Deriving the minimum staff number requirement for intelligent staff scheduling: An efficient constructive method and application

Abstract

AbstractEffective staff scheduling is a critical activity of successful software development management. Due to its difficulty and broad applications in many service delivery scenarios, staff scheduling has been studied for several decades. However, most existing work focus on constructing the working schedules based on a given workforce size. This paper tries to solve a prerequisite issue before performing staff scheduling, i.e., testing whether the already existed manpower can meet the scheduling requirements. Though it is possible to use network flow theory or artificial intelligence (AI) methods like genetic algorithms to solve this problem, their time complexities could be too high to be used for large problem sizes. This paper proposes a constructive method that can derive the minimum staff number for three scheduling problem variants in a linear running time, and in the meantime a corresponding working schedule that can satisfy all the problem constraints can be produced. We not only theoretically show the lower bound for the computation time complexity of our proposed method but also prove its correctness. Moreover, based on the derived minimum staff number, we further explore the genetic algorithm for generating the schedule and compare its performance with our method. The experiments show that our method outperforms the baselines in terms of both effectiveness and efficiency.