 EPQ models with bivariate random imperfect proportions and learning-dependent production and demand ratesS. Ganesan and R. Uthayakumar Journal of Management Analytics, 2021, vol. 8, issue 1, 134-170 Abstract: In this paper, three production inventory models are constructed for an imperfect manufacturing system by considering a warm-up production run, shortages during the hybrid maintenance period, and the rework of imperfect items. The proportions of imperfect items produced during the warm-up and regular production runs are random and they are represented using a bivariate random variable. The shortage quantity is partially backordered and the supply of backorder quantity is planned simultaneously with regular demand satisfaction. The learning models are designed to accommodate the different learning capabilities of workers in unit production time during warm-up and regular production periods. The production and demand rates of these models are made dependent on the learning exponents. As the resulting models are highly nonlinear in the decision variable, they are optimized using a genetic algorithm. The models are illustrated using numerical examples and sensitivity studies are performed to find the influence of the key parameters. Date: 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:tjmaxx:v:8:y:2021:i:1:p:134-170 Ordering information: This journal article can be ordered from DOI: 10.1080/23270012.2020.1818320 Access Statistics for this article Journal of Management Analytics is currently edited by Li Xu More articles in Journal of Management Analytics from Taylor & Francis Journals |
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