Abstract:
In this paper, we focus on a class of nonlinear bilevel programming problems where the follower’s objective is a function of the linear expression of all variables, and the follower’s constraint functions are convex with respect to the follower’s variables. First, based on the features of the follower’s problem, we give a new decomposition scheme by which the follower’s optimal solution can be obtained easily. Then, to solve efficiently this class of problems by using evolutionary algorithm, novel evolutionary operators are designed by considering the best individuals and the diversity of individuals in the populations. Finally, based on these techniques, a new evolutionary algorithm is proposed. The numerical results on 20 test problems illustrate that the proposed algorithm is efficient and stable.

Abstract:
This study involves three dimensions: distance language education (DLE) as the context, videoconferencing as the technology, and the provision of synchronous oral and visual interaction in DLE as the core research problem. This article follows on this author's previous research in regard to the inclusion of oral and visual interaction in distance language learning through the use of Internet-based desktop videoconferencing tools. It evaluates the findings from a two-stage evaluation of a particular videoconferencing tool, NetMeeting. The results from this research confirm that the present generation of Internet-based desktop videoconferencing tools are capable of supporting oral and visual interaction in DLE. Recommendations are presented for future use of videoconferencing in DLE.

Abstract:
As an effective production system, TPS (Toyota Production System) has been gone in for by numerous domestic and foreign factories recently. However, it is not effectively as in Japan actually, and many problems appear. This article analyses TPS’s domestic operational status, and put forward a set of reasonable countermeasures and proposals. This analysis will promote the TPS applicability research and guide the domestic enterprises to push TPS entirely.

Abstract:
In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solution is dominated by other solutions, the solution can generate more new solutions than those solutions, which makes the solution of each subobjective space converge to the optimal solutions as far as possible. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D and NSGAII. Simulation results on six multiobjective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms. 1. Introduction Since there are many problems with several optimization problems or criteria in real world [1], multiobjective optimization has become a hot research topic. Unlike single-objective optimization problem, multiobjective optimization problem has a series of noninferior alternative solutions, also known as Pareto optimal solutions (the set of Pareto optimal solutions is called Pareto front [2]), which represent the possible trade-off among various conflicting objectives. Therefore, multiobjective optimization algorithms for MOP should be able to discover solutions as close to the optimal solutions as possible; find solutions as uniform as possible in the obtained nondominated front; determine solutions to cover the true Pareto front (PF) as broad as possible. However, achieving these three goals simultaneously is still a challenge for multiobjective optimization algorithms. Among various multiobjective optimization algorithms, multiobjective evolutionary algorithms (MOEA), which make use of the strategy of the population evolutionary to optimize the problems, are an effective method for solving MOPs. In recent years, many MOEAs have been proposed for solving the multiobjective optimization problems [3–18]. In the MOEA literatures, Goldberg’s population categorization strategy [19] based on nondominance is important. Many algorithms use the strategy to assign a fitness value based on the nondominance rank of members. For example, the

Abstract:
The basic theory on the conformal geometry of timelike surfaces in pseudo-Riemannian space forms is introduced, which is parallel to the classical framework of Burstall etc. for spacelike surfaces. Then we provide a discussion on the transforms of timelike $(\pm)-$isothermic surfaces (or real isothermic, complex isothermic surfaces), including $c-$ polar transforms, Darboux transforms and spectral transforms. The first main result is that $c-$polar transforms preserve timelike $(\pm)-$isothermic surfaces, which are generalizations of the classical Christoffel transforms. The next main result is that a Darboux pair of timelike isothermic surfaces can also be characterized as a Lorentzian $O(n-r+1,r+1)/O(n-r,r)\times O(1,1)-$type curved flat. Finally two permutability theorems of $c-$polar transforms are established.

Abstract:
An auxiliary function method is proposed for finding the global minimizer of integer programming problem. Firstly, we propose a method to transform the original problem into an integer programming with box constraint, which does not change the properties of the original problem. For the transformed problem, we propose an auxiliary function to escape from the current local minimizer and to get a better one. Then, based on the proposed auxiliary function, a new algorithm to find the global minimizer of integer programming is proposed. At last, numerical results are given to demonstrate the effectiveness and efficiency of the proposed method. 1. Introduction Consider the following integer programming problem: where is the -dimensional integer set, and the set is bounded. Many real-life applications can be modeled as problem ( ), such as production planning, scheduling, and operations research problem. The objective functions of most of the problems are nonlinear and have more than one local optimal solutions over feasible region . This requires the global optimization techniques to find the best solution amongst multiple local optima. Since integer programming problems are generally NP-hard, there are no efficient algorithms with polynomial-time complexity for solving them. Thus, many approximate algorithms have been rapidly developed in recent years, such as greedy search (see [1–4]), simulated annealing (see [5, 6]), genetic algorithm (see [7–9]), tabu search (see [10, 11]), and discrete filled function techniques (see [12–17]). The discrete filled function method is one of the more recently developed global optimization methods to solve integer programming problems. Once a local minimizer has been found by a local search method, the discrete filled function method introduces an auxiliary function to escape from the current local minimizer and to get a better one. At present, the discrete filled function methods mainly focus on the unconstrained integer programming problems, and most of the existing filled functions contain parameters which are needed to adjust. Thus, solving general integer programming problems is difficult, and solving the unconstrained integer programming problems needs much computation. In this paper, we propose an auxiliary function method to solve problem ( ). The proposed auxiliary function has no parameters, and the computation of the proposed method is relatively small. The remainder of this paper is organized as follows: Section 2 gives some useful notations and definitions. In Section 3, we propose a method to transform the

Abstract:
For the problem that the energy efficiency of the cloud computing data center is low, from the point of view of the energy efficiency of the servers, we propose a new energy-efficient multi-job scheduling model based on Google’s massive data processing framework. To solve this model, we design a practical encoding and decoding method for the individuals and construct an overall energy efficiency function of the servers as the fitness value of each individual. Meanwhile, in order to accelerate the convergent speed of our algorithm and enhance its searching ability, a local search operator is introduced. Finally, the experiments show that the proposed algorithm is effective and efficient.

Abstract:
Evolutionary algorithms (EA) are a class of general optimization algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. In this paper, a novel evolutionary algorithm is proposed to solve global numerical optimization with continuous variables. In order to make the algorithm more robust, the initial population is generated by combining determinate factors with random ones. And a decent scale function is designed to tailor the crossover operator so that it can not only find the decent direction quickly but also keep scanning evenly in the whole feasible space. In addition, to improve the performance of the algorithm, a mutation operator which increases the convergence-rate and ensures the convergence of the proposed algorithm is designed. Then, the global convergence of the presented algorithm is proved at length. Finally, the presented algorithm is executed to solve 24 benchmark problems. And the results show that the convergence-rate is noticeably increased by our algorithm.

Abstract:
There is not a unanimous conclusion about the
influential elements of online customers’ repurchase intention. We established
a concept model and discussed how utilitarian values (perceived ease of use and
perceived usefulness), social values (satisfaction and trust) and the hedonic value
(perceived enjoyment) directly and indirectly influenced customers’ repurchase
intention in the context of online shopping. It adapted questionnaire to
collect data and testified the hypothesis by structural equation model. The
results showed that perceived usefulness, online customers’ satisfaction and perceived
enjoyment had significantly positive impact on online customers’ repurchase
intention. Moreover, we found that compared with utilitarian factors, the
hedonic factor had a stronger positive impact on repurchase intention.

Abstract:
The main purpose of this paper is to analyze if RMB appreciation is the reason for the decline of Guangdong textile and garment exports and how to make textile and garment industry of Guangdong province break through the bottleneck of development, so as to achieve a qualitative leap in the context of the RMB exchange rate fluctuations and rising production costs. Five parts are included, the first part is introduction, which elaborates the research significance and literature review; the second part is about data and research methods; the third part is an empirical analysis of the impact of RMB exchange rate fluctuation on textile and clothing export of Guangdong; the fourth part is the conclusion analysis, while the last part are policy suggestion, which includes three aspects, government industry and enterprise themselves. In theory, the appreciation of the exchange rate will reduce the export price advantage to reduce exports. However, in this paper, the VAR and VEC models were constructed by using the monthly data of Guangdong textile and clothing export (LNEX), RMB real exchange rate (LNER) and purchasing price index (LNPPI) in 2007-2014, finding that Although LNEX has a co-integration relationship with LNER in the long run, LNER is not the Granger Cause of LNEX, while LNPPI passes the test of causality; Impulse response curves show that LNER has a negative impact on LNEX in the short term, while there is no shock effect for a long time; Variance decomposition analysis shows that contribution of LNER to LNEX was 0, both in the long term and short term, while the long-term contribution of LNPPI to LNEX is about 8%.