Minimizing makespan in a multi-stage hybrid flow shop scheduling problem with dedicated machines

This paper addresses the scheduling problem for flow shops with multiple dedicated machines, commonly referred to as the Hybrid Flow Shop Scheduling Problem with Dedicated Machines (HFSSPDM). It corresponds to the case where dedicated machines are assigned to particular job types. The primary objective is to determine a schedule that minimizes the makespan, ensuring efficient utilization of machines. To tackle this challenging problem, we propose a Genetic Algorithm (GA), which is well-suited for solving complex scheduling problems. A preliminary analysis was conducted to evaluate the performance of the proposed GA in terms of solution quality and computation time across a set of HFSSPDM instances. The results demonstrate that the GA is capable of producing high-quality feasible solutions for several problem instances. Future efforts will focus on addressing the observed limitations to develop a more robust and efficient version of the Genetic Algorithm model.

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Publication Details

Published
2025-05-01
DOI
https://doi.org/10.1109/ic_aset65966.2025.11232262
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
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article

Minimizing makespan in a multi-stage hybrid flow shop scheduling problem with dedicated machines

Najla Aissaoui, Safa Bhar Layeb, Asma Ouled Bedhief, Amira Brahmi
Scheduling and Optimization Algorithms
article

Minimizing makespan in a multi-stage hybrid flow shop scheduling problem with dedicated machines

Najla Aissaoui, Safa Bhar Layeb, Asma Ouled Bedhief, Amira Brahmi
article en

Abstract

This paper addresses the scheduling problem for flow shops with multiple dedicated machines, commonly referred to as the Hybrid Flow Shop Scheduling Problem with Dedicated Machines (HFSSPDM). It corresponds to the case where dedicated machines are assigned to particular job types. The primary objective is to determine a schedule that minimizes the makespan, ensuring efficient utilization of machines. To tackle this challenging problem, we propose a Genetic Algorithm (GA), which is well-suited for solving complex scheduling problems. A preliminary analysis was conducted to evaluate the performance of the proposed GA in terms of solution quality and computation time across a set of HFSSPDM instances. The results demonstrate that the GA is capable of producing high-quality feasible solutions for several problem instances. Future efforts will focus on addressing the observed limitations to develop a more robust and efficient version of the Genetic Algorithm model.

National Engineering School of Tunis (TN), Tunis El Manar University (TN)
Openalex Percentile: Top 52%
Scheduling and Optimization Algorithms
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