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ISSN (Print) 1570-1166 - ISSN (Online) 1572-9214
• On Compromise Mixed Allocation in Multivariate Stratified Sampling with Random Parameters
• Abstract: Abstract For estimating the population mean Clark and Steel (Stat. 49, 1970–207 2000) worked out the optimum allocation of sample sizes to strata and stages with simple additional constraints to use different type of allocations in different strata. Ahsan et al. (Aligarh J. Statist. 25, 87–97 2005) used the same idea to work out optimum allocation in univariate stratified sampling and called it a ‘Mixed Allocation’. Later on Varshney and Ahsan (J. Indian Soc. Agr. Stat. 65(3), 291–296, 2011) extended this work for multivariate stratified sampling and called it a compromise mixed allocation. This article presents a more realistic approach to the compromise mixed allocation by formulating the problem as a Stochastic Nonlinear Programming Problem in which the stratum-wise measurement costs and the sample stratum standard deviations are independent random variables with known probability distributions. The application of this approach is exhibited through a numerical example with normal distributions of the random parameters. The proposed compromise mixed allocation is compared with some other well known compromise allocations available in multivariate stratified sampling literature. It is found that the author’s proposed compromise mixed allocation is the most efficient allocation among the discussed allocations. A simulation study is also carried out to support the claim made by the authors on the basis of the results of the numerical example.
PubDate: 2013-12-03

• Reconstructing Convex Matrices by Integer Programming Approaches
• Abstract: Abstract We consider the problem of reconstructing two-dimensional convex binary matrices from their row and column sums with adjacent ones. Instead of requiring the ones to occur consecutively in each row and column, we maximize the number of adjacent ones. We reformulate the problem by using integer programming and we develop approximate solutions based on linearization and convexification techniques.
PubDate: 2013-12-01

• Generalized Univex Functions in Nonsmooth Multiobjective Optimization
• Abstract: Abstract In this paper, we have considered a nonsmooth multiobjective optimization problem where the objective and constraint functions involved are directionally differentiable. A new class of generalized functions (d − ρ − η − θ)-type I univex is introduced which generalizes many earlier classes cited in literature. Based upon these generalized functions, we have derived weak, strong, converse and strict converse duality theorems for mixed type multiobjective dual program in order to relate the efficient and weak efficient solutions of primal and dual problem.
PubDate: 2013-12-01

• Minimum Cost Compromise Mixed Allocation
• Abstract: Abstract When more than one (say p) characteristics in multivariate stratified population are defined on each unit of the population, the individual optimum allocations may differ widely and can not be used practically. Moreover, there may be a situation such that no standard allocation is advisable to all the strata, for one reason or another. In such a situation, Clark and Steel (J R Stat Soc, Ser D Stat 49(2):197–207, 2000) suggested that different allocations may be used for different groups of strata having some common characteristics for double sampling in stratification. Later on, Ahsan et al. (Aligarh J Stat 25:87–97, 2005) used the same concept in univariate stratified sampling. They minimized the variance of the stratified sample mean for a fixed cost to obtain an allocation and called this allocation “mixed allocation”. In the present paper, a “compromise mixed allocation” is worked out for the fixed precisions of the estimates of the p-population means of a multivariate stratified population. A numerical example is also presented.
PubDate: 2013-12-01

• Generalized Algorithm for Restricted Weak Composition Generation
• Abstract: Abstract This paper presents a new algorithm that arrives at a generalized solution for the generation of restricted weak compositions of n-parts. In particular, this generalized algorithm covers many commonly sought compositions such as bounded compositions, restricted compositions, weak compositions, and restricted part compositions. Introduced is an algorithm for generating generalized types of restricted weak compositions called first-order, and second-order restricted weak compositions.
PubDate: 2013-12-01

• Optimum Allocation in Two-Stage Stratified Randomized Response Model
• Abstract: Abstract In the present paper a two-stage stratified Warner’s randomized response model is used to determine the optimum allocation in the presence of non-response. The problem is formulated as a Nonlinear Programming Problem. A complete method of solution of the formulated problem is proposed. Two numerical examples are worked out to illustrate the computational details of the proposed method.
PubDate: 2013-12-01

• Designing WDM Optical Networks Using Branch-and-Price
• Abstract: Abstract In this paper, we present an exact solution procedure for the design of two-layer wavelength division multiplexing (WDM) optical networks with wavelength changers and bifurcated flows. This design problem closely resembles the traditional multicommodity flow problem, except that in the case of WDM optical networks, we are concerned with the routing of multiple commodities in two network layers. Consequently, the corresponding optimization models have to deal with two types of multicommodity variables defined for each of the network layers. The proposed procedure represents one of the first branch-and-price algorithms for a general WDM optical network setting with no assumptions on the number of logical links that can be established between nodes in the network. We apply our procedure in a computational study with four different network configurations. Our results show that for the three tested network configurations our branch-and-price algorithm provides solutions that are on average less than 5 % from optimality. We also provide a comparison of our branch-and-price algorithm with two simple variants of the upper bounding heuristic procedure HLDA that is commonly used for WDM optical network design.
PubDate: 2013-12-01

• A PTAS for a Particular Case of the Two-machine Flow Shop with Limited Machine Availability
• Abstract: Abstract In this paper we develop a polynomial-time approximation scheme for a particular case of the two-machine flow shop scheduling problem with several availability constraints on the second machine under the resumable scenario.
PubDate: 2013-11-16

• Goal Directed Programming for Determining Process Efficiency Using Data Envelopment Analysis
• Abstract: Abstract There are numerous measurement methods for process performance control. One of which, widely used in quality control programs, is process capability (C p ) index. The advantage of this index is due to the high amount of information extracted from it. Since C p is independent from a particular measurement unit it can be used to compare several quite different processes. While the relative efficiency of the process performance based on the C p for a period is satisfying, the process may lose efficiency in the next period for variety of reasons and could not keep up with the standard limits. The objective of this paper is to develop a new approach for measuring the relative efficiency of peer decision making units (DMUs) based upon process capability indices. A case study demonstrates the applicability of the proposed approach.
PubDate: 2013-11-07

• Mixed Type Duality in Multiobjective Fractional Programming Under Generalized ρ-univex Function
• Abstract: Abstract In this paper, three approaches given by Dinklebaeh (Manag Sci 13(7):492–498, 1967) and Jagannathan (Z Oper Res 17:618–630, 1968) for both primal and mixed type dual of a non differentiable multiobjective fractional programming problem in which the numerator of objective function contains square root of positive semi definite quadratic form are introduced. Also, the necessary and sufficient conditions of efficient solution for fractional programming are established and a parameterizations technique is used to established duality results under generalized ρ-univexity assumption.
PubDate: 2013-10-24

• An Optimal Chance Constraint Multivariate Stratified Sampling Design Using Auxiliary Information
• Abstract: Abstract When we are dealing with multivariate problem then we need an allocation which is optimal for all the characteristics in some sense because the individual optimum allocations usually differ widely unless the characteristics are highly correlated. So an allocation called “Compromise allocation” is to be worked out suggested by Cochran. When auxiliary information is also available, it is customary to use it to increase the precision of the estimates. Moreover, for practical implementation of an allocation, we need integer values of the sample sizes. In the present paper the problem is to determine the integer optimum compromise allocation when the population means of various characteristics are of interest and auxiliary information is available for the separate and combined ratio and regression estimates. This paper considers the optimum compromise allocation in multivariate stratified sampling with non-linear objective function and probabilistic non-linear cost constraint. The probabilistic non-linear cost constraint is converted into equivalent deterministic one by using Chance Constrained programming. The formulated multi-objective nonlinear programming problem is solved by Fuzzy Goal programming approach and Chebyshev approximation. Numerical illustration is also given to show the practical utility of the approaches.
PubDate: 2013-10-24

• A Generic Interior-point Algorithm for Monotone Symmetric Cone Linear Complementarity Problems Based on a New Kernel Function
• Abstract: Abstract Kernel functions play an important role in defining new search directions for interior-point algorithms for solving monotone linear complementarity problems. In this paper we present a new kernel function which yields the complexity bounds ${\mathcal O}(\sqrt{r}\log r\log\frac{r}{\epsilon})$ and ${\mathcal O}(\sqrt{r}\log\frac{r}{\epsilon})$ for large-and small-update methods, respectively, which are currently the best known bounds for such methods.
PubDate: 2013-10-22

• Web Page Textual Color Contrast Compensation for CVD Users Using Optimization Methods
• Abstract: Abstract With this paper, we propose two methods for color contrast compensation of the textual information contained in a web page using numerical optimization. The optimization process can be reduced to the minimization of a single objective function which aims to achieve an on-the-fly compensation inducing a small amount of change in the original colors. Mass-spring system based optimization and CMA-ES metaheuristics are compared with the problem in order to assess their efficiency for compensating the loss. Experiments conducted on real and artificial datasets, prove the methods efficiency even with a small number of evaluations. Also, the methods behaviour is bound to the amount of compensation needed.
PubDate: 2013-10-19

• Optimum Allocation in Multivariate Stratified Random Sampling: A Modified Prékopa’s Approach
• Abstract: Abstract Considering the possible correlation between the characteristics (variables) in multivariate stratified random sampling, a modified Prékopa’s approach is suggested for the problem of optimum allocation in multivariate stratified random sampling. An example is solved by applying the proposed methodology.
PubDate: 2013-09-21

• An Iterated Local Search for the Budget Constrained Generalized Maximal Covering Location Problem
• Abstract: Abstract Capacitated covering models aim at covering the maximum amount of customers’ demand using a set of capacitated facilities. Based on the assumptions made in such models, there is a unique scenario to open a facility in which each facility has a pre-specified capacity and an operating budget. In this paper, we propose a generalization of the maximal covering location problem, in which facilities have different scenarios for being constructed. Essentially, based on the budget invested to construct a given facility, it can provide different service levels to the surrounded customers. Having a limited budget to open the facilities, the goal is locating a subset of facilities with the optimal opening scenario, in order to maximize the total covered demand and subject to the service level constraint. Integer linear programming formulations are proposed and tested using ILOG CPLEX. An iterated local search algorithm is also developed to solve the introduced problem.
PubDate: 2013-09-13

• A Recursive Algorithm for State Dependent GI/M/1/N Queue with Bernoulli-Schedule Vacation
• Abstract: Abstract In this paper, we study a renewal input working vacations queue with state dependent services and Bernoulli-schedule vacations. The model is analyzed with single and multiple working vacations. The server goes for exponential working vacation whenever the queue is empty and the vacation rate is state dependent. At the instant of a service completion, the vacation is interrupted and the server resumes a regular busy period with probability 1 − q (if there are customers in the queue), or continues the vacation with probability q (0 ≤ q ≤ 1). We provide a recursive algorithm using the supplementary variable technique to numerically compute the stationary queue length distribution of the system. Finally, using some numerical results, we present the parameter effect on the various performance measures.
PubDate: 2013-09-11

• Diffusion of Active Ingredients in Textiles
• Abstract: Abstract Most practical textile models are based on a two scale approach: a one-dimensional fiber model and a fabric model, see Fan et al. (Commun Comput Phys 4(4):929–948, 2008). No meso-level is used in between, i.e. the yarn scale is neglected in this setup. For dense textile substrates this seems appropriate as the yarns connect everywhere, but for loose fabrics or scrims this approach cannot be kept. Specifically when one is interested in tracking an active component released by the fibers, the yarn level plays an important role. This is because the saturation vapor pressure will influence the release rate from the fibers, and its value will vary over the yarn cross-section. Therefore, in this work we present a three step multiscale model: the active component is tracked in the fiber, the yarn, and finally at the fabric level. At the fiber level a one-dimensional reduction to a non-linear diffusion equation is performed, and solved on an as needed basis. At the yarn level both a two-dimensional or a one-dimensional model can be applied, and finally the yarn result is upscaled to the fabric level.
PubDate: 2013-09-01

• Bound Analysis Through HDMR for Multivariate Data Modelling - CMMSE
• Abstract: Abstract Multivariate data modelling problems consist of a number of nodes with associated function (class) values. The main purpose of these problems is to construct an analytical model to represent the characteristics of the problem under consideration. Because the devices, tools, and/or algorithms used to collect the data may have incapabilities or limited capabilities, the data set is likely to contain unavoidable errors. That is, each component of data is reliable only within an interval which contains the data value. To this end, when an analytical structure is needed for the given data, a band structure should be determined instead of a unique structure. As the multivariance of the given data set increases, divide–and–conquer methods become important in multivariate modelling problems. HDMR based methods allow us to partition the given multivariate data into less variate data sets to reduce the complexity of the given problem. This paper focuses on Interval Factorized HDMR method developed to determine an approximate band structure for a given multivariate data modelling problem having uncertainties on its nodes and function values.
PubDate: 2013-09-01

• A New Predicting Method for Long-Term Photovoltaic Storage Using Rescaled Range Analysis: Application to Two Algerian Sites
• Abstract: Abstract A new predicting approach of long-term storage capacity for autonomous PV installations has been developed using the rescaled range analysis (R/S). The method consists mainly in establishing a mathematical law between the (R/S) τ ratio and the time period τ. The method has been tested over one year for two autonomous PV systems located in the huge desert of Algeria. Data used are converted solar energy which are not stationary. The experimental results show that even if the condition of stationarity is not satisfied, the rescaled range is well described by a power function of the time, this is possible by introducing a new exponent E. Using the power law the PV storage capacity is predicted for periods ranging from 1 to 5 years. The obtained results show that for an energy demand equalling the mean of converted energy, a storage of several months is needed to obtain the autonomy of the PV systems, consequently it will be too expensive to set-up such PV installations. Thus, an optimization method has been proposed to reduce the size of the storage. Application of this method for the two studied systems has led to significant reduction of the PV storage size.
PubDate: 2013-09-01

• New Optimization Techniques in Engineering
• PubDate: 2013-04-27

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