Korespondensi: Lease Contracts with Servicing Strategy Model for Used Product Considering Crisp and Fuzzy Usage Rates Hennie Husniah Primary Author Bermawi P. Iskandar Primary Author Asep K. Supriatna Primary Author mixed material bibliography 2020 monographic en English
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 Abstract Leasing activity is becoming popular in recent years for several reasons. One among thereasons is in regards to extremely high cost of purchasing for some products or equipment, e.g. heavy equipment used in a mining industry and other manufactures usingsophisticated equipment. In these areas, due to the expensive production equipment,many operators adopt a leasing contract, rather than purchasing the equipment directly. Inthis paper, we study lease contracts for used product involving servicing strategy. Thelessor (agent) will give servicing strategies which allows more than one imperfect repair tothe lessee. A penalty cost incurred when the time required to perform a repair actionexceeds a predetermined target. The servicing strategy proposed reduces equipmentfailures and hence decreases total maintenance cost during the lease contract. We develop a mathematical model to find the optimal maintenance strategy, such as the coststructure and the maintenance degree during the lease period. We find the optimalmaintenance level and the threshold values for the servicing strategy such that to minimizethe expected total maintenance cost. Numerical examples are presented to illustrate theoptimal solution, firstly by considering crisp usage rates level and secondly by consideringfuzzy usage rates level. Servicing strategy fuzzy usagerate lease contract expected cost imperfect repair 600 Repository Fakultas Teknik Sistem Repository Elektronik Skripsi dan Penelitian Dosen Fakultas Teknik 658 HEN l KJITI.014 My Library 658 HEN l Korespondensi: Lease Contracts with Servicing Strategy Model for Used Product Considering Crisp and Fuzzy Usage Rates Korespondensi_-_12._Lease_Contracts_with_Servicing_Strategy_Model_for_Used_Product_Considering_Crisp 986 2023-02-01 15:24:54 2023-02-01 15:32:25 machine generated