Non-parametric generalised newsvendor model

Soham Ghosh () and Sujay Mukhoti
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Soham Ghosh: Indian Institute of Technology Indore

Annals of Operations Research, 2023, vol. 321, issue 1, No 10, 266 pages

Abstract: Abstract 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.

Keywords: Stochastic programming; Non-parametric estimation; Strong consistency; Monte-Carlo simulation; Newsvendor problem; Non-linear optimisation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-05112-5

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