Weixuan Xu

Hybrid evolutionary method for capacitated location-allocation problem

Abstract Location-allocation model is widely applied for facility location design in practice. In this paper, we discuss an extension of location-allocation model which has capacity constraints and propose a hybrid evolutionary method to solve it which absorbs ideas from both genetic algorithms (GAs) and evolutionary strategy (ES) as well as combined with efficient traditional optimization techniques. It is shown that the proposed method is effective in finding global or near global solutions by numerical simulations.

Hybrid evolutionary method for obstacle location-allocation

In this paper we propose a hybrid evolutionary method for Obstacle Location-allocation problem. This problem can be described as a tri-level mixed integer programming problem. Since this problem is very complex and with many local solutions, no direct method Is effective to solve it, Heuristic methods were proposed to it, but optimality is not guaranteed yet. Our hybrid evolutionary method adopts the main structure of Genetic Algorithms (GA) absorbing ideas from Evolutionary Strategy (ES) and combines with some traditional optimization techniques. In this way we can pursue global optimization maintaining a good efficiency of our method. A case study shows the effectiveness of this method.

Virtual enterprise and its intelligence management

Virtual enterprise (VE) will be the efficient organization form in the future owing to the rapid development of computer techniques and information technology. This paper exposes systematically the concept of VE and its structure. Methodology for intelligentized management is described. An integrated system based on client/server is proposed, which is useful for intelligent decision making in VE management.

Artificial neural network based predictive method for flood disaster

Most of flood disaster predictions belong to ill-structured problems, while artificial neural network (ANN) has several characteristics that are suitable for solving them. In this paper, a neural network based predictive method for flood disaster problem is proposed in which the neural network model and its basic designing principles are described, and an example of flood disaster area in China from 1949 to 1994 is used for demonstration.

A modified ANN for convex programming with linear constraints

Convex programming with linear constraints represents a large class of optimization problems which has wide applications, such as linear programming, quadratic programming and some network flow programming problems. In this paper an artificial neural network (ANN) approach based on the Lagrangian multiplier method (Lagrangian ANN) is discussed to solve this problem. The emphasis of the paper is on analyzing the defect of premature of the conventional Lagrangian ANN, and a modification to it in order to overcome the premature defect is presented. It is proved that the modified Lagrangian ANN can always give the optimal solution. Numerical simulations demonstrate the effectiveness of the proposed modification.

Application of VaR methodology to risk management in the stock market in China

This paper applies the new risk management tool, Value at Risk (VaR) methodology, to the stock market in China. From the comparison between the predicted VaR and real return, the calculated results are mostly satisfied with the confidence level at 95%.

A genetic algorithm method for one-dimensional machine location problems

One goal of designing a generalized flow line is to minimize the backtracking of jobs, i.e., to assign M machines to M locations along a linear track to minimize the total backtracking movements of jobs. It can be formulated as a quadratic assignment problem which is a difficult task to solve. In this paper, a genetic algorithm method is proposed and compared with some other methods. It is shown that the genetic algorithm method can find good quality solution effectively.

An integrated methodology for decision making of mining method selection

Mine enterprises increasingly depend on computerised decision support tools to improve decision-making in areas such as mining method selection, equipment selection and production scheduling. Information technology and management science are capable of improving the decision making of mining engineering, which have been integrated into a system we have developed. Selection of the mining method for ore deposits is extremely complex and it relies on several factors; the decision maker also confronts multiple conflicting objectives such as maximising benefits and safety, minimising cost, etc. In this paper, a methodology for developing an intelligent decision system for mining method selection integrated with expert systems and fuzzy evaluation based multi-attributes decision theory is described.

Multiple objective-integrated methodology of global optimum decision-making on mineral resources exploitation

The concept of a complex large system of mineral resource exploitation was advanced from the system theory viewpoint. On the basis of the integrated techniques of qualitative and quantitative analysis, the multiple objective intelligence-integrated methodology for decision-making on mineral resource exploitation was investigated and its integrated model was also formulated. The mineral deposit model, expert system model for mining method selection and the artificial neural network model for prediction economic index, as well as the programming model, acted as the core of this system, while the multiple goal programming model served as the top of the model. An intelligent system to assist the programming of decision-making on mineral resource exploitation for operating mines was developed by means of the systematic combination of optimization technology with an artificial neural network, an expert system, computer-aided design and operations research (OR). The structure of an integrated intelligent system for decision-making on mineral resource exploitation was described. In addition, forecast and simulation, as well as optimization, were integrated. The systems preliminary application was illustrated with an example.

Neural network approach for allocation with capacity

In this paper we discuss neural network approach for allocation with capacity constraints problem. This problem can be formulated as zero-one integer programming problem. We transform this zero-one integer programming problem into an equivalent nonlinear programming problem by replacing zero-one constraints with quadratic concave equality constraints. We propose two kinds of neural network structures based on penalty function method and augmented Lagrangian multiplier method, and compare them by theoretical analysis and numerical simulation. We show that penalty function based neural network approach is not good to combinatorial optimization problem because it falls in the dilemma whether terminating at an infeasible solution or sticking at any feasible solution, and augmented Lagrangian multiplier method based neural network can alleviate this suffering in some degree.