Single machine preemptive scheduling Considering Energy Consumption and Predicting Machine failures with Data Mining Approach

Prediction of unexpected incidents and energy consumption are some industry issues and problems. The present study addressed the single machine scheduling with preemption and considering failures. This study also aimed at minimizing earliness and tardiness penalties and job expansion and compression. The present study presented a mathematical model for this problem by considering processing time, machine idle, release time, rotational speed and torque, failure time, and machine availability after repair and maintenance. The failure time has been predicted using a machine learning algorithm. The results indicate that the proposed model is useful for problems with 6-job dimensions. This study solves this problem in two parts. The first part predicts failures and obtained some rules to correct the process, and the second includes the sequence of single-machine scheduling operations. In the second part, the scheduling model was used considering these failures and machine idle in single-machine scheduling to achieve an optimal sequence, minimize energy consumption, and reduce failures.