Shift Scheduling and Employee Rostering: An Evolutionary Ruin & Stochastic Recreate Solution

For decades, since the inception of the field, scheduling problems have been solved with a variety of techniques. Many proven algorithms to these problems exist; however, there is no single method to solve all the vast variety of problems that exist across many sub-fields with differing datasets. In this paper we apply Evolutionary Ruin & Stochastic Recreate, equipped with a Exponential Monte Carlo acceptance criterion control mechanism, to a real-world employee scheduling problem. The combinatorial possibilities of parameterisation are very large - the Taguchi design of experiments method is used to examine a subset of those possibilities within a limited runtime budget. Evolutionary Ruin and Stochastic Recreate has not previously been applied to the specific scheduling domain of employee scheduling and rostering: the effect of different parameter values on runtime behaviour is investigated. The proposed approach is able to find close to optimal solutions to shift scheduling and employee rostering problems.