A Novel Sedentary Workforce Scheduling Optimization Algorithm using 2nd Order Polynomial Kernel

Abstract

No two humans are identical. There are variations in their capability, thinking process, and personality. That is why human society is diverse. This diversity is visible everywhere, including in office environments. The office environments are diverse based on culture, service, and goals. Offices, where the workforce uses computing devices to perform their responsibilities, are mostly sedentary settings where employees must remain seated during office hours. Perseverance and self-motivation are mandatory to make sedentary office hours effective. However, these qualities are not common to everyone. It causes an imbalance in workload distribution and scheduling which facilitates the possibility of irrelevant performance evaluation. This paper addresses these issues and proposes a novel algorithm to optimally schedule the workforce in a sedentary office environment. The proposed algorithm uses 2 nd order polynomial kernel-based Support Vector Machines (SVM) classifier and classifies human activities with 95.0% accuracy. This accurate classification is further utilized to optimally schedule the workforce, which improves performance by 30.6% and saves an average of 1 hour and 2 minutes per day.