scholarly journals Stochastic Fractional Programming Approach to a Mean and Variance Model of a Transportation Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
V. Charles ◽  
V. S. S. Yadavalli ◽  
M. C. L. Rao ◽  
P. R. S. Reddy
Keyword(s):  
Fractional Programming ◽  
Programming Model ◽  
Vital Role ◽  
Linear Functions ◽  
Probabilistic Constraints ◽  
Programming Approach ◽  
Variance Model ◽  
Linear Fractional Program ◽  
Non Linear ◽  
Mean And Variance

In this paper, we propose a stochastic programming model, which considers a ratio of two nonlinear functions and probabilistic constraints. In the former, only expected model has been proposed without caring variability in the model. On the other hand, in the variance model, the variability played a vital role without concerning its counterpart, namely, the expected model. Further, the expected model optimizes the ratio of two linear cost functions where as variance model optimize the ratio of two non-linear functions, that is, the stochastic nature in the denominator and numerator and considering expectation and variability as well leads to a non-linear fractional program. In this paper, a transportation model with stochastic fractional programming (SFP) problem approach is proposed, which strikes the balance between previous models available in the literature.

The Initial Return and Its Conditional Return Volatility: Evidence from the Chinese IPO Market

2014 ◽  
Vol 17 (04) ◽  
pp. 1450022 ◽  
Author(s):  
M. Monica Hussein ◽  
Zhong-Guo Zhou
Keyword(s):  
Time Series ◽  
Linear Functions ◽  
Return Volatility ◽  
Cross Sectional ◽  
Variance Model ◽  
Initial Return ◽  
Traditional Market ◽  
Mean And Variance ◽  
The Mean ◽  
Sectional Analysis

This paper investigates the monthly initial return and its conditional return volatility for Chinese IPOs. We find that the mean initial return (IR) and cross-sectional return volatility are highly auto- and cross-correlated, and time-varying. We propose a system of two simultaneous equations: a GARCH-in-mean (GARCH-M) process with an ARMA(1,1) adjustment in the residuals for the IR and an EGARCH process for the conditional return volatility, assuming that the IR and its conditional return volatility are linear functions of the same market, firm- and offer-specific characteristics. We find that the model captures both time-series and cross-sectional correlations at the mean and variance levels. Our findings suggest that the conditional return volatility affects the IR positively and significantly, in addition to the traditional market, firm- and offer-specific characteristics. IPOs with higher conditional return volatility, as a proxy for information asymmetry, tend to be underpriced more. The paper demonstrates the merit of using a conditional variance model, along with time series and cross-sectional analysis to price Chinese IPOs.

MATHEMATICAL PROGRAMMING APPROACH TO MULTIATTRIBUTE DECISION MAKING UNDER INTUITIONISTIC FUZZY ENVIRONMENTS

2008 ◽  
Vol 16 (04) ◽  
pp. 557-577 ◽  
Author(s):  
DENG-FENG LI ◽  
YONG-CHUN WANG
Keyword(s):  
Decision Making ◽  
Mathematical Programming ◽  
Fractional Programming ◽  
Programming Model ◽  
Comprehensive Evaluation ◽  
Intuitionistic Fuzzy Set ◽  
Programming Approach ◽  
Interval Numbers ◽  
Intuitionistic Fuzzy ◽  
Relative Closeness

There exists little investigation on multiattribute decision making under intuitionistic fuzzy environments although both crisp and fuzzy multiattribute decision making have achieved a great progress. In this paper, multiattribute decision making problems using intuitionistic fuzzy sets are investigated and the TOPSIS is further extended to develop one new methodology for solving such problems. In this methodology, an interval fractional programming model is constructed on the basis of the relative closeness coefficient using the TOPSIS. Comprehensive evaluation of each alternative, which may be described as an intuitionistic fuzzy set or interval number, is calculated using two auxiliary mathematical programming problems derived from the interval fractional programming model proposed in this paper. Optimal degrees of membership for alternatives are calculated to determine their ranking order using the concept of likelihood based on the ranking method of interval numbers. Implementation process of the method proposed in this paper is illustrated with a numerical example.

Solvent Polarity Studies of the Water+t-Butyl Alcohol and Water+t-Butylamine Binary Systems With the Solvatochromic Dyes Nile Red and Pyridinium-N-phenoxide Betaine, Refractometry and Permittivity Measurements

1994 ◽  
Vol 47 (9) ◽  
pp. 1771 ◽  
Author(s):  
PK Kipkemboi ◽  
AJ Easteal
Keyword(s):  
Binary Systems ◽  
Hydrophobic Interactions ◽  
Nile Red ◽  
Solvent Polarity ◽  
Butyl Alcohol ◽  
Linear Functions ◽  
Non Linear ◽  
Organic Cosolvent ◽  
Two Parameters ◽  
Polarity Parameters

The empirical solvent polarity parameters ENR and ET for the solvatochromic compounds Nile Red (1) and pyridinium-N-phenoxide betaine (2), respectively, have been determined as a function of composition for water+t -butyl alcohol and water+t-butylamine binary mixtures, over the whole composition range at 298 K. For both systems the two parameters vary with composition in a strongly non-linear fashion, and the polarity of the mixture decreases with increasing proportion of the organic cosolvent. The non-linear variation of the polarity parameters is attributed to water-cosolvent hydrophobic interactions at low cosolvent contents, and hydrogen-bonding interactions at higher cosolvent contents. Permittivity and refractive index have also been measured at 298 K for both systems, and both properties are strongly non-linear functions of composition.

Separate and/or simultaneous generations of non-linear functions inherent in non-ohmic resistivities

1976 ◽  
Vol 18 (1) ◽  
pp. 51-61
Author(s):  
Yasuhiro Kobayashi∗ ◽  
Masaaki Ohkita ◽  
Michio Inoue ◽  
Masao Nakamura
Keyword(s):  
Linear Functions ◽  
Non Linear

Iterative Parametric Approach for Quadratically Constrained Bi-Level Multiobjective Quadratic Fractional Programming

2020 ◽  
Vol 17 (11) ◽  
pp. 5046-5051
Author(s):  
Vandana Goyal ◽  
Namrata Rani ◽  
Deepak Gupta
Keyword(s):  
Fractional Programming ◽  
Optimization Problem ◽  
Programming Model ◽  
Pareto Optimal Solutions ◽  
Parametric Approach ◽  
Second Stage ◽  
Quadratic Fractional Programming ◽  
Upper Level ◽  
Quadratically Constrained ◽  
Constraint Method

The paper proposed an iterative parametric approach procedure for solving Bi-level Multiobjective Quadratic Fractional Programming model. The Model is divided into two levels-upper and lower. In the first stage of the approach, a set of pareto optimal solutions of upper Level is obtained by converting the problem into equivalent single non-fractional parametric objective optimization problem by using parametric vector and ε-constraint method. Then for the second stage, the solution of upper level is followed by the lower level decision maker while finding solution with the proposed algorithm to obtain the best preferred solution. A numerical example is solved in the last to validate the feasibility of the approach.

scholarly journals A Bilevel Stochastic Dynamic Programming Model to Assess the Value of Information on Actual Food Quality at Wholesale Markets

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xiangyu Hou ◽  
Rene Haijema ◽  
Dacheng Liu
Keyword(s):  
Dynamic Programming ◽  
Programming Model ◽  
Market Price ◽  
Programming Approach ◽  
Stochastic Dynamic ◽  
Wholesale Market ◽  
Dynamic Programming Approach ◽  
Additional Information ◽  
Similar Appearance ◽  
Inventory Decision

In the fresh produce wholesale market, the market price is determined by the total demand and supply. The price is stochastic, and either wholesaler or retailer has few influence on it. In the wholesaler’s inventory decision, the price’s uncertainty plays an important role as well as the uncertainty from the demand side: the wholesaler makes his decision based on the retailer’s ordering, which is influenced by the stochastic market price and the distribution of the consumer’s demand. In addition, when at the wholesale stage, the products show a similar quality of similar appearance. With more efforts being input, the wholesaler could detect and record more additional information than that reflected from the appearance. Based on this, he can classify the quality into different levels. No experience shows how the wholesaler could use the underlying quality information and how much this information could improve his profit. To describe and explore this problem, a bilevel dynamic programming approach is employed. We evaluate different strategies of using the underlying information, show the features of the optimal policy, develop heuristics, and discuss the influence of factors such as quality and market price. We also develop the managerial principles for the practical use.

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