Optimization of assignment problems in production lines with different skilled labor levels

In order to serve the purpose of the study, two different mathematical models which belong to production line to were developed to assign workforces to the jobs who have different skills. The assignment model had been examined on a product that produced in a specified welding line in the automotive...

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Veröffentlicht in:International Advanced Researches and Engineering Journal 2019-08, Vol.3 (2), p.123-136
Hauptverfasser: DURMAZ, Hazel, KOYUNCU, Melik
Format: Artikel
Sprache:eng
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Zusammenfassung:In order to serve the purpose of the study, two different mathematical models which belong to production line to were developed to assign workforces to the jobs who have different skills. The assignment model had been examined on a product that produced in a specified welding line in the automotive industry. All labor levels related to the product were analyzed by time study firstly and then the collected data were analyzed. New skill levels of workers, skill matrix were determined according to skill requirements (min. and max.) of the jobs. All collected data which come from time studies and skill matrix were used as an input parameter in the mathematical model that aim to minimize the unit production cost. Reports of mathematical models showed that it is necessary to assign right skilled worker to the job to minimize the production cost and increase the efficiency online. In this case the implementation was made by selecting a product in the welding manufacturing line. In the line, there are 5 stations that have 14 jobs and 27 operators. A modeling has been carried out in which the operators can be assigned to jobs under optimum conditions, based on assignment criterias. Models of sensitivity analyzes and statistic report results obtained the importance of the differences in the level of competence and the effect on the duration of the job as well as the decrease in the labor costs by assigning the correct workforce to the right job and by assigning right conditions to the model.
ISSN:2618-575X
2618-575X
DOI:10.35860/iarej.501847