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Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions

This paper proposed a new general probabilistic multi-item, single-source inventory model with varying mixture shortage cost under two restrictions. One of them is on the expected varying backorder cost and the other is on the expected varying lost sales cost. This model is formulated to analyze how...

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Detalles Bibliográficos
Autor principal: Fergany, Hala A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987759/
https://www.ncbi.nlm.nih.gov/pubmed/27588244
http://dx.doi.org/10.1186/s40064-016-2962-2
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author Fergany, Hala A.
author_facet Fergany, Hala A.
author_sort Fergany, Hala A.
collection PubMed
description This paper proposed a new general probabilistic multi-item, single-source inventory model with varying mixture shortage cost under two restrictions. One of them is on the expected varying backorder cost and the other is on the expected varying lost sales cost. This model is formulated to analyze how the firm can deduce the optimal order quantity and the optimal reorder point for each item to reach the main goal of minimizing the expected total cost. The demand is a random variable and the lead time is a constant. The demand during the lead time is a random variable that follows any continuous distribution, for example; the normal distribution, the exponential distribution and the Chi square distribution. An application with real data is analyzed and the goal of minimization the expected total cost is achieved. Two special cases are deduced.
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spelling pubmed-49877592016-09-01 Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions Fergany, Hala A. Springerplus Research This paper proposed a new general probabilistic multi-item, single-source inventory model with varying mixture shortage cost under two restrictions. One of them is on the expected varying backorder cost and the other is on the expected varying lost sales cost. This model is formulated to analyze how the firm can deduce the optimal order quantity and the optimal reorder point for each item to reach the main goal of minimizing the expected total cost. The demand is a random variable and the lead time is a constant. The demand during the lead time is a random variable that follows any continuous distribution, for example; the normal distribution, the exponential distribution and the Chi square distribution. An application with real data is analyzed and the goal of minimization the expected total cost is achieved. Two special cases are deduced. Springer International Publishing 2016-08-16 /pmc/articles/PMC4987759/ /pubmed/27588244 http://dx.doi.org/10.1186/s40064-016-2962-2 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Fergany, Hala A.
Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions
title Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions
title_full Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions
title_fullStr Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions
title_full_unstemmed Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions
title_short Probabilistic multi-item inventory model with varying mixture shortage cost under restrictions
title_sort probabilistic multi-item inventory model with varying mixture shortage cost under restrictions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987759/
https://www.ncbi.nlm.nih.gov/pubmed/27588244
http://dx.doi.org/10.1186/s40064-016-2962-2
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