A continuous approximation approach for assessment routing in disaster relief
In this paper, we focus on the assessment routing problem which routes teams to different communities to assess damage and relief needs following a disaster. To address time-sensitivity, the routing problem is modeled with the objective of minimizing the sum of arrival times to beneficiaries. We propose a continuous approximation approach which uses aggregated instance data to develop routing policies and cost approximations. Numerical tests are performed that demonstrate the effectiveness of the cost approximations at predicting the true implementation costs of the policies and compare the policies against more complex solution approaches. The continuous approximation approach yields solutions which can be easily implemented; further, this approach reduces the need for detailed data and the computational requirements to solve the problem.
Data gathered is compared to a tabu search solution
Literature review, Algorithm development, Testing
CostImplementation complexitysum of arrival times
Literature review of existing SolutionsDevelopment of the new algorithm for VRP Evaluation with artificial tests, with regards to the objective function. Comparison with tabu search
computational tests to evaluate the approximation and the performance of the continuous approximation modelTest based on synthetic data
In this study, we study the assessment routing problem (ARP), which focuses on routing of assessment teams in the disaster area. The objective of the ARP is to minimize the sum of arrival times at communities, which reflects the time sensitive nature of relief efforts. We develop a continuous approximation model for the ARP.
While a simplified setting is used to illustrate the analysis, the ideas presented can be adapted for service regions of different dimensions, different locations of the depot and non-constant demand densities.
we show how a continuous model may be used to generate easy-to-solve policies for the ARP, approximations to evaluate these policies, and hybrid solutions which can be generated with modest computing resourcesOur numerical tests show that the approximations for the policy can approximate the true cost of implementing the policy quickly and accuratelyThe successful use of aggregate data also suggest that the policy solutions are insensitive to parameter error, which is important in the humanitarian context given the limited information.
we focus on the assessment routing problem which routes teams to different communities to assess damage and relief needs following a disaster
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