<?xml version="1.0" encoding="UTF-8"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-10-29T20:43:32Z</responseDate><request verb="GetRecord" identifier="oai:munzur-rdlopen.rawdatalibrary.net:munzur/3308" metadataPrefix="oai_dc">https://munzur-rdlopen.rawdatalibrary.net/oai/request</request><GetRecord><record><header><identifier>oai:munzur-rdlopen.rawdatalibrary.net:munzur/3308</identifier><datestamp>2026-10-28T15:39:29Z</datestamp><setSpec>openaire</setSpec><setSpec>com_munzur_1</setSpec><setSpec>col_munzur_2</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:title>An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times</dc:title><dc:creator>Serin, Faruk</dc:creator><dc:creator>Mete, Süleyman</dc:creator><dc:creator>Çelik, Erkan</dc:creator><dc:subject>Assembly line</dc:subject><dc:subject>Genetic algorithms</dc:subject><dc:subject>Re-balancing</dc:subject><dc:subject>Stochastic task times</dc:subject><dc:subject>U-type</dc:subject><dc:description>Purpose: Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times. Design/methodology/approach: In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times. Findings: The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost. Practical implications: Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case. Originality/value: As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.</dc:description><dc:publisher>Emerald Group Holdings Ltd.</dc:publisher><dc:date>2019</dc:date><dc:type>Article</dc:type><dc:identifier>https://rdlopen.rawdatalibrary.net/r/munzur/3308</dc:identifier><dc:identifier>https://doi.org/10.1108/aa-07-2018-106</dc:identifier><dc:identifier>0144-5154</dc:identifier><dc:language>en</dc:language><dc:relation>Assembly Automation</dc:relation><dc:rights>info:eu-repo/semantics/closedAccess</dc:rights><dc:type>info:eu-repo/semantics/article</dc:type><dc:identifier>https://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/3308</dc:identifier></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>