A Comparison of Optimization Algorithms for Practical Staff Scheduling Problems in Logistics and Retailing

The current paper uses real-life scenarios of staff scheduling applications to compare the effectiveness and efficiency of two fundamentally different solution approaches. One can be called centralised and is based on search in the solution space with two adapted metaheuristic, namely particle swarm optimization (PSO) and evolution strategy (ES). The second approach, a multi-agent system (MAS), is distributed. PSO and ES outperform MAS. ES often delivers the best overall results in terms of solution quality and is the method of choice, when CPU-time is not limited. MAS is vastly quicker in finding solutions. But the results of MAS are not useful, if the problems are very complex. The results suggest that agents could be an interesting method for real-time scheduling or re-scheduling tasks on problems with less complex constraints.