Balancing two-sided U-type assembly lines using modified particle swarm optimization algorithm

In this paper, a new two-sided U-type assembly line balancing (TUALB) procedure and a new algorithm based on the particle swarm optimization algorithm to solve the TUALB problem are proposed. The proposed approach minimizes the number of stations for a given cycle time as the primary objective and it minimizes the number of positions as a secondary objective. The proposed approach is illustrated with an example problem. In order to evaluate the efficiency of the proposed algorithm, the test problems available in the literature are used. The experimental results show that the proposed approach performs well.     

On differential equations with interval coefficients

In this study, a new approach is developed to solve the initial value problem for interval linear differential equations. In the considered problem, the coefficients and the initial values are constant intervals. In the developed approach, there is no need to define a derivative for interval-valued functions. All derivatives used in the approach are classical derivatives of real functions. The reason for this is that the solution of the problem is defined as a bunch of real functions. Such a solution concept is compatible also with the robust stability concept. Sufficient conditions are provided for the solution to be expressed analytically. In addition, on a numerical example, the solution obtained by the proposed approach is compared with the solution obtained by the generalized Hukuhara differentiability. It is shown that the proposed approach gives a new type of solution. The main advantage of the proposed approach is that the solution to the considered interval initial value problem exists and is unique, as in the real case.     

A deterministic approach to global box-constrained optimization

A deterministic global optimization algorithm for box-constrained problems is presented. The proposed approach is based on well-known non-uniform space covering technique. In the paper this approach is further elaborated. We propose a new techniques that enables a significant reduction of the search space by means of dropping parts of processed boxes. Also a new quadratic underestimation for the objective function based on hessian eigenvalues bounds is presented. It is shown how this underestimation can be improved by exploiting the first-order optimality conditions. In the experimental section we compare the proposed approach with existing methods and programming tools. Numerical tests indicate that the proposed algorithm is highly competitive with considered methods.     

A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty

Location planning for urban distribution centers is vital in saving distribution costs and minimizing traffic congestion arising from goods movement in urban areas. In this paper, we present a multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. The proposed approach involves identification of potential locations, selection of evaluation criteria, use of fuzzy theory to quantify criteria values under uncertainty and application of fuzzy TOPSIS to evaluate and select the best location for implementing an urban distribution center. Sensitivity analysis is performed to determine the influence of criteria weights on location planning decisions for urban distribution centers.The strength of the proposed work is the ability to deal with uncertainty arising due to a lack of real data in location planning for new urban distribution centers. The proposed approach can be practically applied by logistics operators in deciding on the location of new distribution centers considering the sustainable freight regulations proposed by municipal administrations. A numerical application is provided to illustrate the approach.     

有理曲线和曲面的分片多项式逼近

本文利用摄动的思想,以摄动有理曲线（曲面）的系数的无穷模作为优化目标,给出了用多项式曲线（曲面）逼近有理曲线（曲面）的一种新方法．同以前的各种方法相比,该方法不仅收敛而且具有更快的收敛速度,并且可以与细分技术相结合,得到有理曲线与曲面的整体光滑、分片多项式的逼近．     

The principle of minimum of partial local variations for determining convective flows in the numerical solution of one-dimensional nonlinear scalar hyperbolic equations

For the CABARET finite difference scheme, a new approach to the construction of convective flows for the one-dimensional nonlinear transport equation is proposed based on the minimum principle of partial local variations. The new approach ensures the monotonicity of solutions for a wide class of problems of a fairly general form including those involving discontinuous and nonconvex functions. Numerical results illustrating the properties of the proposed method are discussed.     

A parametric bootstrap approach for the equality of coefficients of variation

In this article, a parametric bootstrap approach for testing the equality of coefficient of variation of $k$ normal populations is proposed. Simulations show that the actual size of our proposed test is close to the nominal level, irrespective of the number of populations and sample sizes, and that this new approach is better than the other existing ones. Also, the power of our approach is satisfactory. An example is proposed for illustrating our new approach.     

RM approach for ranking of generalized trapezoidal fuzzy numbers

Ranking of fuzzy numbers play an important role in decision making, optimization and forecasting etc. Fuzzy numbers must be ranked before an action is taken by a decision maker. In this paper, with the help of several counter examples, it is proved that ranking method proposed by Chen and Chen (Expert Systems with Applications 36 (3): 6833) is incorrect. The main aim of this paper is to propose a new approach for the ranking of generalized trapezoidal fuzzy numbers. The proposed ranking approach is based on rank and mode so it is named as an RM approach. The main advantage of the proposed approach is that the proposed approach provides the correct ordering of generalized and normal trapezoidal fuzzy numbers and also the proposed approach is very simple and easy to apply in the real life problems. It is shown that proposed ranking function satisfies all the reasonable properties of fuzzy quantities proposed by Wang and Kerre (Fuzzy Sets and Systems 118 (3): 375).     

A new approach to the reconstruction of images from Radon projections

A new approach is proposed for reconstruction of images from Radon projections. Based on Fourier expansions in orthogonal polynomials of two and three variables, instead of Fourier transforms, the approach provides a new algorithm for the computed tomography. The convergence of the algorithm is established under mild assumptions.     

A mapping strategy for the identification of structural systems

In this paper an alternative approach for identification problems is discussed. Unlike existing methods, this new approach combines in a general way finite differences and function approximation and is herein used for the identification of a particular system in structural dynamics, that is the damped Duffing oscillator subject to a swept-sine excitation. The solution obtained by means of the proposed method has been compared with the one obtained by a neural network. The present method gives better results at a low computational cost, with the advantage of solutions in explicit form. Besides, it is possible to prove that the solutions are stable and that from this new approach one can deduce, as a particular case, the approximation previously proposed by other authors.     

New Approach to Solving a Two-Dimensional Problem for an Orthotropic Plate

A new approach to solving a two-dimensional problem for an orthotropic multiply connected plate is proposed in place of the standard approach which reduces the problem to integrating a fourth-order differential equation. The new method reduces the problem to integrating second-order differential equations that can be solved successively using a perturbation technique.     

Ranking generalized fuzzy numbers in fuzzy decision making based on the left and right transfer coefficients and areas

Although a number of recent studies have proposed ranking fuzzy numbers based on the deviation degree, most of them have exhibited several shortcomings associated with non-discriminative and counter-intuitive problems. In fact, none of the existing deviation degree methods has guaranteed consistencies between the ranking of fuzzy numbers and that of their images under all situations. They have also ignored decision maker’s attitude toward risk, which significantly influences final ranking result. To overcome the above-mentioned drawbacks, this study proposes a new approach for ranking fuzzy numbers that ensures full consideration for all information of fuzzy numbers. Accordingly, an overall ranking index is obtained by the integration of the information from the left and the right (LR) areas between fuzzy numbers, the centroid points of fuzzy numbers and the decision maker’s attitude toward risk. This new method is efficient for evaluating generalized fuzzy numbers and distinguishing symmetric fuzzy numbers. It also overcomes the shortcomings of the existing approaches based on deviation degree. Several numerical examples are provided to illustrate the superiority of the proposed approach. Lastly, a new fuzzy MCDM approach for generalized fuzzy numbers is proposed based on the proposed ranking approach and the concept of generalized fuzzy numbers. The proposed fuzzy MCDM approach does not require the normalization process and thus avoids the loss of information results from transforming generalized fuzzy numbers to normal form.     

A new look at fractional programming

The parametric approach to fractional programming problems is examined and a new format is proposed for it. The latter reflects the fact that the approach as a whole capitalizes on a first-order necessary and sufficient optimality condition pertaining to differentiable pseudolinear functions.     

A network-based data mining approach to portfolio selection via weighted clique relaxations

We introduce a new network-based data mining approach to selecting diversified portfolios by modeling the stock market as a network and utilizing combinatorial optimization techniques to find maximum-weight s-plexes in the obtained networks. The considered approach is based on the weighted market graph model, which is used for identifying clusters of stocks according to a correlation-based criterion. The proposed techniques provide a new framework for selecting profitable diversified portfolios, which is verified by computational experiments on historical data over the past decade. In addition, the proposed approach can be used as a complementary tool for narrowing down a set of “candidate” stocks for a diversified portfolio, which can potentially be analyzed using other known portfolio selection techniques.     

一元B样条的B网观点

1.问题的提出 近年来,多元样条的研究进程表明,从多变量的观点重新认识一元样条的理论是很有必要的.本文运用重心坐标,以近代的B网方法为工具,重新探讨一元分片多项式的结构,进而为研究多元样条提供工具. 假设Q_n(t)是给定的分割:     

Decomposition approach for the global minimization of biconcave functions over polytopes

A decomposition approach is proposed for minimizing biconcave functions over polytopes. Important special cases include concave minimization, bilinear and indefinite quadratic programming for which new algorithms result. The approach introduces a new polyhedral partition and combines branch-and-bound techniques, outer approximation, and projection of polytopes in a suitable way.The authors are indebted to two anonymous reviewers for suggestions which have considerably improved this article.     

A Midpoint-Radius approach to regression with interval data

In this paper, a revisited interval approach for linear regression is proposed. In this context, according to the Midpoint-Radius (MR) representation, the uncertainty attached to the set-valued model can be decoupled from its trend. The estimated interval model is built from interval input-output data with the objective of covering all available data. The constrained optimization problem is addressed using a linear programming approach in which a new criterion is proposed for representing the global uncertainty of the interval model. The potential of the proposed method is illustrated by simulation examples.     

Generating optimal path by level set approach for a mobile robot moving in static/dynamic environments

According to the analogy between the mobile robot navigation path and the heat transferring path under steady state, the robot path planning problem during navigation is converted into identify the heat transferring path that minimizes the thermal compliance across the analysis domain. A new path planning approach which combines the concept of growth simulation and level set based heat conduction topology optimization framework is adopted to determine the heat transferring path. By introducing the concept of growth simulation, the proposed approach could calculate a few steps of the navigation path, which is of great significance for online reactive navigation. The proposed approach could avoid local minima and search for the optimal growth orientation freely without constraints from background mesh since the inherent characteristics of heat conduction and the level set approach, respectively. A new reactive navigation algorithm based on the proposed path planning approach and the concept of temporarily safe path is proposed to navigate the mobile robot from the start point to the goal point in unknown dynamic environment with static and dynamic obstacles. Diverse simulation cases are carried to illustrate the effectiveness of the reactive navigation algorithm.     

Forecasting time series with multiple seasonal patterns

A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state space model is developed for the series using the innovations approach which enables us to develop explicit models for both additive and multiplicative seasonality. Parameter estimates may be obtained using methods from exponential smoothing. The proposed model is used to examine hourly and daily patterns in hourly data for both utility loads and traffic flows. Our formulation provides a model for several existing seasonal methods and also provides new options, which result in superior forecasting performance over a range of prediction horizons. In particular, seasonal components can be updated more frequently than once during a seasonal cycle. The approach is likely to be useful in a wide range of applications involving both high and low frequency data, and it handles missing values in a straightforward manner.     

Error analysis of variable degree mixed methods for elliptic problems via hybridization

A new approach to error analysis of hybridized mixed methods is proposed and applied to study a new hybridized variable degree Raviart-Thomas method for second order elliptic problems. The approach gives error estimates for the Lagrange multipliers without using error estimates for the other variables. Error estimates for the primal and flux variables then follow from those for the Lagrange multipliers. In contrast, traditional error analyses obtain error estimates for the flux and primal variables first and then use it to get error estimates for the Lagrange multipliers. The new approach not only gives new error estimates for the new variable degree Raviart-Thomas method, but also new error estimates for the classical uniform degree method with less stringent regularity requirements than previously known estimates. The error analysis is achieved by using a variational characterization of the Lagrange multipliers wherein the other unknowns do not appear. This approach can be applied to other hybridized mixed methods as well.