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Many organizations are developing their competencies in logistics and green supply chain management (GSCM) in order to maintain competitiveness. When an organization decides to implement GSCM, the administrators always encounter internal and external risk items or difficulties which they know even they dont know. This study therefore proposes an analytic hierarchy model to help the administrators understand the critical risk factors influence the GSCM system initiation, and an aggregative risk degree is indicated which risk grade they are in. Successful influences can help the enterprise under their competitive advantages to appropriate allocation of resources, to further improve the efficiency of resource utilization, and provide enterprises to choose business strategy of the green supply chain management has reached the goal of corporate operating performance and brand management.

It is important issue to select and design a suitable Assistive Input Devices (AID) for disabled people. This study applies the Incomplete Linguistic Preference Relations (InLinPreRa) analytical framework to predicting the success of design Assistive Input Devices (AID) for disabled people implementation. The results demonstrate that the five most important influential factors are (C6) User-friendly interface (0.166) (C4) Budget (0.163), (C5) Stability of Device (0.159), (C3) easy to maintain (0.144), (C1) easy to operate (0.126). The prediction success rate for AID for disabled people system implementation is 62.7%.

When the school decides to implement the RFID campus-safety system, the administrators always encounter internal and external risk items or difficulties which they know even they don’t know. This study therefore proposes an analytic hierarchy model to help the administrators understand the critical risk factors influence the RFID campus-safety system initiation, and an aggregative risk degree is indicated which risk grade they are in. The importance weights of risk factors and possible occurrence ratings of four risk grade (high-risk, medium-risk, low-risk and none-risk) are determined by using consistent fuzzy preference relations. The relative priority weights of evaluators are taken into consideration at the same time and obtained by simple additive weighting method. By multiplying the importance weights of risk factors, possible occurrence ratings of risk grades and the relative priority weights of evaluators, the aggregative risk degree of implementing RFID campus-safety system.