Tri-Sequence-Based Ant Colony Optimization for Facility Location Allocation in Emergency Medical Service Systems with Unmanned Aerial Vehicles

초록

With the development of unmanned aerial vehicles (UAVs), multiple facilities with heterogeneous UAVs are adopted to build emergency medical services systems. The location and UAV allocation of facilities is critical to cover large-scale demand points, posing challenges to existing methods. Thus, this paper proposes a tri-sequence-based ant colony optimization algorithm to minimize the cost, which adopts a tri-sequence encoding scheme and a corresponding solution construction strategy. Particularly, the tri-sequence encoding scheme adopts an integer sequence to represent the location of opening facilities, an integer sequence to represent the assigned facilities for each demand point, and an integer sequence to represent the number of UAVs assigned to each opening facility. Two pheromone matrices are designed to record the search experience on the location selection of facilities and the assignment relationship between facilities and demand points, respectively. Heuristic information is specifically designed to introduce problem information for accelerating algorithm convergence. Infeasible solutions are repaired to meet constraints. In addition, a local search is developed to further improve solution quality by adjusting the location of facilities and the assignment between facilities and demand points. Experimental results on multiple instances show that the proposed method is significantly better than state-of-the-art algorithms in terms of solution optimality.

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