Chance Constrained Approach for Treating Multicriterion Inventory Model with Random Variables in the Constraints

This paper presents the problematic period of neutrosophic inventory in an inaccurate and unsafe mixed environment. The purpose of this paper is to present demand as a neutrosophic random variable. For this model, a new method is developed for determining the optimal sequence size in the presence of neutrosophic random variables. Where to get optimality by gradually expressing the average value of integration. The newsvendor problem is used to describe the proposed model.

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Limit Distribution of Inventory Level of Perishable Inventory Model

Mathematical Problems in Engineering ◽

10.1155/2011/329531 ◽

2011 ◽

Vol 2011 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Hailing Dong ◽

Guochao Jiang

Keyword(s):

Explicit Expression ◽

Limit Distribution ◽

Random Variables ◽

Inventory Model ◽

Independent Random Variables ◽

General Distribution ◽

Inventory Level ◽

Perishable Items ◽

Perishable Inventory ◽

Existence Conditions

This paper studies a perishable inventory model, which assumes that each perishable item has finite lifetime, and only one item is consumed each time. The lifetimes of perishable items are independent random variables with the general distribution and so are the consumption internal. Under this assumption, by using backward equations and limit distribution of Markov skeleton processes, this paper obtains the existence conditions and the explicit expression of the limit distribution of the inventory level of perishable inventory model.

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Normal approximations to discrete unimodal distributions

Journal of Applied Probability ◽

10.1017/s0021900200115931 ◽

1986 ◽

Vol 23 (04) ◽

pp. 1013-1018

Author(s):

B. G. Quinn ◽

H. L. MacGillivray

Keyword(s):

Stationary Distribution ◽

Sufficient Conditions ◽

Random Variables ◽

Hypergeometric Distribution ◽

Death Process ◽

Normal Approximations ◽

Discrete Random Variables ◽

Birth Death

Sufficient conditions are presented for the limiting normality of sequences of discrete random variables possessing unimodal distributions. The conditions are applied to obtain normal approximations directly for the hypergeometric distribution and the stationary distribution of a special birth-death process.

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Random Variables

PsycEXTRA Dataset ◽

10.1037/e528352011-001 ◽

1989 ◽

Keyword(s):

Random Variables

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On the Mathematical Basis of Zelen’s Prerandomized Designs

Methods of Information in Medicine ◽

10.1055/s-0038-1635370 ◽

1985 ◽

Vol 24 (03) ◽

pp. 120-130 ◽

Cited By ~ 28

Author(s):

E. Brunner ◽

N. Neumann

Keyword(s):

Clinical Trial ◽

Normal Distribution ◽

Random Variables ◽

Small Samples ◽

Selection Effects ◽

Sample Sizes ◽

Mathematical Basis ◽

Additional Assumptions

SummaryThe mathematical basis of Zelen’s suggestion [4] of pre randomizing patients in a clinical trial and then asking them for their consent is investigated. The first problem is to estimate the therapy and selection effects. In the simple prerandomized design (PRD) this is possible without any problems. Similar observations have been made by Anbar [1] and McHugh [3]. However, for the double PRD additional assumptions are needed in order to render therapy and selection effects estimable. The second problem is to determine the distribution of the statistics. It has to be taken into consideration that the sample sizes are random variables in the PRDs. This is why the distribution of the statistics can only be determined asymptotically, even under the assumption of normal distribution. The behaviour of the statistics for small samples is investigated by means of simulations, where the statistics considered in the present paper are compared with the statistics suggested by Ihm [2]. It turns out that the statistics suggested in [2] may lead to anticonservative decisions, whereas the “canonical statistics” suggested by Zelen [4] and considered in the present paper keep the level quite well or may lead to slightly conservative decisions, if there are considerable selection effects.

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A Backlog Inventory Model During Restricted Sale Periods

Journal of the Operational Research Society ◽

10.1057/palgrave.jors.0470910 ◽

1996 ◽

Vol 47 (9) ◽

pp. 1192-1200 ◽

Cited By ~ 6

Author(s):

Rhonda L Aull-Hyde

Keyword(s):

Inventory Model

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Modified algorithm for fast bandwidth selection for kernel estimates of multidimensional probability densities

Izmeritel`naya Tekhnika ◽

10.32446/0368-1025it.2020-11-9-13 ◽

2020 ◽

pp. 9-13

Author(s):

A. V. Lapko ◽

V. A. Lapko

Keyword(s):

Probability Density ◽

Optimal Parameter ◽

Random Variables ◽

Kernel Functions ◽

Independent Random Variables ◽

Functional Dependencies ◽

Approximation Properties ◽

Probability Density Estimation ◽

Multidimensional Probability ◽

Selection Of

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.

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