Resource Allocation and Patrol Scheduling of Recovery Workers in Large-scale Disasters using Hierarchical Optimization

Abstract

In the event of a large-scale disaster, numerous equipment recovery orders arise simultaneously, and efficient recovery planning is necessary for the timely completion of recovery work. In this study, we developed code to solve the scheduling problem of recovery workers' tours using hierarchical optimization to achieve efficient recovery planning. To verify the code, we set a sub-problem to minimize the required time for work in each sub-area using genetic algorithm and compared the results by executing the code under the conditions of the CVRP benchmark problem. Our study successfully derived approximate solutions for the scheduling problem in each sub-area using hierarchical optimization, which can contribute to efficient recovery planning. In the future, we expect to achieve a more realistic problem setting by introducing constraints such as resource allocation optimization, work priority, and work skills into the higher-level problem.