自提和送货时间范围限制

OptimizeToursRequest 会应用以下限制条件:

  • 装运,这会影响装运方式
  • 车辆,会影响车辆路线的计算方式
  • 在全球范围内,这同时影响了车辆和货运。

本指南重点介绍一个基本的装运限制条件,即时间范围

时间窗口是在 OptimizeToursRequest 消息(RESTgRPC)中提供的一种限制条件,用于指定运输活动基于时间的限制。这种限制条件会影响配送的执行时间和方式以及运单的车辆分配。有了这些约束条件,优化器会优先考虑最能满足运送时间限制的车辆。

配送限制:时间范围

您可以在 Shipment.VisitRequest 消息中指定自提或送餐时间,如下所示:

  • 在消息中使用 timeWindows 属性(RESTgRPC
  • TimeWindow 消息(RESTgRPC)中指定开始时间和结束时间。

具有时间范围限制的请求示例

此处的示例展示了三种不同的运单,每种运单都有自己的送货时间范围。为简单起见,此示例仅在 deliveries 上设置时间范围,但也可以将时间范围应用于上车点。可以指定多个时间范围,但此示例仅使用每个传送 VisitRequest 中的一个时间范围。

查看带有时间范围的请求示例

{
  "populatePolylines": false,
  "populateTransitionPolylines": false,
  "model": {
    "globalStartTime": "2023-01-13T16:00:00Z",
    "globalEndTime": "2023-01-14T16:00:00Z",
    "shipments": [
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.789456,
              "longitude": -122.390192
            },
            "duration": "250s",
            "timeWindows": [
              {
                "startTime": "2023-01-13T18:00:00Z",
                "endTime": "2023-01-13T19:00:00Z"
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 100.0
      },
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.789116,
              "longitude": -122.395080
            },
            "duration": "250s",
            "timeWindows": [
              {
                "startTime": "2023-01-13T18:00:00Z",
                "endTime": "2023-01-13T18:30:00Z"
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 20.0
      },
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.795242,
              "longitude": -122.399347
            },
            "duration": "250s",
            "timeWindows": [
              {
                "startTime": "2023-01-13T17:30:00Z",
                "endTime": "2023-01-13T18:00:00Z"
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 50.0
      }
    ],
    "vehicles": [
      {
        "endLocation": {
          "latitude": 37.794465,
          "longitude": -122.394839
        },
        "startLocation": {
          "latitude": 37.794465,
          "longitude": -122.394839
        },
        "costPerHour": 40.0,
        "costPerKilometer": 10.0
      }
    ]
  }
}
    

具有时间范围限制的响应示例

在示例响应中,车辆的开始时间和结束时间分别为 17:35:50 和 18:17:24。这些时间反映了优化器最大限度地减少操作在请求中指定的车辆所需的时间 costPerHour,同时满足所有时间范围限制。如果使用 17:35:50 作为开始时间,则在访问的时间范围开始后,车辆无需在访问地点等待。这在响应中显示为零个 waitDuration 值。

查看对示例请求的响应(带有时间范围)

{
  "routes": [
    {
      "vehicleStartTime": "2023-01-13T17:35:50Z",
      "vehicleEndTime": "2023-01-13T18:17:24Z",
      "visits": [
        {
          "isPickup": true,
          "startTime": "2023-01-13T17:35:50Z",
          "detour": "0s"
        },
        {
          "shipmentIndex": 1,
          "isPickup": true,
          "startTime": "2023-01-13T17:38:20Z",
          "detour": "150s"
        },
        {
          "shipmentIndex": 2,
          "isPickup": true,
          "startTime": "2023-01-13T17:40:50Z",
          "detour": "300s"
        },
        {
          "shipmentIndex": 2,
          "startTime": "2023-01-13T17:50:09Z",
          "detour": "0s"
        },
        {
          "shipmentIndex": 1,
          "startTime": "2023-01-13T18:00:00Z",
          "detour": "796s"
        },
        {
          "startTime": "2023-01-13T18:07:35Z",
          "detour": "1520s"
        }
      ],
      "transitions": [
        {
          "travelDuration": "0s",
          "waitDuration": "0s",
          "totalDuration": "0s",
          "startTime": "2023-01-13T17:35:50Z"
        },
        {
          "travelDuration": "0s",
          "waitDuration": "0s",
          "totalDuration": "0s",
          "startTime": "2023-01-13T17:38:20Z"
        },
        {
          "travelDuration": "0s",
          "waitDuration": "0s",
          "totalDuration": "0s",
          "startTime": "2023-01-13T17:40:50Z"
        },
        {
          "travelDuration": "409s",
          "travelDistanceMeters": 1371,
          "waitDuration": "0s",
          "totalDuration": "409s",
          "startTime": "2023-01-13T17:43:20Z"
        },
        {
          "travelDuration": "341s",
          "travelDistanceMeters": 1312,
          "waitDuration": "0s",
          "totalDuration": "341s",
          "startTime": "2023-01-13T17:54:19Z"
        },
        {
          "travelDuration": "205s",
          "travelDistanceMeters": 636,
          "waitDuration": "0s",
          "totalDuration": "205s",
          "startTime": "2023-01-13T18:04:10Z"
        },
        {
          "travelDuration": "339s",
          "travelDistanceMeters": 1276,
          "waitDuration": "0s",
          "totalDuration": "339s",
          "startTime": "2023-01-13T18:11:45Z"
        }
      ],
      "metrics": {
        "performedShipmentCount": 3,
        "travelDuration": "1294s",
        "waitDuration": "0s",
        "delayDuration": "0s",
        "breakDuration": "0s",
        "visitDuration": "1200s",
        "totalDuration": "2494s",
        "travelDistanceMeters": 4595
      },
      "routeCosts": {
        "model.vehicles.cost_per_hour": 27.711111111111112,
        "model.vehicles.cost_per_kilometer": 45.95
      },
      "routeTotalCost": 73.661111111111111
    }
  ],
  "metrics": {
    "aggregatedRouteMetrics": {
      "performedShipmentCount": 3,
      "travelDuration": "1294s",
      "waitDuration": "0s",
      "delayDuration": "0s",
      "breakDuration": "0s",
      "visitDuration": "1200s",
      "totalDuration": "2494s",
      "travelDistanceMeters": 4595
    },
    "usedVehicleCount": 1,
    "earliestVehicleStartTime": "2023-01-13T17:35:50Z",
    "latestVehicleEndTime": "2023-01-13T18:17:24Z",
    "totalCost": 73.661111111111111,
    "costs": {
      "model.vehicles.cost_per_hour": 27.711111111111112,
      "model.vehicles.cost_per_kilometer": 45.95
    }
  }
}
    

时间范围已对车辆的 visits 进行排序,以便首先交付时间范围最早的货物。

  1. shipments[2]在 17:50 送达
  2. shipments[1]在 18:00 送达
  3. shipments[0]在 18:07 送达

示例请求指定了硬性时间范围限制,要求交付会在这些时间范围内完成。如果在某个时间范围内完成运单的 VisitRequests 不可行或不具备成本效益,优化器会跳过该运单。如果运单具有 penaltyCost,优化器会将其添加到响应 metrics 中报告的费用。否则,OptimizeToursResponse 消息(RESTgRPC)的 skippedMandatoryShipmentCount 属性会增加。

如果您通过将 shipment[1] 的窗口移到几小时后(从 18:00 改为 21:00)来更改时间范围,如以下示例所示。

查看无法满足时间范围的请求示例

{
  "populatePolylines": false,
  "populateTransitionPolylines": false,
  "model": {
    "globalStartTime": "2023-01-13T16:00:00Z",
    "globalEndTime": "2023-01-14T16:00:00Z",
    "shipments": [
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.789456,
              "longitude": -122.390192
            },
            "duration": "250s",
            "timeWindows": [
              {
                "startTime": "2023-01-13T18:00:00Z",
                "endTime": "2023-01-13T19:00:00Z"
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 100.0
      },
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.789116,
              "longitude": -122.395080
            },
            "duration": "250s",
            "timeWindows": [
              {
                "startTime": "2023-01-13T21:00:00Z",
                "endTime": "2023-01-13T21:30:00Z"
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 20.0
      },
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.795242,
              "longitude": -122.399347
            },
            "duration": "250s",
            "timeWindows": [
              {
                "startTime": "2023-01-13T17:30:00Z",
                "endTime": "2023-01-13T18:00:00Z"
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 50.0
      }
    ],
    "vehicles": [
      {
        "endLocation": {
          "latitude": 37.794465,
          "longitude": -122.394839
        },
        "startLocation": {
          "latitude": 37.794465,
          "longitude": -122.394839
        },
        "costPerHour": 40.0,
        "costPerKilometer": 10.0
      }
    ]
  }
}
    

查看对第二个示例请求的响应(带有时间范围,其中跳过了发货)

{
  "routes": [
    {
      "vehicleStartTime": "2023-01-13T17:37:49Z",
      "vehicleEndTime": "2023-01-13T18:09:49Z",
      "visits": [
        {
          "isPickup": true,
          "startTime": "2023-01-13T17:37:49Z",
          "detour": "0s"
        },
        {
          "shipmentIndex": 2,
          "isPickup": true,
          "startTime": "2023-01-13T17:40:19Z",
          "detour": "150s"
        },
        {
          "shipmentIndex": 2,
          "startTime": "2023-01-13T17:49:38Z",
          "detour": "0s"
        },
        {
          "startTime": "2023-01-13T18:00:00Z",
          "detour": "946s"
        }
      ],
      "transitions": [
        {
          "travelDuration": "0s",
          "waitDuration": "0s",
          "totalDuration": "0s",
          "startTime": "2023-01-13T17:37:49Z"
        },
        {
          "travelDuration": "0s",
          "waitDuration": "0s",
          "totalDuration": "0s",
          "startTime": "2023-01-13T17:40:19Z"
        },
        {
          "travelDuration": "409s",
          "travelDistanceMeters": 1371,
          "waitDuration": "0s",
          "totalDuration": "409s",
          "startTime": "2023-01-13T17:42:49Z"
        },
        {
          "travelDuration": "372s",
          "travelDistanceMeters": 1348,
          "waitDuration": "0s",
          "totalDuration": "372s",
          "startTime": "2023-01-13T17:53:48Z"
        },
        {
          "travelDuration": "339s",
          "travelDistanceMeters": 1276,
          "waitDuration": "0s",
          "totalDuration": "339s",
          "startTime": "2023-01-13T18:04:10Z"
        }
      ],
      "metrics": {
        "performedShipmentCount": 2,
        "travelDuration": "1120s",
        "waitDuration": "0s",
        "delayDuration": "0s",
        "breakDuration": "0s",
        "visitDuration": "800s",
        "totalDuration": "1920s",
        "travelDistanceMeters": 3995
      },
      "routeCosts": {
        "model.vehicles.cost_per_kilometer": 39.95,
        "model.vehicles.cost_per_hour": 21.333333333333332
      },
      "routeTotalCost": 61.283333333333331
    }
  ],
  "skippedShipments": [
    {
      "index": 1
    }
  ],
  "metrics": {
    "aggregatedRouteMetrics": {
      "performedShipmentCount": 2,
      "travelDuration": "1120s",
      "waitDuration": "0s",
      "delayDuration": "0s",
      "breakDuration": "0s",
      "visitDuration": "800s",
      "totalDuration": "1920s",
      "travelDistanceMeters": 3995
    },
    "usedVehicleCount": 1,
    "earliestVehicleStartTime": "2023-01-13T17:37:49Z",
    "latestVehicleEndTime": "2023-01-13T18:09:49Z",
    "totalCost": 81.283333333333331,
    "costs": {
      "model.shipments.penalty_cost": 20,
      "model.vehicles.cost_per_hour": 21.333333333333332,
      "model.vehicles.cost_per_kilometer": 39.95
    }
  }
}
    

在此示例中,较晚的时间窗口导致 shipment[1] 被跳过,因为在指定时间范围内完成配送订单所需的额外车辆操作时间超过了配送的罚金。shipment[1] 的处罚费用显示在 metrics.costs 中,其索引显示在 skippedShipments 中。

软性时间范围限制

费用模型参数中简要提到的,时间范围可应用为软约束。软约束条件与硬性约束条件有以下不同:

  • 硬性约束:不能违反,且优化器不会提供违反约束条件的解决方案,即使这意味着跳过配送。
  • 软约束:可能会违反,这意味着优化器可能会提供违反软约束的解决方案。不过,优化器还会对所有违规行为应用费用。您可以在时间窗口中将此费用作为附加属性提供,通常作为活动发生时间窗口之前或之后每小时的费用。

可通过分别使用 softStartTimesoftEndTime(而非 startTimeendTime)和设置 costPerHourBeforeSoftStartTimecostPerHourAfterSoftEndTime 来软化时间窗口。

如果自提或配送应该在指定时间范围内进行,但并非必须要在该时间范围内自提或配送,请使用软时间范围限制。您可以结合使用硬的时间窗口限制和软时间限制来表达业务目标。例如:

  • 硬性时间范围:表示客户的营业时间,例如上午 9 点至下午 5 点。
  • 软时间窗口:指示与发送给客户的通知匹配的送货或自提时间范围,例如上午 9 点至下午 1 点。

在此示例中,之前因时间范围开始太晚而跳过的运单已软化其开始时间限制。其他配送的时间范围的结束时间也缩短了。

查看具有硬时间窗口和软时间窗口的请求示例

{
  "populatePolylines": false,
  "populateTransitionPolylines": false,
  "model": {
    "globalStartTime": "2023-01-13T16:00:00Z",
    "globalEndTime": "2023-01-14T16:00:00Z",
    "shipments": [
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.789456,
              "longitude": -122.390192
            },
            "duration": "250s",
            "timeWindows": [
              {
                "startTime": "2023-01-13T18:00:00Z",
                "softEndTime": "2023-01-13T19:00:00Z",
                "costPerHourAfterSoftEndTime": 2.0
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 100.0
      },
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.789116,
              "longitude": -122.395080
            },
            "duration": "250s",
            "timeWindows": [
              {
                "softStartTime": "2023-01-13T21:00:00Z",
                "endTime": "2023-01-13T21:30:00Z",
                "costPerHourBeforeSoftStartTime": 2.0
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 20.0
      },
      {
        "deliveries": [
          {
            "arrivalLocation": {
              "latitude": 37.795242,
              "longitude": -122.399347
            },
            "duration": "250s",
            "timeWindows": [
              {
                "startTime": "2023-01-13T17:30:00Z",
                "softEndTime": "2023-01-13T18:00:00Z",
                "costPerHourAfterSoftEndTime": 2.0
              }
            ]
          }
        ],
        "pickups": [
          {
            "arrivalLocation": {
              "latitude": 37.794465,
              "longitude": -122.394839
            },
            "duration": "150s"
          }
        ],
        "penaltyCost": 50.0
      }
    ],
    "vehicles": [
      {
        "endLocation": {
          "latitude": 37.794465,
          "longitude": -122.394839
        },
        "startLocation": {
          "latitude": 37.794465,
          "longitude": -122.394839
        },
        "costPerHour": 40.0,
        "costPerKilometer": 10.0
      }
    ]
  }
}
    

查看对具有硬性和软性时间范围的示例请求的响应

{
  "routes": [
    {
      "vehicleStartTime": "2023-01-13T17:48:35Z",
      "vehicleEndTime": "2023-01-13T18:24:28Z",
      "visits": [
        {
          "isPickup": true,
          "startTime": "2023-01-13T17:48:35Z",
          "detour": "0s"
        },
        {
          "shipmentIndex": 1,
          "isPickup": true,
          "startTime": "2023-01-13T17:51:05Z",
          "detour": "150s"
        },
        {
          "shipmentIndex": 2,
          "isPickup": true,
          "startTime": "2023-01-13T17:53:35Z",
          "detour": "300s"
        },
        {
          "startTime": "2023-01-13T18:00:00Z",
          "detour": "300s"
        },
        {
          "shipmentIndex": 1,
          "startTime": "2023-01-13T18:07:42Z",
          "detour": "493s"
        },
        {
          "shipmentIndex": 2,
          "startTime": "2023-01-13T18:17:27Z",
          "detour": "873s"
        }
      ],
      "transitions": [
        {
          "travelDuration": "0s",
          "waitDuration": "0s",
          "totalDuration": "0s",
          "startTime": "2023-01-13T17:48:35Z"
        },
        {
          "travelDuration": "0s",
          "waitDuration": "0s",
          "totalDuration": "0s",
          "startTime": "2023-01-13T17:51:05Z"
        },
        {
          "travelDuration": "0s",
          "waitDuration": "0s",
          "totalDuration": "0s",
          "startTime": "2023-01-13T17:53:35Z"
        },
        {
          "travelDuration": "235s",
          "travelDistanceMeters": 795,
          "waitDuration": "0s",
          "totalDuration": "235s",
          "startTime": "2023-01-13T17:56:05Z"
        },
        {
          "travelDuration": "212s",
          "travelDistanceMeters": 791,
          "waitDuration": "0s",
          "totalDuration": "212s",
          "startTime": "2023-01-13T18:04:10Z"
        },
        {
          "travelDuration": "335s",
          "travelDistanceMeters": 1204,
          "waitDuration": "0s",
          "totalDuration": "335s",
          "startTime": "2023-01-13T18:11:52Z"
        },
        {
          "travelDuration": "171s",
          "travelDistanceMeters": 665,
          "waitDuration": "0s",
          "totalDuration": "171s",
          "startTime": "2023-01-13T18:21:37Z"
        }
      ],
      "metrics": {
        "performedShipmentCount": 3,
        "travelDuration": "953s",
        "waitDuration": "0s",
        "delayDuration": "0s",
        "breakDuration": "0s",
        "visitDuration": "1200s",
        "totalDuration": "2153s",
        "travelDistanceMeters": 3455
      },
      "routeCosts": {
        "model.shipments.deliveries.time_windows.cost_per_hour_after_soft_end_time": 0.58166666666666667,
        "model.shipments.deliveries.time_windows.cost_per_hour_before_soft_start_time": 5.7433333333333332,
        "model.vehicles.cost_per_hour": 23.922222222222221,
        "model.vehicles.cost_per_kilometer": 34.55
      },
      "routeTotalCost": 64.797222222222217
    }
  ],
  "metrics": {
    "aggregatedRouteMetrics": {
      "performedShipmentCount": 3,
      "travelDuration": "953s",
      "waitDuration": "0s",
      "delayDuration": "0s",
      "breakDuration": "0s",
      "visitDuration": "1200s",
      "totalDuration": "2153s",
      "travelDistanceMeters": 3455
    },
    "usedVehicleCount": 1,
    "earliestVehicleStartTime": "2023-01-13T17:48:35Z",
    "latestVehicleEndTime": "2023-01-13T18:24:28Z",
    "totalCost": 64.797222222222217,
    "costs": {
      "model.vehicles.cost_per_kilometer": 34.55,
      "model.shipments.deliveries.time_windows.cost_per_hour_before_soft_start_time": 5.7433333333333332,
      "model.shipments.deliveries.time_windows.cost_per_hour_after_soft_end_time": 0.58166666666666667,
      "model.vehicles.cost_per_hour": 23.922222222222221
    }
  }
}
    

如果仅具有硬性时间范围约束的示例完全跳过了 shipment[1],则软化其传送时间范围会导致其在时间范围开始时间之前传送。同样,通过软化其他运单的结束时间,shipment[2] 可以在其时间范围结束后送达。

同时,费用和总运费都发生了变化:

  • totalCost:从 81.283 下降到 64.797
  • 已完成的运单总数:增加了 2 个到 3 个

与上一个示例相比,优化器发现了一个费用更低的解决方案,因为时间限制放宽了。

最后,metrics.costs 属性还包含一个新键,用于指示根据约束的乘积和错过送货窗口的时长而产生的实际费用。具体来说:

  • costPerHourBeforeSoftStartTime/2.0 和
  • 从实际投放时间到时间段开始的时间: 2.83583 小时

结果:

model.shipments.deliveries.time_windows.cost_per_hour_before_soft_start_time:5.6716666666666669。

通过这些指标,您可以进行费用分析,了解硬性约束条件与软性约束条件之间的权衡取舍,然后据此调整约束条件,使其更好地契合您的特定业务规则。在这种情况下,总费用小于 shipment[1].penalty_cost 20.0。优化器发现,与跳过装运相比,提早交付货运的性价比更高