An ant colony optimization approach to the multi-vehicle prize-collecting arc routing for connectivity problem

Blocked roads can jeopardize emergency response activities in the aftermath of disasters, such as earthquakes. In many cases, the distribution of relief supplies to affected communities, the evacuation of victims, and Search and Rescue activities cannot be resumed due to the lack of connectivity of the road network. The multi-vehicle prize collecting arc routing for connectivity problem (KPC-ARCP) aims to determine the routes of synchronized road clearing teams during the immediate response of a disaster, to reconnect the network so that the utility of the reconnection is maximized. This paper proposes an Ant Colony Optimization (ACO) algorithm to solve the KPC-ARCP and compares its performance to results of earlier studies which apply GRASP and Matheuristic methods to solve the problem. Performance comparisons consider the computation time and accuracy of the solution, and are implemented on academic and real cases in Istanbul. The runs on academic and real-world instances indicate that ACO has a reasonable performance compared to the existing methods. However, the high complexity of its parameter tuning suggests that GRASP is likely more suitable for KPC-ARCP.