Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards

End-of-life (EOL) electromechanical products often have multiple failure characteristics and material hazard attributes. These factors create uncertain disassembly task sequences and affect the remanufacturing cost, environmental sustainability, and disassembly efficiency of the remanufacturing disa...

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Main Authors: Wei Meng, Xiufen Zhang
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/12/18/7318
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spelling doaj-6e89295ec8e24ef6a0c690b9ee77e28f2020-11-25T03:18:59ZengMDPI AGSustainability2071-10502020-09-01127318731810.3390/su12187318Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material HazardsWei Meng0Xiufen Zhang1College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaCollege of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaEnd-of-life (EOL) electromechanical products often have multiple failure characteristics and material hazard attributes. These factors create uncertain disassembly task sequences and affect the remanufacturing cost, environmental sustainability, and disassembly efficiency of the remanufacturing disassembly line system. To address this problem, a novel multi-constraint remanufacturing disassembly line balancing model (MC-RDLBM) is constructed in this article, which accounts for the failure characteristics of the parts and material hazard constraints. To assign the disassembly task reasonably, a disassembly priority decision-making model was presented to describe the relationship between the failure layer, the material hazards layer, and the economic feasibility layer. Furthermore, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) optimization for the MC-RDLBM is improved. To increase the convergence speed of the algorithm, an initial population construction method is designed, which includes the component failure and material hazards. Moreover, a novel genetic algorithm evolution rule with a Pareto non-dominant relation and crowded distance constraint is established, which expands the search scope of the chromosome’s autonomous evolution and avoids local convergence. Furthermore, a Pareto grade-based evaluation strategy for non-dominant solutions is proposed to eliminate the invalid remanufacturing disassembly task sequences. Finally, a case study verified the effectiveness and feasibility of the proposed method.https://www.mdpi.com/2071-1050/12/18/7318remanufacturing disassembly line balance (RDLB)multiple failuresmaterial hazardsNSGA-II algorithmmulti-objective optimization
collection DOAJ
language English
format Article
sources DOAJ
author Wei Meng
Xiufen Zhang
spellingShingle Wei Meng
Xiufen Zhang
Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards
Sustainability
remanufacturing disassembly line balance (RDLB)
multiple failures
material hazards
NSGA-II algorithm
multi-objective optimization
author_facet Wei Meng
Xiufen Zhang
author_sort Wei Meng
title Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards
title_short Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards
title_full Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards
title_fullStr Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards
title_full_unstemmed Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards
title_sort optimization of remanufacturing disassembly line balance considering multiple failures and material hazards
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2020-09-01
description End-of-life (EOL) electromechanical products often have multiple failure characteristics and material hazard attributes. These factors create uncertain disassembly task sequences and affect the remanufacturing cost, environmental sustainability, and disassembly efficiency of the remanufacturing disassembly line system. To address this problem, a novel multi-constraint remanufacturing disassembly line balancing model (MC-RDLBM) is constructed in this article, which accounts for the failure characteristics of the parts and material hazard constraints. To assign the disassembly task reasonably, a disassembly priority decision-making model was presented to describe the relationship between the failure layer, the material hazards layer, and the economic feasibility layer. Furthermore, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) optimization for the MC-RDLBM is improved. To increase the convergence speed of the algorithm, an initial population construction method is designed, which includes the component failure and material hazards. Moreover, a novel genetic algorithm evolution rule with a Pareto non-dominant relation and crowded distance constraint is established, which expands the search scope of the chromosome’s autonomous evolution and avoids local convergence. Furthermore, a Pareto grade-based evaluation strategy for non-dominant solutions is proposed to eliminate the invalid remanufacturing disassembly task sequences. Finally, a case study verified the effectiveness and feasibility of the proposed method.
topic remanufacturing disassembly line balance (RDLB)
multiple failures
material hazards
NSGA-II algorithm
multi-objective optimization
url https://www.mdpi.com/2071-1050/12/18/7318
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