A stochastic bi-objective cybersecurity analyst scheduling problem with preferential days off and upskilling decisions

Abstract

With higher shifting rates towards digitalization, companies are more prone to the rising number of cyberattacks. Despite the insufficient supply of experts needed to safeguard the Information and Communications (ICT) system, their retention, training, and skill enhancement have become an additional challenge. The scheduling of experts is one among many areas in cybersecurity defense strategies which has attracted some recent concern. This work features a solution to a more generalized bi-objective scheduling problem using epsilon constraint and NSGA-II using a mixed integer linear program (MILP). Here, the model characterizes the enhancement of the employees’ analyzing ability while simultaneously focusing on overtime preferences. Although, a single objective approach is seen in a few articles yet bi-objective formulation in expert scheduling is never attempted. Results witness the optimal schedule in a tabulated structure with four overlapping shifts and eight-time windows implicitly representing rows and columns.