Fuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages

In this paper, a fuzzy inventory model with a Weibull deterioration rate, a quadratic demand rate, and a variable holding cost under permissible shortages has been developed. The deterioration rate is expressed by a two-parameter Weibull distribution. During a shortage, some buyers wait for...

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Main Authors: Poswal Preety, Chauhan Anand, Rajoria Yogendra Kumar, Boadh Rahul, Goel Abhinav
Format: Article
Language:English
Published: University of Belgrade 2022-01-01
Series:Yugoslav Journal of Operations Research
Subjects:
Online Access:http://www.doiserbia.nb.rs/img/doi/0354-0243/2022/0354-02432204453P.pdf
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author Poswal Preety
Chauhan Anand
Rajoria Yogendra Kumar
Boadh Rahul
Goel Abhinav
author_facet Poswal Preety
Chauhan Anand
Rajoria Yogendra Kumar
Boadh Rahul
Goel Abhinav
author_sort Poswal Preety
collection DOAJ
description In this paper, a fuzzy inventory model with a Weibull deterioration rate, a quadratic demand rate, and a variable holding cost under permissible shortages has been developed. The deterioration rate is expressed by a two-parameter Weibull distribution. During a shortage, some buyers wait for the actual product, while others do not. This shortfall is considered partially backlogged in this model. Some buyers wait for the actual product during such shortages, but many do not. Therefore, partially backlogged shortages are taken into account in this approach. In a traditional inventory model, all parameters such as purchasing cost, shortage cost, holding cost, etc. are predetermined. However, there will be some variations. As a result, fuzzy factors are more accurate to deal with the real world’s problems. This research attempts to cut down the cost in a fuzzy environment by using quadratic demand, shortage, Weibull deterioration rate, and variable holding cost. Costs such as ordering, shortage, and deterioration are addressed as pentagonal fuzzy numbers that are defuzzified using a graded mean representation approach. Finally, sensitivity analysis was carried out to investigate the influence of cost parameters on total inventory cost. A numerical example is used to validate the proposed model in a real-world system.
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spelling doaj.art-5293c48d29bd4f5f8904eaaacb0d11c62023-03-10T08:08:02ZengUniversity of BelgradeYugoslav Journal of Operations Research0354-02431820-743X2022-01-0132445347010.2298/YJOR220115021P0354-02432204453PFuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortagesPoswal Preety0Chauhan Anand1Rajoria Yogendra Kumar2Boadh Rahul3Goel Abhinav4Department of Mathematics, K.R. Mangalam University, Gurgaon, Haryana, IndiaDepartment of Mathematics, Graphic Era Deemed University, Dehradun, U.K, IndiaDepartment of Mathematics, School of Basic and Applied Science, K.R. Mangalam University, Gurgaon, Haryana, IndiaDepartment of Mathematics, K.R. Mangalam University, Gurgaon, Haryana, IndiaDepartment of Mathematics, Graphic Era Deemed University, Dehradun, U.K, IndiaIn this paper, a fuzzy inventory model with a Weibull deterioration rate, a quadratic demand rate, and a variable holding cost under permissible shortages has been developed. The deterioration rate is expressed by a two-parameter Weibull distribution. During a shortage, some buyers wait for the actual product, while others do not. This shortfall is considered partially backlogged in this model. Some buyers wait for the actual product during such shortages, but many do not. Therefore, partially backlogged shortages are taken into account in this approach. In a traditional inventory model, all parameters such as purchasing cost, shortage cost, holding cost, etc. are predetermined. However, there will be some variations. As a result, fuzzy factors are more accurate to deal with the real world’s problems. This research attempts to cut down the cost in a fuzzy environment by using quadratic demand, shortage, Weibull deterioration rate, and variable holding cost. Costs such as ordering, shortage, and deterioration are addressed as pentagonal fuzzy numbers that are defuzzified using a graded mean representation approach. Finally, sensitivity analysis was carried out to investigate the influence of cost parameters on total inventory cost. A numerical example is used to validate the proposed model in a real-world system.http://www.doiserbia.nb.rs/img/doi/0354-0243/2022/0354-02432204453P.pdfeoq modeldeteriorationweibull deterioration ratevariable dependent demandshortagespentagonal numbergraded mean integration method
spellingShingle Poswal Preety
Chauhan Anand
Rajoria Yogendra Kumar
Boadh Rahul
Goel Abhinav
Fuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages
Yugoslav Journal of Operations Research
eoq model
deterioration
weibull deterioration rate
variable dependent demand
shortages
pentagonal number
graded mean integration method
title Fuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages
title_full Fuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages
title_fullStr Fuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages
title_full_unstemmed Fuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages
title_short Fuzzy optimization model of two parameter Weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages
title_sort fuzzy optimization model of two parameter weibull deteriorating rate with quadratic demand and variable holding cost under allowable shortages
topic eoq model
deterioration
weibull deterioration rate
variable dependent demand
shortages
pentagonal number
graded mean integration method
url http://www.doiserbia.nb.rs/img/doi/0354-0243/2022/0354-02432204453P.pdf
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