6533b82bfe1ef96bd128df9e

RESEARCH PRODUCT

Improvement of Inventory Control under Parametric Uncertainty and Constraints

Konstantin N. NechvalUldis RozevskisNicholas A. NechvalMaris Purgailis

subject

Inventory controlMathematical optimizationNumerical analysisStatistical inferenceConstrained optimizationEquivalence principle (geometric)Extension (predicate logic)Pivotal quantityMathematicsParametric statistics

description

The aim of the present paper is to show how the statistical inference equivalence principle (SIEP), the idea of which belongs to the authors, may be employed in the particular case of finding the effective statistical decisions for the multi-product inventory problems with constraints. To our knowledge, no analytical or efficient numerical method for finding the optimal policies under parametric uncertainty for the multi-product inventory problems with constraints has been reported in the literature. Using the (equivalent) predictive distributions, this paper represents an extension of analytical results obtained for unconstrained optimization under parametric uncertainty to the case of constrained optimization. A numerical example is given.

yearjournalcountryeditionlanguage
2011-01-01
https://doi.org/10.1007/978-3-642-20267-4_15