Towards Addressing Dynamic Multi-agent Task Allocation in Law Enforcement

To deal with the underlying heterogeneous law enforcement problem (LEP H), one needs to allocate police officers to dynamic tasks whose locations, arrival times, and importance levels are unknown a priory. Addressing this challenge and inspired by real police logs, this research aims to solve the LEP H problem by using and comparing three methods: Fisher market-based FMC_TA H+ , swarm intelligence HDBA, and Simulated Annealing SA algorithms. The three methods were compared in this study for the performance measures that are commonly used by law enforcement authorities. The results indicate an advantage for FMC_TA H+ both in total utility and in the average arrival time to tasks. Also, compared respectively to HDBA and SA, FMC_TA H+ leads to 34% and 32% higher team utility in the highest shift workload.