In this paper, a blood supply chain network, where the occurrence of disruption might interrupt the flow of Red Blood Cells, is dealt with.
The crux of self-supervised video representation learning is to build general features from unlabeled videos.
This study offers a step-by-step practical procedure from the analysis of the current status of the spare parts inventory system to advanced service-level analysis by virtue of simulation-optimization technique for a real-world case study associated with a seaport.
Therefore, we propose to capture human motion by jointly analyzing these Internet videos instead of using single videos separately.
Furthermore, a rational presumption is reflected in the problem statement in which the time and cost of PM are pertinent to the interval between the prior perfect repair and current PM.