Architecture Of Intelligent Decision Support System Research Articles

An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment

Global manufacturing companies have some pressing needs to improve production visibility and decision-making performance by implementing effective production monitoring and scheduling. This paper proposes a radio frequency identification (RFID)-based intelligent decision support system architecture to handle production monitoring and scheduling in a distributed manufacturing environment. A pilot implementation of the architecture is reported in a distributed clothing manufacturing environment. RFID and cloud technologies were integrated for real-time and remote production capture and monitoring. Intelligent optimization techniques were also implemented to generate effective production scheduling solutions. A prototype system with remote monitoring and production scheduling functions was developed and implemented in a distributed manufacturing environment, which demonstrated the effectiveness of the architecture. The proposed architecture has good extensibility and scalability, which can easily be integrated with production decision-making as well as production and logistics operations in the supply chain. Lastly, this paper discusses the difficulties encountered and lessons learned during system implementation and the managerial implications of the proposed architecture.

An Intelligent Decision Support System for Residential Energy Consumption and Renewable Energy Utilization in Rural China

On the basis of investigation on residential energy consumption and renewable energy status quo in more than 300 rural areas of China's 31 provinces and cities, a database is built, and a corresponding intelligent decision support system (IDSS) based on SoftMan (Zeng and Tu, 2003) is put forward. First, the architecture of IDSS based on SoftMan is analyzed. Second, the three-level decision-making indexes are established, then nondimensional indicators of treatment is done by a multi-level fuzzy comprehensive evaluation model, and the weight coefficients are determined by the grey relational analysis. Third, the authors coordinate the decision-making process based on SoftMan and their implementations are given. The system can predict the future trends in energy consumption and the possibility of renewable energy alternatives to conventional energy by statistical analysis and evaluation on the results, and a reasonable energy plan for the government can be generated. It can offer valuable lessons for the pursuit of a sustainable rural development strategy.

A Review of Simulation-based Intelligent Decision Support System Architecture for the Adaptive Control of Flexible Manufacturing Systems

A Review of Simulation-based Intelligent Decision Support System Architecture for the Adaptive Control of Flexible Manufacturing Systems