An iterated local search for a multi‐period orienteering problem arising in a car patrolling application

Abstract This paper addresses a real‐world multi‐period orienteering problem arising in large Italian company that needs to patrol an area order provide security services set of customers. Each customer requires different on weekly basis. Some are mandatory, while others optional. It might be impossible perform all optional services, and each them is assigned score when performed. The challenge determine routes, one per day, maximizes weighted sum the total collected working time, meeting several operational constraints, including hard time windows, maximum riding minimum number performed, between two consecutive visits for same service at customer. To solve problem, we propose iterated local search invokes iteration inner variable neighborhood descent procedure. Computational tests performed instances prove developed algorithm very efficient, finds short solutions consistently better than those produced by mathematical model, use company.