An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II

Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective...

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Main Authors: Aidin Delgoshaei, Mohd Khairol Anuar Bin Mohd Ariffin, Zulkiflle B. Leman
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/10/23/4607
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author Aidin Delgoshaei
Mohd Khairol Anuar Bin Mohd Ariffin
Zulkiflle B. Leman
author_facet Aidin Delgoshaei
Mohd Khairol Anuar Bin Mohd Ariffin
Zulkiflle B. Leman
author_sort Aidin Delgoshaei
collection DOAJ
description Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective factors, determine their weights on multi-objective functions, and, accordingly, schedule manufacturing systems in a fuzzy environment. The aim is to optimize product completion time and operational and product defect costs in a job-shop-based multi-objective fuzzy scheduling problem. In the first and second phases of the proposed framework, it was shown that the existing uncertainty of the internal factors for the studied cases causes the weights of factors to change up to 44.5%. Then, a fuzzy-weighted NSGA-II is proposed (FW-NSGA-II) to address the developed Non-linear Fuzzy Multi-objective Dual resource-constrained scheduling problem. Comparing the outcomes of the proposed method with other solving algorithms, such as the Sine Cosine Algorithm, Simulated Annealing, Tabu Search, and TLBO heuristic, using seven series of comprehensive computational experiments, indicates the superiority of the proposed framework in scheduling manufacturing systems. The outcomes indicated that using the proposed method for the studied cases saved up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems. The outcomes of this study can help production planning managers to provide more realistic schedules by considering fuzzy factors in their manufacturing systems. Further investigating the proposed method for dynamic product conditions is another direction for future research.
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spelling doaj.art-25d9abdfa02249009b93a5bec654a4982023-11-24T11:36:22ZengMDPI AGMathematics2227-73902022-12-011023460710.3390/math10234607An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-IIAidin Delgoshaei0Mohd Khairol Anuar Bin Mohd Ariffin1Zulkiflle B. Leman2Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, SL, MalaysiaDepartment of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, SL, MalaysiaDepartment of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, SL, MalaysiaImproving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective factors, determine their weights on multi-objective functions, and, accordingly, schedule manufacturing systems in a fuzzy environment. The aim is to optimize product completion time and operational and product defect costs in a job-shop-based multi-objective fuzzy scheduling problem. In the first and second phases of the proposed framework, it was shown that the existing uncertainty of the internal factors for the studied cases causes the weights of factors to change up to 44.5%. Then, a fuzzy-weighted NSGA-II is proposed (FW-NSGA-II) to address the developed Non-linear Fuzzy Multi-objective Dual resource-constrained scheduling problem. Comparing the outcomes of the proposed method with other solving algorithms, such as the Sine Cosine Algorithm, Simulated Annealing, Tabu Search, and TLBO heuristic, using seven series of comprehensive computational experiments, indicates the superiority of the proposed framework in scheduling manufacturing systems. The outcomes indicated that using the proposed method for the studied cases saved up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems. The outcomes of this study can help production planning managers to provide more realistic schedules by considering fuzzy factors in their manufacturing systems. Further investigating the proposed method for dynamic product conditions is another direction for future research.https://www.mdpi.com/2227-7390/10/23/4607product schedulingjob-shopweighted NSGA-IIproduct completion time
spellingShingle Aidin Delgoshaei
Mohd Khairol Anuar Bin Mohd Ariffin
Zulkiflle B. Leman
An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II
Mathematics
product scheduling
job-shop
weighted NSGA-II
product completion time
title An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II
title_full An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II
title_fullStr An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II
title_full_unstemmed An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II
title_short An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II
title_sort effective 4 phased framework for scheduling job shop manufacturing systems using weighted nsga ii
topic product scheduling
job-shop
weighted NSGA-II
product completion time
url https://www.mdpi.com/2227-7390/10/23/4607
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