A Multi-Objective Agent-Based Model for Optimising Polling Place Resource Allocation

Background Long queues at polling stations reduce voter satisfaction and raise the cost of running elections. In Australian federal elections, unpredictable arrival patterns and early-voting behaviour make traditional staffing estimates unreliable, often resulting in either under-utilised staff or wait times that exceed an hour. Objectives (1) Build a framework that couples an agent-based simulation of a polling station with a multi objective optimisation engine to reveal trade-offs between operational resources and voter wait times. (2) Generate Pareto-optimal solutions that balance the number of ballot issuing officers with queue waiting duration. (3) Identify which evolutionary multi-objective algorithm provides the best performance for this problem. Methods An agent-based model of a polling station was created in AnyLogic using arrival profiles and processing times collected from the 2017 Bennelong by-election. Six integer decision variables describe the numbers of ordinary and declaration issuing points, screens and lookup methods. The model was linked to the jMetal library and evaluated with three evolutionary algorithms: NSGA II, SPEA2 and IBEA. Experiments covered voter turnouts from 600 to 2000 in steps of 100, using a population of 50 and 30 generations per run. Solutions were assessed with the hypervolume indicator and a hard constraint that average waiting time must not exceed 25 minutes. Results and Conclusion The optimisation produced sets of trade-off solutions for each turnout level. NSGA II and SPEA2 consistently achieved higher hypervolume values than IBEA, indicating better convergence and diversity. The Pareto-fronts show that adding issuing points shortens peak-time queues but increases staffing costs, while fewer points lengthen waits. The proposed framework delivers scalable, evidence-based staffing recommendations that improve voter experience and control election costs.