Multi-objective decision analytics for short-notice bushfire evacuation: An Australian case study

Abstract

Late evacuation in a bushfire is a crucial stage of emergency response, which requires a quick response under life-threatening conditions. Lack of operational intelligence and decision support system to execute evacuation of residents within a 10 minutes time window were among key factors, which contributed to the loss of 119 lives (68 percent of total fatalities) during the Black Saturday 2009 bushfire in Victoria. Hard constraints of the limited time window, the uncertainty of bushfire spread, road disruptions and elderly and disabled late evacuees pose multiple challenges for fire agencies. An application of analytics and decision support system, capable of computing Just-in-time allocation of resources, can enhance the capacity of fire services agencies. Using the multi-objective analytics, this paper therefore develops optimal resource allocation models to enhance emergency response to improve the efficiency of late evacuation response. Three key operational challenges are tackled including timely evacuation, shelter assignment and routing. Three bushfire scenarios are constructed to incorporate constraints of restricted time-window and potential road disruptions. Capacity and number of rescue vehicles and shelters are other constraints. This mathematical model is solved by the application of the ε- constraint approach. Objective functions are simultaneously optimised to maximise the total number of evacuees while minimise the number of assigned rescue vehicles and shelters. We argue that this model provides a scenario-based decision-making tool to aid maximise the resource utilisation and coverage of late evacuees. The analytics based insights drawn from various disruption scenarios could help emergency services agencies in identifying appropriate strategies to improve the efficiency of late evacuation response.