A Greedy Randomized Adaptive Search Procedure (GRASP) for the multi-vehicle prize collecting arc routing for connectivity problem

Abstract

Natural disasters such as earthquakes can severely impact road networks. Depending on the disaster intensity and the size of the affected area, the network may be divided into multiple disconnected parts. In a disaster response context, decision-makers need to determine the roads that should be unblocked to facilitate relief activities such as search and rescue, evacuation, and distribution of emergency supplies. The multi-vehicle prize collecting arc routing for connectivity problem (KPC-ARCP) is a well-known problem dealing with such a scenario. A matheuristic to solve the KPC-ARCP was proposed in previous research, which tested instances with fewer than 400 vertices and 700 edges. However, it is unknown whether the matheuristic can handle larger instances. This article proposes a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic with the hypothesis that GRASP is faster and can solve more extensive networks. Two sets of tests are performed on randomly generated instances with increasing size. The gap in the objective function values and the execution times of GRASP versus the matheuristic are compared. The results indicate that GRASP can achieve objective function values as good as the matheuristic and is significantly faster depending on the parameter settings.