Description:
AbstractA new vehicle routing problem (VRP), called the familiarity vehicle routing problem (FVRP) is introduced in this paper. Practical logistical challenges arise when the routes assigned to vehicle drivers differ significantly from one day to the next, resulting in increased travel times and subsequent degraded operational efficiency. The FVRP is aimed at streamlining the practical implementation of planned delivery schedules by creating driver-route familiarity of the routes assigned to drivers. This is achieved in two phases, a strategic phase and an operational phase. The strategic phase is concerned with determining a set of standard routes, called master routes, with which vehicle drivers may become familiar in the future. The operational phase is then concerned with computing actual delivery routes to be as similar as possible to appropriate portions of the master routes, in order for vehicle drivers to be familiar with the routes along which they travel and also increase their familiarity of these routes over time. A novel mixed integer linear programming (MILP) model and a metaheuristic are proposed and tested for each phase of the FVRP. Finally, a case study is performed in which real-world data are provided as input to the proposed metaheuristics