Improving the Performance of MMPP/M/C Queue by Convex Optimization–A Real-World Application in Iron and Steel Industry

Abstract

Now the information system of the iron and steel industry is a typical sensor cloud computing system. The system has accumulated a lot of relevant manufacturing data. Using these data can well solve the slab storage problem in the production process for the iron and steel industry. In this paper, we investigate a queueing system where customers arrive according to a Markov Modulated Poisson Process (MMPP). MMPP can describe how the arrival rate changes with the environment, which is more realistic. We develop an MMPP(3)/M/C queueing model to solve the congestion problem in the iron and steel industry. In the actual production process, the slab arrival rates vary with states, therefore MMPP is used to model the arrival process in this paper. Based on explicit performance measures, we develop a nonlinear optimization model of queueing system, and convert the model into a convex optimization problem. Through the convex optimization method, the MMPP(3)/M/C model, resulted from the practical system, can be analyzed by the M/M/C model approximately.

Keywords

• Sensor-cloud systems
• Markov modulated Poisson process
• convex optimization
• slab
• crane