; Ahcene Bounceur; Saeed Mian Qaisar; Mervat Chouman

doi:10.1109/LT60077.2024.10469011

Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization

Madani Bezoui^*, Abdulaziz Turki Almaktoom, Ahcene Bounceur, Saeed Mian Qaisar, Mervat Chouman

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper explores the transition to Industry 5.0, highlighting its focus on sustainable, human-centred and resilient industrial progress. In this new era, the integration of advanced technology with human expertise is crucial, emphasising the importance of balancing efficiency, cost, quality, and sustainability. At the heart of this research is Multi-Objective Optimisation (MOO), which is used to address the complex challenges of modern manufacturing systems. We propose an innovative approach that combines mathematical modelling with swarm intelligence to tackle complex optimisation problems. A detailed Multi-Objective Mixed Integer Linear Programming (MILP) model is developed and its effectiveness is demonstrated through the application of Multi-Objective Particle Swarm Optimisation (MOPSO). The study compares the performance of MOPSO with traditional optimisation methods using synthetic data analysis. The results not only demonstrate the potential of MOPSO in modern manufacturing, but also set the stage for future research to integrate human ergonomics into the optimization framework, thereby contributing to the holistic advancement of Industry 5.0.

Original language	English
Title of host publication	21st International Learning and Technology Conference
Subtitle of host publication	Reality and Science Fiction in Education, L and T 2024
Editors	Akila Sarirete, Mohamed F. El-Amin Mousa, Passent M. Elkafrawy, Zain Balfagih, Tayeb Brahimi
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	245-249
Number of pages	5
ISBN (Electronic)	9798350393569
DOIs	https://doi.org/10.1109/LT60077.2024.10469011
State	Published - 2024
Event	21st International Learning and Technology Conference, L and T 2024 - Jeddah, Saudi Arabia Duration: 15 Jan 2024 → 16 Jan 2024

Publication series

Name	21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024

Conference

Conference	21st International Learning and Technology Conference, L and T 2024
Country/Territory	Saudi Arabia
City	Jeddah
Period	15/01/24 → 16/01/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

Industry 5.0
Metahurestic Algorithms
Multi-Objective Optimization
Particle Swarm Optimization

ASJC Scopus subject areas

Control and Optimization
Modeling and Simulation
Education
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems and Management
Media Technology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/LT60077.2024.10469011

Cite this

Bezoui, M., Almaktoom, A. T., Bounceur, A., Qaisar, S. M., & Chouman, M. (2024). Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization. In A. Sarirete, M. F. El-Amin Mousa, P. M. Elkafrawy, Z. Balfagih, & T. Brahimi (Eds.), 21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024 (pp. 245-249). (21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LT60077.2024.10469011

Bezoui, Madani ; Almaktoom, Abdulaziz Turki ; Bounceur, Ahcene et al. / Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization. 21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024. editor / Akila Sarirete ; Mohamed F. El-Amin Mousa ; Passent M. Elkafrawy ; Zain Balfagih ; Tayeb Brahimi. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 245-249 (21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024).

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title = "Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization",

abstract = "This paper explores the transition to Industry 5.0, highlighting its focus on sustainable, human-centred and resilient industrial progress. In this new era, the integration of advanced technology with human expertise is crucial, emphasising the importance of balancing efficiency, cost, quality, and sustainability. At the heart of this research is Multi-Objective Optimisation (MOO), which is used to address the complex challenges of modern manufacturing systems. We propose an innovative approach that combines mathematical modelling with swarm intelligence to tackle complex optimisation problems. A detailed Multi-Objective Mixed Integer Linear Programming (MILP) model is developed and its effectiveness is demonstrated through the application of Multi-Objective Particle Swarm Optimisation (MOPSO). The study compares the performance of MOPSO with traditional optimisation methods using synthetic data analysis. The results not only demonstrate the potential of MOPSO in modern manufacturing, but also set the stage for future research to integrate human ergonomics into the optimization framework, thereby contributing to the holistic advancement of Industry 5.0.",

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doi = "10.1109/LT60077.2024.10469011",

language = "English",

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pages = "245--249",

editor = "Akila Sarirete and \{El-Amin Mousa\}, \{Mohamed F.\} and Elkafrawy, \{Passent M.\} and Zain Balfagih and Tayeb Brahimi",

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Bezoui, M, Almaktoom, AT, Bounceur, A, Qaisar, SM & Chouman, M 2024, Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization. in A Sarirete, MF El-Amin Mousa, PM Elkafrawy, Z Balfagih & T Brahimi (eds), 21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024. 21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024, Institute of Electrical and Electronics Engineers Inc., pp. 245-249, 21st International Learning and Technology Conference, L and T 2024, Jeddah, Saudi Arabia, 15/01/24. https://doi.org/10.1109/LT60077.2024.10469011

Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization. / Bezoui, Madani; Almaktoom, Abdulaziz Turki; Bounceur, Ahcene et al.
21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024. ed. / Akila Sarirete; Mohamed F. El-Amin Mousa; Passent M. Elkafrawy; Zain Balfagih; Tayeb Brahimi. Institute of Electrical and Electronics Engineers Inc., 2024. p. 245-249 (21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization

AU - Bezoui, Madani

AU - Almaktoom, Abdulaziz Turki

AU - Bounceur, Ahcene

AU - Qaisar, Saeed Mian

AU - Chouman, Mervat

PY - 2024

Y1 - 2024

N2 - This paper explores the transition to Industry 5.0, highlighting its focus on sustainable, human-centred and resilient industrial progress. In this new era, the integration of advanced technology with human expertise is crucial, emphasising the importance of balancing efficiency, cost, quality, and sustainability. At the heart of this research is Multi-Objective Optimisation (MOO), which is used to address the complex challenges of modern manufacturing systems. We propose an innovative approach that combines mathematical modelling with swarm intelligence to tackle complex optimisation problems. A detailed Multi-Objective Mixed Integer Linear Programming (MILP) model is developed and its effectiveness is demonstrated through the application of Multi-Objective Particle Swarm Optimisation (MOPSO). The study compares the performance of MOPSO with traditional optimisation methods using synthetic data analysis. The results not only demonstrate the potential of MOPSO in modern manufacturing, but also set the stage for future research to integrate human ergonomics into the optimization framework, thereby contributing to the holistic advancement of Industry 5.0.

AB - This paper explores the transition to Industry 5.0, highlighting its focus on sustainable, human-centred and resilient industrial progress. In this new era, the integration of advanced technology with human expertise is crucial, emphasising the importance of balancing efficiency, cost, quality, and sustainability. At the heart of this research is Multi-Objective Optimisation (MOO), which is used to address the complex challenges of modern manufacturing systems. We propose an innovative approach that combines mathematical modelling with swarm intelligence to tackle complex optimisation problems. A detailed Multi-Objective Mixed Integer Linear Programming (MILP) model is developed and its effectiveness is demonstrated through the application of Multi-Objective Particle Swarm Optimisation (MOPSO). The study compares the performance of MOPSO with traditional optimisation methods using synthetic data analysis. The results not only demonstrate the potential of MOPSO in modern manufacturing, but also set the stage for future research to integrate human ergonomics into the optimization framework, thereby contributing to the holistic advancement of Industry 5.0.

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KW - Metahurestic Algorithms

KW - Multi-Objective Optimization

KW - Particle Swarm Optimization

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DO - 10.1109/LT60077.2024.10469011

M3 - Conference contribution

AN - SCOPUS:85190065894

T3 - 21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024

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EP - 249

BT - 21st International Learning and Technology Conference

A2 - Sarirete, Akila

A2 - El-Amin Mousa, Mohamed F.

A2 - Elkafrawy, Passent M.

A2 - Balfagih, Zain

A2 - Brahimi, Tayeb

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st International Learning and Technology Conference, L and T 2024

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ER -

Bezoui M, Almaktoom AT, Bounceur A, Qaisar SM, Chouman M. Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization. In Sarirete A, El-Amin Mousa MF, Elkafrawy PM, Balfagih Z, Brahimi T, editors, 21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024. Institute of Electrical and Electronics Engineers Inc. 2024. p. 245-249. (21st International Learning and Technology Conference: Reality and Science Fiction in Education, L and T 2024). doi: 10.1109/LT60077.2024.10469011

Hybrid Metaheuristics for Industry 5.0 Multi-Objective Manufacturing and Supply Chain Optimization

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this