自提和送货时间范围限制

OptimizeToursRequest 可在以下方面应用限制:

  • 货件,影响货件的运输方式
  • 车辆,影响车辆路线的计算方式
  • 在全球范围内影响车辆和货件。

本指南重点介绍一项重要的配送限制:时间段

时间范围是一种约束条件,您可以在 OptimizeToursRequest 消息(RESTgRPC)中提供这种约束条件,以便为配送活动指定基于时间的限制。此类限制会影响配送的时间和方式,以及配送的车辆分配。有了这些限制,优化器会优先考虑最能满足货件时间限制的车辆。

货件限制:时间窗口

您可以在 Shipment.VisitRequest 消息中指定可以取件或送达的时间,如下所示:

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

包含时间窗口限制的请求示例

此处的示例展示了三批不同的货物,每批货物都有自己的送货时段。为简单起见,此示例仅针对 deliveries 设置了时间窗口,但时间窗口也可应用于取货。您可以指定多个时间窗口,不过此示例中每个配送 VisitRequest 仅使用一个时间窗口。

查看包含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",
                "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
      }
    ]
  }
}
    

包含时间窗口限制的响应示例

在示例回答中,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在满足所有时间窗口限制条件的同时,最大限度缩短了操作请求中指定车辆 (costPerHour) 所需的时间。使用 17:35:50 作为开始时间,可避免车辆在拜访地点等待,直到拜访时间窗口开始。在响应中,这会显示为零个 waitDuration 值。

查看对包含时间窗口的示例请求的响应

{
  "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
    }
  }
}
    

时间窗口已对车辆的 visits 进行排序,以便先交付时间窗口最早的货件。

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

示例请求指定了硬性时间窗口限制,要求在这些窗口内完成配送。如果无法在任何时间窗口内完成货件的 VisitRequests,或者这样做不经济实惠,优化器会跳过该货件。如果相应货件具有 penaltyCost,优化器会将其添加到响应 metrics 中报告的费用中。否则,OptimizeToursResponse 消息(RESTgRPC)的 skippedMandatoryShipmentCount 属性会增加。

如果您将时间窗口向后移动几个小时(从 18:00 移至 21:00),结果会有所不同,如以下示例所示。shipment[1]

查看无法满足时间窗口的示例请求

{
  "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(包含时间窗口),其中跳过了某次配送

{
  "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,由于在指定时间范围内完成配送所需的额外车辆运营时间超过了货件的违规费用,因此系统跳过了较晚的时间窗口 shipment[1]shipment[1] 的罚款费用显示在 metrics.costs 中,其指数显示在 skippedShipments 中。

软时间窗口限制

费用模型参数中所述,时间窗口可以作为约束条件应用。软性限制与硬性限制的区别如下:

  • 硬性限制条件:不得违反,即使这意味着跳过某次配送,优化器也不会提供违反该限制条件的解决方案。
  • 软约束:可能会被违反,这意味着优化器可能会提供违反软约束的解决方案。不过,优化器还会对任何违规行为应用费用。您可以在时间窗口中以附加属性的形式提供此费用,通常以每小时费用表示,适用于活动发生时间之前或之后的每个小时。

通过使用 softStartTimesoftEndTime(而非 startTimeendTime)并设置 costPerHourBeforeSoftStartTimecostPerHourAfterSoftEndTime,可以柔化时间窗口。

如果取货或送货在指定时间窗口内进行,但并非必须在该时间窗口内取货或送货,请使用软时间窗口限制。您可以同时使用硬性和软性时间窗口限制来表达业务目标。例如:

  • 硬性时间窗口:表示客户的营业时间,例如上午 9 点到下午 5 点。
  • 软时间范围:表示与发送给客户的通知相符的送货或自提时间范围,例如上午 9 点到下午 1 点。

在此示例中,之前因时间窗口开始时间过晚而被跳过的配送的开始时间限制放宽了。其他货件的时间窗口结束时间也已放宽。

查看包含硬性和软性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
      }
    ]
  }
}
    

查看对包含硬性和软性时间窗口的示例请求的响应

{
  "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
    }
  }
}
    

在仅包含硬性时间窗口限制的示例中,shipment[1] 被完全跳过,而放宽其配送时间窗口后,shipment[1] 将在时间窗口开始时间之前配送。同样,放宽其他货件的结束时间后,shipment[2] 便可在时间窗口结束后交付。

与此同时,费用和总货件数都发生了变化:

  • totalCost:从 81.283降至 64.797
  • 已完成的总发货量:从 2 增加到 3

与上一个示例相比,由于时间窗口限制放宽,优化器找到了成本更低的解决方案。

最后,metrics.costs 属性还包含一个新键,用于指示根据限制和错过的配送时段长度计算出的实际费用。具体来说:

  • 2.0 的 costPerHourBeforeSoftStartTime
  • 实际送达时间与时间范围开始时间之间的间隔:2.83583 小时

结果:

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

借助这些指标,您可以进行成本分析,了解硬性限制与软性限制之间的权衡,从而调整限制,使其更符合您的特定业务规则。在这种情况下,总费用低于 20.0 的 shipment[1].penalty_cost。优化器已确定,提前发货比不发货更具成本效益