Fuqiang Lu

A Coordination of Risk Management for Supply Chains Organized as Virtual Enterprises

As a new management mode, great attention has been paid to virtual enterprise (VE). While there is much research material on risk management of VE, a relationship perspective on owner and partner performance assessment and management can bring an added dimension. The coordination of risk management in fashion and textiles (FTs) supply chain organized as a VE is studied in this paper. The aim of this study is to find proper decision mechanisms that can improve the overall performance of risk management for the whole VE as well as each member. For the risk management problem in VE, a centralized mechanism is given as the base case, and then a distributed decision-making (DDM) mechanism with incentive scheme is introduced to establish a practicable strategic partnership. Under the DDM mechanism, a relationship performance definition that incorporates the financial dimension is investigated. For the two resulting optimization problems, a particle swarm optimization (PSO) algorithm is designed. In the numerical examples, the study shows that the DDM mechanism with incentive scheme can improve the overall benefit of risk management beyond the centralized one. Additionally, sensitivity analysis is conducted with respect to the bonus parameter, and suggestions are made for further research.

PSO based stochastic programming model for risk management in Virtual Enterprise

Risk management in a virtual enterprise (VE) is an important issue due to its agility and diversity of its members and its distributed characteristics. In this paper, a stochastic programming model of risk management is proposed. More specifically, we consider about the stochastic characters of the risk in VE, and then we build a stochastic programming model to deal with the stochastic characters of the risk. In detail, this is a chance constraint programming model. One of the great advantages of this class of model is that it can exactly describe the risk preference of the manager. In this model, the risk level of VE is obtained from a composite result of many risk factors. In order to reduce the risk level of VE, the manager has to select effective action for every risk factor. For each risk factor, there are several actions provided. Here we only select one action for a risk factor or do nothing with it. To solve this stochastic programming model, a particle swarm optimization (PSO) algorithm is designed. On the other hand, to deal with those stochastic variables, Monte Carlo simulation is combined with PSO algorithm. Finally, a numerical example is given to illustrate the effectiveness of the PSO algorithm and the result shows that the model is very useful for risk management in VE.

A constructional DDM model for risk management of Virtual Enterprise

To guarantee the profit, virtual enterprise (VE) has to manage its risk effectively. Due to VEpsilas distributed characteristics, such as the diversity of members and distribution of cooperative regions, we introduce a distributed decision making (DDM) model for risk management of VE. In detail, we propose a constructional DDM model, which focuses on the situation of team or enforced team relationship between partners of VE. A taboo search (TS) algorithm is designed to solve the model. Numerical examples are given to illustrate the effectiveness of the model and the proposed algorithm.

Urban emergency intelligent decision system based on variable precision graded rough set on two universes

In recent years, different kinds of rough set models on two universes have been given. In this paper a variable precision graded rough set model based on two distinct but related universes (VPGRSTU) is proposed. We determine the scope of the grade from the absolute quantitative point of view, and the precision of n can be changed with user requirements. We introduce an algorithm to compute the new lower and upper approximations in VPGRSTU. Furthermore, a method to measure roughness of VPGRSTU is given. The method also can be used to compute the lower and upper approximations. An algorithm based on matrix is proposed to realize above method. Finally, several examples are employed to demonstrate the VPGRSTU and the theory in this paper. At last we build urban emergency intelligent decision system based on VPGRSTU.

Intelligent collaborative urban traffic management system based on SOA and cloud computing

Now the congestion of road has impacted on the city development seriously, and became the crux which constrained the city development. An intelligent urban traffic management system is needed urgently. In this paper an architecture of the intelligent collaborative urban traffic management system based on SOA and cloud computing is proposed. A collaborative service model based on SOA is given to realize intelligent collaborative management of urban traffic. The technology of service request and response dispatching based on SOA is proposed in the paper to match the collaborative event processing. Mass calculation was realized by the application of the cloud computing platform. The system fundamentally realizes the intelligent monitoring and management of urban traffic and realizes the purpose of intelligent dredge of urban traffic.

GA Based Stochastic Risk Programming for Virtual Enterprise

With the development of word economy and IT, virtual enterprise is considered as one of the most promising paradigms for future enterprises. As Virtual Enterprises agility and diversity of its members, which make its internal and external risk with great character of stochastic. The multi actions single choices model of risk programming is proposed in this paper, which combines with the probability measure risk evaluation method. Its a stochastic programming model with stochastic variables. In this model the total risk of Virtual Enterprise and the total cost using to reduce the risk must be held with a confidence level alpha,beta, respectively. To solve the model, a genetic algorithm is designed, which combines with the Monte Carlo simulation. Finally, a numerical example is given to illustrate the effective of this algorithm.

Schedule Risk Management of IT Outsourcing Project Using Negotiation Mechanism

Schedule risk control is a very important issue for the success of IT outsourcing project. There are two independent agents in IT outsourcing project, which is the client and the vendor, the two sides need to reach an agreement on the schedule and cost of the project. However, traditional schedule risk control methods haven’t taken this characteristic of IT outsourcing into account. Given that, this paper analyze the characteristic of IT outsourcing with negotiation mechanism. We formulate the negotiation protocol that the two sides need to obey in the process of negotiation, design the decision making model and concession functions. Based on the simulation results, the effectiveness of the negotiation mechanism is verified. We also analyzed the influence of maximum negotiation rounds and concession factors on the negotiation results.

Risk evaluation of IT outsourcing using Risk-matrix

The success of IT outsourcing (ITO) is significantly influenced by the risk management of ITO. The accuracy of risk evaluation directly impacts the effectiveness of risk management. In this paper, we analyze risk factors of ITO, and Risk-matrix has been reviewed; then we calculate the Borda price to recognize the importance of the risk, finally the managers can focus on the more critical risks.

Partners relationship based Distributed Decision Making models for risk management of Virtual Enterprise

To guarantee the expected profit, Virtual Enterprise (VE) has to manage its risk effectively. Due to VEpsilas distributed characteristics, we explore the art of Distributed Decision Making (DDM) theory and apply it to risk management of VE. Based on partners relationship, such as team, enforced-team and non-team, we introduce four classes of models, constructional DDM model, organizational DDM model, coordinative model and principle-agent model. The brief solutions of the models are also discussed.

Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project

IT outsourcing is an effective way to enhance the core competitiveness for many enterprises. But the schedule risk of IT outsourcing project may cause enormous economic loss to enterprise. In this paper, the Distributed Decision Making (DDM) theory and the principal-agent theory are used to build a model for schedule risk management of IT outsourcing project. In addition, a hybrid algorithm combining simulated annealing (SA) and genetic algorithm (GA) is designed, namely, simulated annealing genetic algorithm (SAGA). The effect of the proposed model on the schedule risk management problem is analyzed in the simulation experiment. Meanwhile, the simulation results of the three algorithms GA, SA, and SAGA show that SAGA is the most superior one to the other two algorithms in terms of stability and convergence. Consequently, this paper provides the scientific quantitative proposal for the decision maker who needs to manage the schedule risk of IT outsourcing project.