Com-DARP benchmark.zip

An instance characteristics are read as in the following example : "100_3_1.3" means 100 users, 3rd instance of the benchmark with 100 users, maximum ride time coefficient set to 1.3. In total, we generated 24 instances: 8 instances with 100 users, 8 instances 200 users and 8 instances with 400 users. <br> A user has two requests: one morning request and one afternoon request. The pickups and drop-offs locations are randomly distributed on a (-10, 10) grid. For a user, the morning pickup location corresponds to the afternoon drop-off location and the morning drop-off location corresponds to the afternoon pickup location. The morning and the afternoon loads are both equal to 1 and the service duration is always equal to 3 minutes at pickup and drop-off. A user maximum ride time is set to its direct ride times multiplied by a coefficient (1.2, 1.3, 1.4, 1.5). A request maximum ride time is set to the user maximum ride time minus the pair request direct ride time. Additionally, we raise to 15 minutes any maximum ride time lower than this value. The time windows for the morning drop-off and afternoon pickup are set first. The maximum time window for the morning drop-off is randomly selected according to a normal distribution centered on 8am with a variance of 90 minutes. Similarly, the minimum time window for the afternoon pickup is randomly selected according to a normal distribution centered on 4pm with a variance of 90 minutes. Time windows are opened for 10 minutes. Then, the morning pickup and the afternoon drop-off time windows are set according to the time window tightening procedure described in Cordeau (2006). There is one depot located in the center of the map. The service time is equal to zero minute at the depot. A fleet of infinite homogeneous vehicles leaves and returns to this depot. Each vehicle has a capacity of 6 passengers, cost 0.25€ per kilometer, 0.5€ per minute and has a fixed cost of 50€ if used during the day. <br> <br>