The Trade Off Between Diversity and Quality for Multi-objective Workforce Scheduling

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 probl...

• Type: Conference
• Year: 2006
• Where: EVOW
• Authors: Peter I. Cowling, Nic Colledge, Keshav P. Dahal, Stephen Remde
• Keywords: Artificial Intelligence, EVOW 2006, Objective Weights, Scheduling Problem, Workforce Scheduling Problem