In this paper we investigate and compare multi-objective and
weighted single objective approaches to a real world workforce scheduling
problem. For this difficult problem we consider the trade off in solution quality
versus population diversity, for different sets of fixed objective weights. Our
real-world workforce scheduling problem consists of assigning resources with
the appropriate skills to geographically dispersed task locations while satisfying
time window constraints. The problem is NP-Hard and contains the Resource
Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate
a genetic algorithm and serial schedule generation scheme together with
various multi-objective approaches. We show that multi-objective genetic algorithms
can create solutions whose fitness is within 2% of genetic algorithms using
weighted sum objectives even though the multi-objective approaches know
nothing of the weights. The result is highly significant for complex real-world
problems where objective weights are seldom known in advance since it suggests
that a multi-objective approach can generate a solution close to the user
preferred one without having knowledge of user preferences