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The purpose of this paper is to present a method to accurately forecast the tendency of the gross amount of energy sources consumption of the country and construct a new kind of algorithm for forecasting that synthesizes the advantages of the grey model, Markov chains, and least square method.

With the application of this new algorithm, this paper have forecasted the trend of the gross amount of energy sources consumption of the country and come to the conclusions that the new algorithm is more precise than the grey model. It is proved that the improved grey‐Markov chain algorithm is effective and can be used by authorities to make decision.

It was found that combining the grey model, Markov chains, and least square method, can be a new algorithm for forecasting the trendency of the gross amount of energy sources consumption.

The new algorithm is only suitable for the short‐term forecast.

The grey forecasting method reflects the overall tendency of primitive data sequence of the gross amount of energy source, and the Markov chain forecasting method reflects the effect of the random fluctuation. The least square method reflects the tendency of increase. The new algorithm is more precise than the grey model.

The purpose of this paper is to create a model of role‐based access control (RBAC) for virtual enterprise (VE).

An access control model for security and management of VE is presented by integrating generic structure of VE and applying the principles of RBAC. In addition, the application of the model to a supply chain oriented VE illustrates that a general access control scheme can ensure the running of VE.

A theory base of access control for the realization of the VE is found.

The paper provides a very useful new model of access control for VE. This paper is aimed at researchers and engineers.

The purpose of this paper is to create a model of role‐based access control (RBAC) based access control for virtual enterprise (VE).

An access control model for security and management of VE is presented by integrating generic structure of VE and applying the principles of RBAC. In addition, the application of the model to a supply chain‐oriented VE illustrates that a general access control scheme can ensure the running of VE.

A theory base of access control for the realization of the VE is found.

The paper presents a very useful new model of access control for VE. This paper is aimed at researchers and engineers.

The purpose of this paper is to propose the input‐occupancy‐output analysis for grey situations and to manage the economic systems with missing information. The situation is realistic in practice.

During the decision‐making process of an economic system, the managers often get some uncertain data for the missing information. Then, it becomes difficult to analyze the complex system by using the input‐occupancy‐output analysis. Since the uncertain data are grey numbers, the decision maker can control it by using grey system theory and the input‐occupancy‐output analysis.

Get the grey direct consumption and occupancy coefficient matrices and the computational formulas of their matrix‐covered sets. The grey model of input‐occupancy‐output analysis and its covered solution are also obtained.

The chaos of the grey statistic data should satisfy a level of accuracy and it is key to get the number‐covered set that the true data belongs to.

A feasible approach to control the economic system that the accurate data are difficult to be obtained.

Give a new approach to study an economic system by using grey system theory.

“Poor” information systems are found everywhere. In this paper, a control approach to “poor” information systems, such as social economic systems, is proposed. First, “poor” information processing and SCGM‐model will be discussed. Secondly, we will develop an optimal controller of “poor” information systems.

The purpose of this paper is to obtain some strong deviation theorems for arbitrary continuous random variable sequences under suitable restrict Chung‐Teicher type conditions.

The crucial part of the proof is to construct a.s. convergence super‐martingale by means of the notion of limit logarithmic likelihood ratio of random variable sequences and then applying the martingale convergence theorem.

The upper and lower bounds of the deviations from the sums of arbitrary continuous random sequence to their marginals are obtained.

The rigorous bounds are the main limitations which are difficult to obtain.

A useful method to study the property of dependent random sequence.

The paper presents the new approach of proof strong limit theorems.

Bridge plans are a complicated grey system. It depends on various natural and social factors. The purpose of this paper is to provide a scientific method for optimization of bridge construction plan.

Grey relational analysis (GRA) is completely new analysis method has been proposed in the grey system theory. Grey relational order can be used to describe the relation between the related factors based on data series rather than linear relation and typical distribution. First, this paper describes the basic steps and formulae of GRA. Then provides an example to show how to select best bridge construction plan with the method. Specially discusses significant influence of weight selection on decision making for a bridge plan.

The optimization of bridge construction plan will be selected more reasonable and more objective with the method GRA.

This paper will be further studied on how to quantify indicators more objectively and how to decide weight factor more reasonably.

It has significant practical value to apply GRA to optimization of bridge plans and other engineering projects.

A scientific method‐GRA has been applied to the selection of bridge plans for seeking the best comprehensive result.

The purpose of this paper is to extend two‐dimensional sequential uniform design and present three‐dimensional sequential uniform design which can optimize the problem of determining the extreme value of three‐factor polynomial.

Since there are limitations in two‐dimensional sequential uniform design, three‐dimensional sequential uniform design introduced in this paper is to arrange the experimental points by U₉(9³) uniform design table and to determine the maximum of the experimental values according to the demand. It is proven that the convergence of the experimental central point's sequence and that it can optimize the problem of determining the extreme value of no‐cross term quadratic polynomial monotone function class and the corresponding conditions are also proven. Taking the no‐cross term quadratic polynomial monotone function as an example, the superiority of it is testified.

Three‐dimensional sequential uniform design can optimize the problem of determining the extreme value of three‐factor polynomial. It can get higher precision and better convergence but do fewer experiments than general uniform design.

A very effective method in resolving the problem of selecting optimum on multi‐dimensional space.

A new method of three‐dimensional sequential uniform design which can get higher precision and better convergence is presented.

The purpose of this paper is to present a new approach for selecting the good heavy oil reservoirs to develop preferentially, which can avoid the huge economical loss resulted from wrong decision.

A new method of ranking the development priority of heavy oil reservoir is present, in which the neural network is applied for the first time to acquire reservoir parameters' weights through training samples and the genetic algorithm is used to optimize the joint weighs of neurons in case that neural network falling into local minimum. Additionally, the paper establishes subordinate function of every parameter. Eventually, comprehensive evaluation values of all heavy oil reservoirs are obtained.

The method can ensure the veracity and creditability of the parameters' weights, avoid the randomicity brought by experts.

Accessibility of the data of many heavy oil reservoirs is the main limitation.

A very useful and new method for the decision makers of heavy oil reservoirs development.

The new approach of ranking the development priority of heavy oil reservoir based on the neural network and the genetic algorithm. The paper is aimed at the leaders who manage the development of heavy oil reservoirs.

The purpose of this paper is to acquire doubly variable precision‐based knowledge rules from incomplete decision tables (IDTs) in the framework of pansystems methodology. It suggests a new variable precision limited tolerance – a special pansystems relation – rough set model with precision inclusion and a reduct procedure in which it overcomes the non‐monotony in forming tolerance classes when reducing an attribute from attribute set.

Through introducing variable precision and limited tolerance relation in IDT, it constructs symmetric binary relation, dissimilar to non‐symmetric relation proposed by others, and then forms tolerance classes. It proposes a new reduction procedure with absolute value calculation to avoid tolerance classes being non‐monotone. Using variable inclusion, it obtains lower and upper approximations with noises.

Tolerance classes are not monotone with the reduction of attribute from attribute set in the proposed variable precision and limited tolerance relation, but it remains symmetry. Proposed reduction procedure with absolute value calculation is a new approach in adjudging whether a reduct equals to the original whole attribute set within a error range or not.

Using variable precision and limited rough set model with variable inclusion to mine deep knowledge from IDT is a paradise in knowledge discovery in dealing with non‐determinative and vague problems.

The formation of symmetric tolerance relation is natural. The reduction procedure with absolute value calculation is new and not similar to those existed in literatures.