Integrated Optimization for Stock Levels and Cross-Training Schemes with Simulation-Based Genetic Algorithm

Hasan Huseyin Turan, Shaligram Pokharel, Andrei Sleptchenko, Tarek Y. Elmekkawy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

A 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.

Original languageBritish English
Title of host publicationProceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016
EditorsMary Yang, Hamid R. Arabnia, Leonidas Deligiannidis, Leonidas Deligiannidis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1158-1163
Number of pages6
ISBN (Electronic)9781509055104
DOIs
StatePublished - 17 Mar 2017
Event2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016 - Las Vegas, United States
Duration: 15 Dec 201617 Dec 2016

Publication series

NameProceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

Conference

Conference2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016
Country/TerritoryUnited States
CityLas Vegas
Period15/12/1617/12/16

Keywords

  • Cross-training
  • Discrete event simulation
  • Genetic Algorithm
  • Spare part logistics

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