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Mathematical modeling to standardize times in assembly processes: Application to four case studies
by
Colina, Eliezer
, Peña, Mario
, Morocho, Villie
, Siguenza-Guzman, Lorena
in
Assembly lines
/ assembly process
/ assembly process, standard time, regression model, optimization model, fuzzy logic model
/ Bicycles
/ Case studies
/ Circuit boards
/ Fuzzy logic
/ fuzzy logic model
/ Fuzzy systems
/ Mathematical analysis
/ Measuring instruments
/ Methodology
/ Motorcycles
/ Optimization
/ optimization model
/ Regression analysis
/ regression model
/ Regression models
/ standard time
/ Television
2021
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Mathematical modeling to standardize times in assembly processes: Application to four case studies
by
Colina, Eliezer
, Peña, Mario
, Morocho, Villie
, Siguenza-Guzman, Lorena
in
Assembly lines
/ assembly process
/ assembly process, standard time, regression model, optimization model, fuzzy logic model
/ Bicycles
/ Case studies
/ Circuit boards
/ Fuzzy logic
/ fuzzy logic model
/ Fuzzy systems
/ Mathematical analysis
/ Measuring instruments
/ Methodology
/ Motorcycles
/ Optimization
/ optimization model
/ Regression analysis
/ regression model
/ Regression models
/ standard time
/ Television
2021
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Mathematical modeling to standardize times in assembly processes: Application to four case studies
by
Colina, Eliezer
, Peña, Mario
, Morocho, Villie
, Siguenza-Guzman, Lorena
in
Assembly lines
/ assembly process
/ assembly process, standard time, regression model, optimization model, fuzzy logic model
/ Bicycles
/ Case studies
/ Circuit boards
/ Fuzzy logic
/ fuzzy logic model
/ Fuzzy systems
/ Mathematical analysis
/ Measuring instruments
/ Methodology
/ Motorcycles
/ Optimization
/ optimization model
/ Regression analysis
/ regression model
/ Regression models
/ standard time
/ Television
2021
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Mathematical modeling to standardize times in assembly processes: Application to four case studies
Journal Article
Mathematical modeling to standardize times in assembly processes: Application to four case studies
2021
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Overview
Purpose: This paper proposes model-based standard times estimates, using multiple linear regression, nonlinear optimization, and fuzzy systems in four real cases assembly lines. The work includes a description of the models and a comparison of their performance with values obtained using the conventional chronometer method. These models allow estimating standard times without reconducting field studies. Design/methodology/approach: For the development of the time study, the methodology applied by the International Labour Organization (ILO) was used as a baseline. This methodology is structured in three phases: selection of the case study, registration of the process by direct observation, and calculation/estimation of the standard time. The selected case studies belong to real assembly lines of motorcycles, television sets, printed circuit boards, and bicycles. Findings: In the motorcycle's assembly case, the study allowed constructing seven linear regression models to estimate standard times for assembling the front parts, and seven linear regression models to predict standard times for the rear parts of the different motorcycle types. Compared to the classical chronometer method, the results obtained never exceeded 10%. Regarding the case studies of assembling TV sets and PCBs, the study considered the construction of nonlinear optimization models that allow making appropriate predictions of the standard times in their assembly lines. Finally, for the bicycle assembly line, a fuzzy logic model to represent the standard time was constructed and validated. Research limitations/implications: For reasons of confidentiality of information, this work omitted the names of companies, services, and models of manufactured products. Originality/value: The literature consulted does not refer to the representation of standard time on assembly lines using mathematical models. The construction of these models with empirical data from actual assembly lines was a valuable aid to the companies involved in supporting activity planning.
Publisher
OmniaScience,Vicenc Fernandez
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