A multi-objective assembly line balancing problem with worker’s skill and qualification considerations in fuzzy environment

Salehi, Maryam; Maleki, Hamid Reza; Niroomand, Sadegh

doi:10.1007/s10489-017-1065-2

Cited by 28 publications

(10 citation statements)

References 55 publications

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“…Laktionov presented expert estimates using fuzzy logic to select machine operators for complex manufacturing processes [27]. Salehi et al presented a manufacturing line balancing problem that takes into account the skill of the operator in a fuzzy environment [28]. Wang et al attempted to reduce errors in a manufacturing process by applying ergonomic elements and Bayesian networks [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Remote Sensing of Sewing Work Levels Using a Power Monitoring System

Jung

Park²,

Lee³

et al. 2020

Applied Sciences

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The measurement of sewing work in the labor-intensive garment industry depends considerably on the person performing the measurements, making it difficult to quantitatively define the level of skill (LS) of the sewing machine operator and the level of difficulty (LD) of the unit process. In this study, a power monitoring system attached to the sewing machine was used to remotely collect power consumption data, which were then analyzed to extract the working times for a series of sewing tasks. LS of each operator was then classified and LD of each process was analyzed in terms of working time and quality. Finally, the resulting LS and LD weight factors considered to optimize the subject garment production line were compared against those proposed by experts. The LS weight factor proposed by the experts was ~15% less than that indicated by the experimental results, whereas the LD weight factor proposed by the experts was ~15%–40% greater than that indicated by the experimental results. The results of this study suggest that the proposed method could be applied in real time to inform the arrangement of line workers to increase the productivity of a garment production line.

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Section: Literature Reviewmentioning

confidence: 99%

Remote Sensing of Sewing Work Levels Using a Power Monitoring System

Jung

Park²,

Lee³

et al. 2020

Applied Sciences

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“…The distance metrics measure the distance of the Pareto‐optimal solutions from the ideal solutions. Three famous types of distance, (1) Manhattan distance (Distance 1), (2) Euclidean distance (Distance 2), and (3) Chebyshev distance (Distance 3), have been used in the literature (Salehi, Maleki, & Niroomand, 2018) to measure the quality of Pareto‐optimal solutions. For more details on these distances, the study of Salehi et al (2018) can be referred.…”

Section: Computational Studymentioning

confidence: 99%

Uncertain location–allocation decisions for a bi‐objective two‐stage supply chain network design problem with environmental impacts

Mahmoodirad

Niroomand²

2020

Expert Systems

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In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chanceconstrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.

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“…Including this in the optimization model will re-arrange the tasks among the stations so that all the stations have an equal sum of tasks' time variance. The SMT max and STV max are calculated using Equations ( 2) and (3).…”

Section: A Problem Descriptionmentioning

confidence: 99%

“…In principle, ALBPs are generally classified into two groups depending on their assumptions, constants, and objectives. The two groups are simple assembly line balancing problems (SALBPs) and generalized assembly line balancing problems (GALBPs) [2], [3].…”

Section: Introductionmentioning

confidence: 99%

An Optimization Model for Balancing Assembly Lines With Stochastic Task Times and Zoning Constraints

Fathi

Nourmohammadi

et al. 2019

IEEE Access

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This paper aims to bridge the gap between theory and practice by addressing a real-world assembly line balancing problem (ALBP), where task times are stochastic and there are zoning constraints in addition to the commonly known ALBP constraints. A mixed integer programming (MIP) model is proposed for each of the straight and U-shaped assembly line configurations. The primary objective in both cases is to minimize the number of stations; minimizing the maximum of stations' meantime; and the stations' time variance is considered as secondary objectives. Four different scenarios are discussed for each model, with differences in the objective function. The models are validated by solving a real case taken from an automobile manufacturing company and some standard test problems available in this paper. The results indicate that both models are able to provide optimum solutions for problems of different sizes. The technique for order preference by similarity to ideal solution (TOPSIS) is used to create reliable comparisons of the different scenarios and valid analysis of the results. Finally, some insights regarding the selection of straight and U-shaped layouts are provided. INDEX TERMSAssembly line balancing, mathematical programming, stochastic, zoning constraints.

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A multi-objective assembly line balancing problem with worker’s skill and qualification considerations in fuzzy environment

Cited by 28 publications

References 55 publications

Remote Sensing of Sewing Work Levels Using a Power Monitoring System

Remote Sensing of Sewing Work Levels Using a Power Monitoring System

Uncertain location–allocation decisions for a bi‐objective two‐stage supply chain network design problem with environmental impacts

An Optimization Model for Balancing Assembly Lines With Stochastic Task Times and Zoning Constraints

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