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>

实例命名规则如下例所示："100_3_1.3"代表100名用户、该基准测试集的第3个含100用户的实例，且最大乘车时长系数设为1.3。本次共生成24个实例：其中8个实例包含100名用户，8个实例包含200名用户，剩余8个实例包含400名用户。

每位用户拥有两项出行请求，分别为早间请求与午间请求。取送地点均随机分布于(-10,10)的二维网格内。对于单个用户而言，早间上车地点对应午间下车地点，早间下车地点则对应午间上车地点。早间与午间的出行载荷均为1，且上下车环节的服务时长固定为3分钟。

用户的最大乘车时长为其直达出行时长乘以系数（系数取值为1.2、1.3、1.4、1.5）。单项请求的最大乘车时长等于用户的最大乘车时长减去其配对请求的直达出行时长。此外，所有低于15分钟的最大乘车时长将被统一调整为15分钟。

首先设置早间下车与午间上车的时间窗（time windows）。早间下车环节的最大时间窗基于以早8点为均值、方差为90分钟²的正态分布随机选取；同理，午间上车环节的最小时间窗基于以下午4点为均值、方差为90分钟²的正态分布随机选取。两类时间窗的开放时长均为10分钟。随后，早间上车与午间下车的时间窗将遵循Cordeau（2006）提出的时间窗收紧流程进行设置。

地图中心设有一处配送中心（depot），该站点的服务时长为0分钟。拥有一支规模无限的同质车辆车队，所有车辆均从该配送中心出发并最终返回。每辆车的载客容量为6名乘客，每公里运营成本为0.25欧元，每分钟运营成本为0.5欧元，若当日投入使用则需收取50欧元的固定成本。