The training and development of your workforce is vital to the achievement of digital transformation success for businesses. And today, more and more businesses are leveraging e-learning to educate their employees. The advantages for businesses using online learning platforms as opposed to traditional training methods are bountiful. First, it lowers business costs since one training session can be delivered to multiple people. Second, topics can be broken down into bite-sized chunks, meaning that employees do not need to spend lengthy periods of time away from their desks.

When selecting locations for a set of facilities, standard clustering algorithms may place unfair burden on some individuals and neighborhoods. We formulate a fairness concept that takes local population densities into account. In particular, given $k$ facilities to locate and a population of size $n$, we define the "neighborhood radius" of an individual $i$ as the minimum radius of a ball centered at $i$ that contains at least $n/k$ individuals. Our objective is to ensure that each individual has a facility within at most a small constant factor of her neighborhood radius. We present several theoretical results: We show that optimizing this factor is NP-hard; we give an approximation algorithm that guarantees a factor of at most 2 in all metric spaces; and we prove matching lower bounds in some metric spaces. We apply a variant of this algorithm to real-world address data, showing that it is quite different from standard clustering algorithms and outperforms them on our objective function and balances the load between facilities more evenly.

THE training and development of your workforce is vital to the achievement of digital transformation success for businesses. And today, more and more businesses are leveraging e-learning to educate their employees. The advantages for businesses using online learning platforms as opposed to traditional training methods are bountiful. First, it lowers business costs since one training session can be delivered to multiple people. Second, topics can be broken down into bite-sized chunks, meaning that employees do not need to spend lengthy periods of time away from their desks.