Some Alternative Methods of Finding the Optimal Order Quantity in Inventory Models

Chandra, Ayan; Mukherjee, Sarit

doi:10.1177/0008068320050110

Cited by 3 publications

(2 citation statements)

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“…Kyparisis and Koulamas ( 2018 ) addressed the newsvendor problem for quadratic utility function. Khouja ( 1995 ), Chandra and Mukherjee ( 2005 ), among others, considered optimisation of reliability function of the stochastic cost. In this paper, we consider generalisation of the classical newsvendor model of Ghosh et al ( 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Non-parametric generalised newsvendor model

Ghosh

Mukhoti

2022

Ann Oper Res

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In the present paper we generalise the classical newsvendor problem for critical perishable commodities having more severe costs than its linear alternative. Piece wise polynomial cost functions are introduced to accommodate the excess severity. Stochastic demand is assumed to follow a completely unknown probability distribution. Non parametric estimator of the optimal order quantity has been developed from an estimating equation using a random sample. Strong consistency of the estimator is proved for unique optimal order quantity and the result is extended for multiple solutions. Simulation results indicate that non parametric estimator is efficient in terms of mean square error. Real life application of the proposed non-parametric estimator has been demonstrated with Avocado demand in the United States of America and Covid-19 test kit demand during second wave of SARS-COV2 pandemic across 86 countries.

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Section: Introductionmentioning

confidence: 99%

Non-parametric generalised newsvendor model

Ghosh

Mukhoti

2022

Ann Oper Res

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“…Kyparisis and Koulamas [2018] addressed the newsvendor problem for quadratic utility function. Khouja [1995], Chandra and Mukherjee [2005], among others, considered optimisation of reliability function of the stochastic cost. In this paper, we consider generalisation of the classical newsvendor problem by modelling the severity of shortage and excess costs.…”

Section: Introductionmentioning

confidence: 99%

Non-parametric generalised newsvendor model

Ghosh¹,

Mukhoti²

2021

Preprint

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In classical newsvendor model, piece-wise linear shortage and excess costs are balanced out to determine the optimal order quantity. However, for critical perishable commodities, severity of the costs may be much more than linear. In this paper we discuss a generalisation of the newsvendor model with piece-wise polynomial cost functions to accommodate their severity. In addition, the stochastic demand has been assumed to follow a completely unknown probability distribution. Subsequently, non-parametric estimator of the optimal order quantity has been developed from a random polynomial type estimating equation using a random sample on demand. Strong consistency of the estimator has been proven when the true optimal order quantity is unique. The result has been extended to the case where multiple solutions for optimal order quantity are available. Probability of existence of the estimated optimal order quantity has been studied through extensive simulation experiments. Simulation results indicate that the non-parametric method provides robust yet efficient estimator of the optimal order quantity in terms of mean square error.

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