A Greedy Randomized Adaptive Search for the Surveillance Patrol Vehicle Routing Problem

Abstract

In this chapter a new rich vehicle routing problem is introduced, the Surveillance Patrol Vehicle Routing Problem (SPVRP) . This problem came out from a real need of a surveillance company to create fairer routing plans for its security patrols. The problem consist into routing a set of patrols in order to visit a set of checkpoints. Each checkpoint requires one or more visits, each one of which, to be performed within a fixed time window. A minimum time spacing between two consecutive visits should be observed. The goal is to minimize cost while minimizing, at the same time, time windows and minimum spacing constraints violations. In order to avoid repetitiveness in the routes and to provide more unpredictable routing plans, the company looks for a pool of sensibly different high quality solutions form which, each night they can choose the routing plan to be followed. To address this problem a Greedy Randomized Adaptive Search algorithm (GRASP) , is used to provide good solutions and a further GRASP algorithm is used to generate pools of good solutions. The quality of a pool is measured both in terms of averaged quality of the solutions in the pools and in terms of diversity among each others. Experimental tests on real instances are reported.