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All Journal Jurnal Optimasi Sistem Industri International Journal of Industrial Engineering and Engineering Management
Risma, Yolanda Mega
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Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Energy-efficient No-idle Flowshop Scheduling Optimization Using African Vultures Algorithm Risma, Yolanda Mega; Utama, Dana Marsetiya Utama; Amallynda, Ikhlasul
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 1 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i1.8335

Abstract

The issue of energy consumption is currently a major concern globally, especially in the industrial sector, where most of the energy demand comes from the manufacturing sector. To reduce energy consumption, one of the proposed strategies is to reduce the idle time between jobs on machines during the production process, known as No-Idle Permutation Flowshop Scheduling (NIPFSP). This research proposes the application of the African Vultures Optimization Algorithm (AVOA) as a solution to the energy consumption challenge in the case of production scheduling. The algorithm is examined in detail through a series of trials to obtain the most efficient work order in the production schedule, subject to careful setting of iteration and population parameters. The result of implementing the AVOA algorithm is then compared with the method used by the company in a scheduling case. The research findings show that AVOA significantly outperforms the method commonly used by the company, confirming its performance advantage in optimizing energy consumption in the context of production scheduling.
Energy-efficient No-idle Flowshop Scheduling Optimization Using African Vultures Algorithm Risma, Yolanda Mega; Utama, Dana Marsetiya Utama; Amallynda, Ikhlasul
International Journal of Industrial Engineering and Engineering Management Vol. 6 No. 1 (2024)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v6i1.8335

Abstract

The issue of energy consumption is currently a major concern globally, especially in the industrial sector, where most of the energy demand comes from the manufacturing sector. To reduce energy consumption, one of the proposed strategies is to reduce the idle time between jobs on machines during the production process, known as No-Idle Permutation Flowshop Scheduling (NIPFSP). This research proposes the application of the African Vultures Optimization Algorithm (AVOA) as a solution to the energy consumption challenge in the case of production scheduling. The algorithm is examined in detail through a series of trials to obtain the most efficient work order in the production schedule, subject to careful setting of iteration and population parameters. The result of implementing the AVOA algorithm is then compared with the method used by the company in a scheduling case. The research findings show that AVOA significantly outperforms the method commonly used by the company, confirming its performance advantage in optimizing energy consumption in the context of production scheduling.
AVOA and ALO Algorithm for Energy-Efficient No-Idle Permutation Flow Shop Scheduling Problem: A Comparison Study Risma, Yolanda Mega; Utama, Dana Marsetiya
Jurnal Optimasi Sistem Industri Vol. 22 No. 2 (2023): Published in December 2023
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/josi.v22.n2.p126-141.2023

Abstract

Global energy consumption is a pressing issue and is predicted to continue increasing between 2010 and 2040. Among the various sectors, the industrial sector, particularly manufacturing, is the main driver of this increase. To effectively address this growing problem and support energy conservation efforts, reducing idle time on production-related machines is critical. The No-Idle Permutation Flow Shop Problem (NIPFSP) and, indirectly, the need to reduce energy consumption in manufacturing processes are the driving forces behind this study. The African Vultures Optimization Algorithm (AVOA) and the Ant Lion Optimizer (ALO) are two novel meta-heuristic algorithms designed to achieve this goal. The effectiveness of both AVOA and ALO was rigorously evaluated across three distinct scenarios: small, medium, and large. Statistical analysis, in the form of independent sample t-tests, was employed to compare the performance of these algorithms. We found that, while both algorithms yielded similar results in the small case, AVOA demonstrated a superior capability in optimizing the NIPFSP in the medium and large cases and, consequently, in curbing energy consumption. This implies that AVOA offers a more promising approach to addressing energy consumption concerns in the manufacturing sector, particularly in scenarios involving medium- to large-scale production processes. The implementation of such innovative meta-heuristic algorithms holds the potential to significantly contribute to global energy conservation efforts while enhancing the efficiency of industrial operations.
Co-Authors Dana Marsetya Utama Ikhlasul Amallynda Utama, Dana Marsetiya Utama