Dynamic rerouting of vehicles during cooperative wildfire response operations

Incident managers assigning wildfire response vehicles to provide protection to community assets may experience disruptions to their plans arising from factors such as changes in weather, vehicle breakdowns or road closures. We develop an approach to rerouting wildfire response vehicles once a disruption has occurred. The aim is to maximise the total value of assets protected while minimising changes to the original vehicle assignments. A number of functions to measure deviations from the original plans are proposed. The approach is demonstrated using a realistic fire scenario impacting South Hobart, Tasmania, Australia. Computational testing shows that realistic sized problems can be solved within a reasonable time using a commercial solver

A spatial decomposition based math-heuristic approach to the asset protection problem

This paper addresses the highly critical task of planning asset protection activities during uncontrollable wildfires known in the literature as the Asset Protection Problem (APP). In the APP each asset requires a protective service to be performed by a set of emergency response vehicles within a specific time period defined by the spread of fire. We propose a new spatial decomposition based math-heuristic approach for the solution of large-scale APPs. The heuristic exploits the property that time windows are geographically correlated as fire spreads across a landscape. Thus an appropriate division of the landscape allows the problem to be decomposed into smaller more tractable sub-problems. The main challenge then is to minimise the difference between the final locations of vehicles from one division to the optimal starting locations of the next division. The performance of the proposed approach is tested on a set of benchmark instances from the literature and compared to the most recent Adaptive Large Neighborhood Search (ALNS) algorithm developed for the APP. The results show that our proposed solution approach outperforms the ALNS algorithm on all instances with comparable computation time. We also see a trend with the margin of out-performance becoming more significant as the problems become larger

An adaptive large neighbourhood search for asset protection during escaped wildfires

The asset protection problem is encountered where an uncontrollable fire is sweeping across a landscape comprising important infrastructure assets. Protective activities by teams of firefighters can reduce the risk of losing a particular asset. These activities must be performed during a time-window for each asset determined by the progression of the fire. The nature of some assets is such that they require the simultaneous presence of more than one fire vehicle and its capabilities must meet the requirements of each asset visited. The objective is then to maximise the value of the assets protected subject to constraints on the number and type of fire trucks available. The solution times to this problem using commercial solvers preclude their use for operational purposes. In this work we develop an Adaptive Large Neighbourhood Search algorithm (ALNS) based on problem-specific attributes. Several removal and insertion heuristics, including some new algorithms, are applied. A new benchmark set is generated by considering the problem attributes. In tests with small instances the ALNS is shown to achieve optimal, or near optimal, results in a fraction of the time required by CPLEX. In a second set of experiments comprising larger instances the ALNS was able to produce solutions in times suitable for operational purposes. These solutions mean that significantly more assets can be protected than would be the case otherwise

CONNECTED AND AUTONOMOUS VEHICLES EFFECTS ON EMERGENCY RESPONSE TIMES

Emergency response times have been shown to be directly correlated with mortality rates of out-of-hospital patients. Studies have been conducted to show the relationship between time and mortality rates until patients receive the proper treatment. With more cardiac arrests and other life threatening illnesses occurring in the United States, more emergency calls will be required as well. As of today, technological advancements have been made to reduce response times, but human factors still require certain procedures, causing delays in the run time and increasing the rate of mortality. Here we show the results of emergency response times with the market penetration of connected and autonomous vehicles. With connected and autonomous vehicles, the average time emergency vehicles spend on the roadways can be significantly decreased. Safety procedures with human drivers can be eliminated, giving the emergency vehicle a proper right-of-way through virtual emergency lanes and removing the need to slow down and avoid vehicles at intersections or during periods of heavy congestion. Our results show a three minute decrease in response time under full market penetration of the technology, reducing the mortality rate and increasing the potential to save lives