Maximizing comfort in Assembly Lines with temporal, spatial and ergonomic attributes

Bautista, Joaquı́n; Alfaro-Pozo, Rocío; Batalla-García, Cristina

doi:10.1080/18756891.2016.1204125

Cited by 31 publications

(12 citation statements)

References 15 publications

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“…(i) Minimizing both objectives simultaneously (ii) Subordinating one objective to the other one (iii) Solving the problem mono-objectively and determining the other objective afterwards In accordance with the second way, and taking the previous work (Bautista et al, 2016a) as a reference, a mathematical model to minimize the maximum ergonomic risk of the line first, and then, the ergonomic risk dispersion between workstations, is presented. Specifically, in this work, the ergonomic risk dispersion is measured through the standard deviation, unlike Bautista et al (2016a), where the average absolute deviation was considered. The parameters, variables, and the mathematical model formulation are shown below:…”

Section: Mathematical Model: Min R_sd(r)mentioning

confidence: 99%

“…To define the neighborhood of a solution, and how to explore it 3. To define the stopping criterion based on runtime or number of iterations Specifically, the GRASP proposed in this paper is similar to that in Bautista et al (2016a). However, in this work, the main goal is to minimize the ergonomic risk of the critical workstation (station with greatest risk), and subject to this first objective, the second goal is to minimize the standard deviation (SD) from the ergonomic risks of the assembly line.…”

Section: Grasp For Solving the Min R_sd(r) Problemmentioning

confidence: 99%

“…The results given by the proposed GRASP are compared with those obtained by two mono-objective mathematical models because MILP does not support hierarchical objective functions. Specifically, the exact procedures are the MILP-1 and MILP-2, wherein their objectives are minimizing the maximum ergonomic risk of the assembly line and minimizing the risk dispersion between workstations, respectively (see Bautista et al, 2016a).…”

Section: Case Study: Nissan-9engmentioning

confidence: 99%

“…_ model (see Bautista et al, 2016a): a. Its objective function forces an equal sharing of risk between all workstations by minimizing the average absolute deviations from risks of workstations and without considering the maximum risk minimization.…”

Section: Milp-2mentioning

confidence: 99%

“…Later, a new mathematical model was proposed in order to reduce differences between workstations. Specifically, the new model minimized the average absolute deviation of the ergonomic risks of the set of workstations (see Bautista et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A case study at the Nissan Barcelona factory to minimize the ergonomic risk and its standard deviation in a mixed-model assembly line

Valhondo

Pozo

2018

Prog Artif Intell

Self Cite

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This work examines a balancing problem wherein the objective is to minimize both the ergonomic risk dispersion between the set of workstations of a mixed-model assembly line and the risk level of the workstation with the greatest ergonomic factor. A greedy randomized adaptive search procedure (GRASP) procedure is proposed to achieve these two objectives simultaneously. This new procedure is compared against two mixed integer linear programs: the MILP-1 model that minimizes the maximum ergonomic risk of the assembly line and the MILP-2 model that minimizes the average deviation from ergonomic risks of the set of workstations on the line. The results from the case study based on the automotive sector indicate that the proposed GRASP procedure is a very competitive and promising tool for further research.

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Section: Mathematical Model: Min R_sd(r)mentioning

confidence: 99%

Section: Grasp For Solving the Min R_sd(r) Problemmentioning

confidence: 99%

Section: Case Study: Nissan-9engmentioning

confidence: 99%

Section: Milp-2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A case study at the Nissan Barcelona factory to minimize the ergonomic risk and its standard deviation in a mixed-model assembly line

Valhondo

Pozo

2018

Prog Artif Intell

Self Cite

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Human factors’ complexity measurement of human‐based station of assembly line

Fan

Zhao

et al. 2018

Hum Ftrs & Erg Mfg Svc

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Cognitive heterogeneity causes operation time variability and high human error rates at humanbased stations of assembly lines. It will affect the performance of the production activities, the design process of which depends on reliable estimate of operation time. To quantify operator cognition, we measured the human factors' complexity of human-based stations using an information entropy approach in aspect of operation time. Initially, the influence of the operators' cognition on the operation time is analyzed. Based on the analysis, the operation time model that considers cognition is presented. Then, the operation time model is modified by applying a correction method using the behavior correction factor, and, finally, the human factors' complexity measurement model is built. A case study is used to illustrate the application of the proposed method in order to measure the influence of human cognition on the performance of the human-based station. K E Y W O R D Sassembly line, complexity measurement, human cognition, human factors, human-based station

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A bi‐objective ergonomic assembly line balancing model with conic scalarization method

Yetkin

Kahya

2022

Hum Ftrs & Erg Mfg Svc

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The most important factor affecting efficiency and ergonomic risk levels in an assembly line design is the problem of assigning certain tasks to certain stations, namely the assembly line balancing problem. In the literature, assembly line balancing problem has often been studied, but studies that consider ergonomic risks are deficient. Recently, it has been one of the issues that have started to attract great attention with the realization of health problems caused by assembly lines. To this end, in this study, a bi‐objective mathematical model is developed that considers balancing assembly line station time and ergonomic risk levels, simultaneously. It is aimed to minimize both station time and the total deviations of ergonomic risk scores for the stations. Weighted sum and conic scalarization methods are applied to solve the bi‐objective model. To analyze the outcomes of the developed model, an application is proposed and tested on a real industrial case, at a home appliance assembly line. The deployment of the OMAX method is a contribution to the literature since it shows an analysis tool which evaluates the results of assembly line balancing. This method evaluates the performance of the stations based on different criteria such as station time and ergonomic risk. The number of high‐risk stations is obtained as 13 in the single‐objective model aiming to minimize the station time, while it is found to be nine in the bi‐objective model solved with CSM, without an increase in the total number of stations.

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Maximizing comfort in Assembly Lines with temporal, spatial and ergonomic attributes

Cited by 31 publications

References 15 publications

A case study at the Nissan Barcelona factory to minimize the ergonomic risk and its standard deviation in a mixed-model assembly line

A case study at the Nissan Barcelona factory to minimize the ergonomic risk and its standard deviation in a mixed-model assembly line

Human factors’ complexity measurement of human‐based station of assembly line

A bi‐objective ergonomic assembly line balancing model with conic scalarization method

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