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Multi-choice stochastic transportation problem involving general form of distributions

Many authors have presented studies of multi-choice stochastic transportation problem (MCSTP) where availability and demand parameters follow a particular probability distribution (such as exponential, weibull, cauchy or extreme value). In this paper an MCSTP is considered where availability and dem...

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Detalles Bibliográficos
Autores principales: Quddoos, Abdul, ull Hasan, Md Gulzar, Khalid, Mohammad Masood
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194308/
https://www.ncbi.nlm.nih.gov/pubmed/25332865
http://dx.doi.org/10.1186/2193-1801-3-565
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author Quddoos, Abdul
ull Hasan, Md Gulzar
Khalid, Mohammad Masood
author_facet Quddoos, Abdul
ull Hasan, Md Gulzar
Khalid, Mohammad Masood
author_sort Quddoos, Abdul
collection PubMed
description Many authors have presented studies of multi-choice stochastic transportation problem (MCSTP) where availability and demand parameters follow a particular probability distribution (such as exponential, weibull, cauchy or extreme value). In this paper an MCSTP is considered where availability and demand parameters follow general form of distribution and a generalized equivalent deterministic model (GMCSTP) of MCSTP is obtained. It is also shown that all previous models obtained by different authors can be deduced with the help of GMCSTP. MCSTP with pareto, power function or burr-XII distributions are also considered and equivalent deterministic models are obtained. To illustrate the proposed model two numerical examples are presented and solved using LINGO 13.0 software package.
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spelling pubmed-41943082014-10-20 Multi-choice stochastic transportation problem involving general form of distributions Quddoos, Abdul ull Hasan, Md Gulzar Khalid, Mohammad Masood Springerplus Research Many authors have presented studies of multi-choice stochastic transportation problem (MCSTP) where availability and demand parameters follow a particular probability distribution (such as exponential, weibull, cauchy or extreme value). In this paper an MCSTP is considered where availability and demand parameters follow general form of distribution and a generalized equivalent deterministic model (GMCSTP) of MCSTP is obtained. It is also shown that all previous models obtained by different authors can be deduced with the help of GMCSTP. MCSTP with pareto, power function or burr-XII distributions are also considered and equivalent deterministic models are obtained. To illustrate the proposed model two numerical examples are presented and solved using LINGO 13.0 software package. Springer International Publishing 2014-09-27 /pmc/articles/PMC4194308/ /pubmed/25332865 http://dx.doi.org/10.1186/2193-1801-3-565 Text en © Quddoos et al.; licensee Springer. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
spellingShingle Research
Quddoos, Abdul
ull Hasan, Md Gulzar
Khalid, Mohammad Masood
Multi-choice stochastic transportation problem involving general form of distributions
title Multi-choice stochastic transportation problem involving general form of distributions
title_full Multi-choice stochastic transportation problem involving general form of distributions
title_fullStr Multi-choice stochastic transportation problem involving general form of distributions
title_full_unstemmed Multi-choice stochastic transportation problem involving general form of distributions
title_short Multi-choice stochastic transportation problem involving general form of distributions
title_sort multi-choice stochastic transportation problem involving general form of distributions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194308/
https://www.ncbi.nlm.nih.gov/pubmed/25332865
http://dx.doi.org/10.1186/2193-1801-3-565
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