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AuthorTuran, Hasan Huseyin
AuthorPokharel, Shaligram
AuthorSleptchenko, Andrei
AuthorElmekkawy, Tarek Y.
Available date2020-11-26T11:21:08Z
Publication Date2017
Publication NameProceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016
ResourceScopus
URIhttp://dx.doi.org/10.1109/CSCI.2016.0219
URIhttp://hdl.handle.net/10576/17086
AbstractA spare part supply system for repairable spares in a repair shop is modeled as a set of heterogeneous parallel servers that have the ability to repair only certain types of repairables. The proposed model minimizes the total cost of holding inventory for spare parts, cost for backorder arising from downtime of the system due to the lack of spare parts and the cost of crosstraining for servers. Simulation-based Genetic Algorithm (GA) is proposed to optimize inventory levels and to determine the best skill assignments to servers, i.e., cross-training schemes. When methodology's performance is compared with total enumeration, tight optimality gaps are obtained.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCross-training
Discrete event simulation
Genetic Algorithm
Spare part logistics
TitleIntegrated Optimization for Stock Levels and Cross-Training Schemes with Simulation-Based Genetic Algorithm
TypeConference Paper
Pagination1158-1163
dc.accessType Abstract Only


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