Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown

Resource scheduling, job sequencing, and assigning them to available resources are the most critical issues in manufacturing systems, such as flexible job-shop systems. In addition, scheduling uncertainties have attracted significant attention in this field. This study investigates the dual-resource...

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Main Authors:	Parham Soofi, Mehdi Yazdani, Maghsoud Amiri, Mohammad Amin Adibi
Format:	article
Language:	EN
Published:	IEEE 2021
Subjects:	Dual-resource constrained flexible job-shop scheduling genetic algorithm machine breakdown robust fuzzy-stochastic programming uncertainty Electrical engineering. Electronics. Nuclear engineering TK1-9971
Online Access:	https://doaj.org/article/3a0e363e64e74c7b8cbebe6c58d3f78b
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spelling	oai:doaj.org-article:3a0e363e64e74c7b8cbebe6c58d3f78b2021-12-01T00:00:31ZRobust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown2169-353610.1109/ACCESS.2021.3126820https://doaj.org/article/3a0e363e64e74c7b8cbebe6c58d3f78b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9609986/https://doaj.org/toc/2169-3536Resource scheduling, job sequencing, and assigning them to available resources are the most critical issues in manufacturing systems, such as flexible job-shop systems. In addition, scheduling uncertainties have attracted significant attention in this field. This study investigates the dual-resource constrained flexible job-shop scheduling (DRCFJSS) problem under machine breakdown and operational uncertainty. Stochastic scenario-based methods were utilized to study the uncertain nature of the problem. Because process times have inherent uncertainty, they are considered fuzzy numbers and are controlled by a credibility-based measure. Robust scheduling must be developed to address unexpected disruptions, such as machine breakdowns and operational risks, such as uncertain process times. Accordingly, a novel robust fuzzy stochastic programming (RFSP) model is presented for this problem. In the proposed RFSP model, the objective function is formulated using a hybrid measure (i.e., a combined average-case and worst-case performance of the manufacturing system) under probable machine breakdown scenarios. Because the DRCFJSS problem is NP-hard, two types of meta-heuristic algorithms, evolutionary population-based, genetic algorithm (GA), and vibration damping optimization (VDO) algorithm, are used for large-sized problems. Then, the proposed RFSP model was applied to a case study, and numerical experiments with randomly generated test problems were used. In small-sized problems, the proposed model is solved using the CPLEX solver, GA and VDO algorithms. Also, the computational study confirms the proper quality of the results of the GA and VDO algorithms in medium and large-sized problems.Parham SoofiMehdi YazdaniMaghsoud AmiriMohammad Amin AdibiIEEEarticleDual-resource constrainedflexible job-shop schedulinggenetic algorithmmachine breakdownrobust fuzzy-stochastic programminguncertaintyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155740-155762 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Dual-resource constrained flexible job-shop scheduling genetic algorithm machine breakdown robust fuzzy-stochastic programming uncertainty Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Dual-resource constrained flexible job-shop scheduling genetic algorithm machine breakdown robust fuzzy-stochastic programming uncertainty Electrical engineering. Electronics. Nuclear engineering TK1-9971 Parham Soofi Mehdi Yazdani Maghsoud Amiri Mohammad Amin Adibi Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown
description	Resource scheduling, job sequencing, and assigning them to available resources are the most critical issues in manufacturing systems, such as flexible job-shop systems. In addition, scheduling uncertainties have attracted significant attention in this field. This study investigates the dual-resource constrained flexible job-shop scheduling (DRCFJSS) problem under machine breakdown and operational uncertainty. Stochastic scenario-based methods were utilized to study the uncertain nature of the problem. Because process times have inherent uncertainty, they are considered fuzzy numbers and are controlled by a credibility-based measure. Robust scheduling must be developed to address unexpected disruptions, such as machine breakdowns and operational risks, such as uncertain process times. Accordingly, a novel robust fuzzy stochastic programming (RFSP) model is presented for this problem. In the proposed RFSP model, the objective function is formulated using a hybrid measure (i.e., a combined average-case and worst-case performance of the manufacturing system) under probable machine breakdown scenarios. Because the DRCFJSS problem is NP-hard, two types of meta-heuristic algorithms, evolutionary population-based, genetic algorithm (GA), and vibration damping optimization (VDO) algorithm, are used for large-sized problems. Then, the proposed RFSP model was applied to a case study, and numerical experiments with randomly generated test problems were used. In small-sized problems, the proposed model is solved using the CPLEX solver, GA and VDO algorithms. Also, the computational study confirms the proper quality of the results of the GA and VDO algorithms in medium and large-sized problems.
format	article
author	Parham Soofi Mehdi Yazdani Maghsoud Amiri Mohammad Amin Adibi
author_facet	Parham Soofi Mehdi Yazdani Maghsoud Amiri Mohammad Amin Adibi
author_sort	Parham Soofi
title	Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown
title_short	Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown
title_full	Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown
title_fullStr	Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown
title_full_unstemmed	Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown
title_sort	robust fuzzy-stochastic programming model and meta-heuristic algorithms for dual-resource constrained flexible job-shop scheduling problem under machine breakdown
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/3a0e363e64e74c7b8cbebe6c58d3f78b
work_keys_str_mv	AT parhamsoofi robustfuzzystochasticprogrammingmodelandmetaheuristicalgorithmsfordualresourceconstrainedflexiblejobshopschedulingproblemundermachinebreakdown AT mehdiyazdani robustfuzzystochasticprogrammingmodelandmetaheuristicalgorithmsfordualresourceconstrainedflexiblejobshopschedulingproblemundermachinebreakdown AT maghsoudamiri robustfuzzystochasticprogrammingmodelandmetaheuristicalgorithmsfordualresourceconstrainedflexiblejobshopschedulingproblemundermachinebreakdown AT mohammadaminadibi robustfuzzystochasticprogrammingmodelandmetaheuristicalgorithmsfordualresourceconstrainedflexiblejobshopschedulingproblemundermachinebreakdown
_version_	1718406132961116160

Robust Fuzzy-Stochastic Programming Model and Meta-Heuristic Algorithms for Dual-Resource Constrained Flexible Job-Shop Scheduling Problem Under Machine Breakdown

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