Flowshop scheduling with machine deterioration based on job sequences

This paper addresses the two-machine permutation flowshop problem with deterioration. The objectives are minimizing the makespan and the average tardiness. Jobs have a baseline process time in each machine and have a due date. The actual time to process a job depends on the machine performance level at the start of each job, which is a function of the previously processed jobs and their wear/deterioration effect on the machine. The article proposes multiple heuristics and a comprehensive set of experiments. The results indicate that as a group, the heuristics generate solutions that are very close to the optimal for both criteria. Furthermore, no heuristic approach is dominant for all experimental conditions, thus heuristic selection to solve practical problems should be based on the specific problem characteristics.

Monitoring the Wind Turbine Condition Using Big Data Technique

The desire to cut back the price of energy from turbine generation has seen a rise within the analysis applied to the sphere of turbine condition observation. Wind turbine condition observation has the potential to cut back operation and maintenance prices through optimized maintenance programming and also the rejection of major breakdowns. To aid this analysis, increasing volumes of knowledge are being captured and keep. These massive volumes of knowledge could also be deemed ‘Big Data’, and need improved handling techniques so as to figure with the information with efficiency. It introduces a turbine condition observation system that has been put in in AN operational Vestas V47 turbine for the aim of developing algorithms to sight machine deterioration. The system’s ability to capture massive volumes of knowledge (approx.2TB per month) has LED to the need of victimization increased knowledge handling techniques. This paper can discuss these ‘Big Data’ techniques and recommend however they will ultimately be used for condition observation of multiple wind turbines or wind farms.