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| 001 | 9.821334 |
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| 003 | CaOODSP |
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| 005 | 20240219183446 |
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| 007 | cr ||||||||||| |
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| 008 | 160719s2013 onc|||||o f000 0 eng d |
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| 040 | |aCaOODSP|beng |
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| 041 | |aeng|bfre |
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| 043 | |an-cn--- |
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| 086 | 1 |aD68-6/159-2013E-PDF |
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| 100 | 1 |aPall, Raman. |
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| 245 | 13|aAn exposition on solving the joint level of repair analysis-spares problem using a multi-objective genetic algorithm |h[electronic resource] / |cby Raman Pall, Slawomir Wesolkowski, and Matthew Dozois. |
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| 260 | |a[Ottawa] : |bDefence Research and Development Canada, |cc2013. |
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| 300 | |axiv, 36 p. : |btables, graphs. |
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| 490 | 1 |aTechnical Memorandum ; |v2013-159 |
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| 500 | |a"September 2013." |
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| 504 | |aIncludes bibliographical references. |
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| 520 | |aLevel of repair analysis (LORA) is often defined as the problem of determining whether a component should be repaired or discarded upon its failure, and the location in the repair network to do such work. A related problem is the determination of the optimal number of spare components for a given piece of equipment. The most common approaches in the literature on developing a possible spare provisioning decision model are simulation and mathematical programming. Although these two problems (LORA and spare provisioning) are interdependent, they are seldom solved simultaneously due to the complicating nature of the relationships between spare levels and system availability. The need to address LORA and the sparing problems simultaneously has attracted increased attention from the Department of National Defence (DND). In this technical memorandum, the use of a multi-objective genetic algorithm (specifically the Non-dominated Sorting Genetic Algorithm II) is proposed to solve this problem, with optimization objectives as minimizing repair costs (e.g., spare parts, spares transportation, spares storage) and maximizing operational availability. The approach uses a Monte Carlo simulation to generate scenarios based on a dataset which includes failures of the components and their associated times of failure. The objective functions are computed at each genetic algorithm generation based on all generated scenarios. |
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| 692 | 07|2gccst|aTechnical reports |
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| 693 | 07|aLevel of repair analysis |
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| 693 | 07|aMilitary logistics |
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| 700 | 1 |aWesolkowski, Slawomir. |
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| 700 | 1 |aDozoism Matthew. |
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| 710 | 2 |aDefence R&D Canada. |
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| 830 | #0|aTechnical memorandum (Defence R&D Canada)|v2013-159|w(CaOODSP)9.820564 |
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| 856 | 40|qPDF|s637 KB|uhttps://publications.gc.ca/collections/collection_2016/rddc-drdc/D68-6-159-2013-eng.pdf |
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