Regression Model to Estimate Standard Time through Energy Consumption of Workers in Manual Assembly Lines under Moderate Workload
Author(s) -
Abdul Ayabar,
Jorge de la Riva,
Jaime Sánchez,
César Omar Balderrama Armendáriz
Publication year - 2015
Publication title -
journal of industrial engineering
Language(s) - English
Resource type - Journals
eISSN - 2314-4890
pISSN - 2314-4882
DOI - 10.1155/2015/382673
Subject(s) - workload , regression analysis , calorie , statistics , energy consumption , regression , linear regression , consumption (sociology) , work (physics) , simulation , mathematics , econometrics , operations management , engineering , computer science , medicine , mechanical engineering , social science , sociology , electrical engineering , endocrinology , operating system
We propose a Standard Time (ST) Estimate Model based on energy demand to attain more equal work distribution in manual assembly lines. The proposal was developed estimating the energy consumption by monitoring the heart rate (HR) of 84 people between 18 and 48 years old while performing repetitive activities under moderate workload (2.5–5.0 kilocalories/minute (Kcal/min)). Variables on one model were determined, which were based on energy consumption (EC) using the 13-variable Best-Subset function. Subsequently, a general equation for the Standard Time (ST) Estimate Model was calculated through lineal regression. Two significant variables were obtained: total kilocalories (Kcal tot.)/pieces and total Kcal/operation time (OT) for each station, which are included in a Standard Time Estimate Model. ST can be represented with a regression model measuring the total number of kilocalories consumed by workers and the OT, which can help companies to balance the cycle time in their assembly lines
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