Economic lot sizing for unreliable production system with shortages
Neetu Singh,
Madhu Jain and
Nisha Arora
International Journal of Mathematics in Operational Research, 2015, vol. 7, issue 4, 464-483
Abstract:
The purpose of the present study is to analyse the optimal lot size in an unreliable single-machine production system with shortages. The production system is subject to failure due to machine breakdown. Breakdown times are considered to be according to Weibull distribution. It is assumed that the shortages are allowed and backlogged. During each production, the set-up preventive (regular) maintenance is performed. The corrective (i.e., emergency) maintenance is carried out immediately after breakdown. For the illustration purpose, numerical results are provided for the special cases. To obtain the optimal cost per unit time, we also employ the artificial neuro-fuzzy inference system (ANFIS) approach which has the learning capability of neural network as well as advantages of rule-base fuzzy system. It is noted that the results obtained by neuro-fuzzy technique are at par with the results computed by the analytical techniques.
Keywords: inventory modelling; economic lot sizing; ELS; unreliable machines; preventive maintenance; shortages; adaptive neuro-fuzzy inference system; ANFIS; neural networks; fuzzy logic; unreliable production systems; system failure; machine breakdown. (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=70198 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmore:v:7:y:2015:i:4:p:464-483
Access Statistics for this article
More articles in International Journal of Mathematics in Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().