Variable Neighbourhood Search: A case study for a highly-constrained workforce scheduling problem

Abstract

This paper describes a Variable Neighbourhood Search (VNS) combined with Metropolis-Hastings acceptance to tackle a highly constrained workforce scheduling problem typical of field service operations (FSO) companies. A refined greedy algorithm is firstly designed to create an initial solution which meets all hard constraints and satisfies some of the soft constraints. The VNS is then used to swap out less promising combinations, continually moving towards a optimal solution until meeting finishing requirements, which are either a satisfactory mean fitness set as a parameter, or a time allowance of one hour. The results of this approach are promising when compared to the stand-alone greedy algorithm, and have showed an average of 10.3% increase in fitness when parameterized with expected demand data and real employee data.