Possible logistic scenarios

This is the default scenario when you need to plan the delivery of goods to your clients or build a route that visits a specified set of points. It's used if there's only one depot in the request, and orders have the delivery type specified in the location field, or this field isn't set (see Order type). The algorithm will build a route for delivery of all orders from the specified depot.

Diagram

Example

There are 30 orders in this solution. All of them are delivered from a single depot. The order type isn't explicitly specified, so its default value is delivery .

API request (JSON) ⋅ API response ⋅ View on map

This is the default scenario when cargo is collected from your clients to be delivered to the depot. For examples, such cargo may include:

• Medical tests that are taken at home.

• Clothes for dry cleaning.

• Goods that your clients return to the depot.

In the last case, this scenario is often combined with delivery from a single depot.

To specify pickup points, specify the order type = pickup (see Order type). After that, different options are possible:

1. All couriers must return to the depot at the end of the day (see Returning to a depot at the end of the working day), meaning return_to_depot = true. In this case, you don't need to configure anything else, and the courier completes the route at the depot by default.
2. The cargo must stay in the courier's vehicle and be returned to the depot the next morning when the courier arrives for their next shift. To do this, set return_to_depot = false. The route will end at the last pickup point. You don't need to configure anything else in this case, either.
3. Not all couriers return to the depot at the end of the day. The algorithm chooses which ones must return. For couriers, the return_to_depot = false option is used, and for pickup orders, you specify whether it's mandatory to return to the depot in the current route pickup_must_reach_depot = true or set up delivery_deadline, which is the time of order delivery to the depot.

Diagram

Example 1

Cargo is picked up and taken to the depot. All vehicles must return to the depot at the end of the day (return_to_depot = true). There are 30 orders, 1 depot, and 5 cargo pickup points.

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Example 2

The same as example 1. One of the vehicles has return_to_depot set to false. This means that the cargo will remain in the courier's vehicle and return to the depot the next morning.

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Example 3

The same as example 1, but different pickup points are selected. For all vehicles, return_to_depot = false, and delivery_deadline is specified for the cargo pickup points. At the end of the day, only the vehicle that has all the pickup orders will return to the depot.

API request (JSON) ⋅ API response ⋅ View on map

When to use this

By default, the courier route starts and ends at the depot. However, in some cases, you may need to plan a route from the vehicle's parking spot or from the courier's home. Or the other way around: you may need to complete the route closer to these points.

How to implement this scenario

Start points are defined using the start_at and visit_depot_at_start parameters. End points are defined using the finish_at and return_to_depot parameters.

The parameters of the start and end points are independent of each other, so you can configure any combination here:

• Set start and end points separately for each courier.

• Specify the same point for multiple couriers.

• Specify the same start and end points.

The garage order type is used to specify the start or end point. If vehicle.visit_depot_at_start = false, and vehicle.start_at isn't specified, the algorithm assumes that the courier can start the route from the first order without visiting the depot.

Schemes for start points

• Starting a route from the order point without going to the depot (start_at isn't defined, visit_depot_at_start = false). The algorithm chooses the start point of the order for the courier.

  
• Starting a route from the depot (start_at isn't defined, visit_depot_at_start = true ). The algorithm's default behavior.

  
• Starting a route from a specific point without going to the depot (start_at is defined, visit_depot_at_start = false). Start points may differ for different couriers.

  
• Starting a route from a specific point with a visit to the depot (start_at is defined, visit_depot_at_start = true). Start points may differ for different couriers.

  

Schemes for end points

• Ending a route at the last point without returning to the depot (finish_at isn't defined, return_to_depot = false ).

  
• Returning to the depot at the end of the route ( finish_at isn't defined, return_to_depot = true ).

  
• Ending a route at a specific point without returning to the depot (finish_at is defined, return_to_depot = false). End points may differ for different couriers.

  
• Ending the route at a specific point with a visit to the depot before the end point (finish_at is defined, return_to_depot = true). End points may differ for different couriers.

  

When to use this

This scenario is used for planning door-to-door cargo delivery without involving intermediate depots or reloading points. For example:

• Goods must be picked up from the supplier's depot and taken to the client (multiple pickup scenarios are supported for when you need to collect the goods from different suppliers at several depots and deliver them to the same client).

• Goods are returned (you need to pick them up from the client and take them to the depot for returns, not to the main depot).

You can also use this scenario in cases where there are simple dependencies between points. For example, before you get to the client, you need to pick up tools, documents, or keys.

You can use this scenario together with the scenarios for collecting cargo at the depot and delivery from a single point.

How to implement this scenario

A distinctive feature of using the pickup & delivery scenario is that there's a pair of linked points, one of which must always be visited before the other: first pickup, then delivery. The points are linked using the delivery_to field: you fill it in for the pickup point, specifying the ID of the associated delivery.

In a multiple pickup scenario for several different pickup points, you can specify the same value in the delivery_to field. A group of multiple pickup orders and one delivery order will always be assigned to a single courier.

If there's one pickup point from which you need to deliver to multiple delivery points, specify different pickup & delivery pairs (the pickup orders will have the same coordinates, but they must have different IDs). Such pickup & delivery pairs can be assigned to different couriers (despite the fact that they have the same pickup point). For mandatory planning for one courier, we recommend using grouping locations.

To control the sequence when carrying out pickup & delivery pairs, we recommend using location.in_lifo_order (for more information, see Order of reception and delivery).

Schemes

• pickup & delivery.

  
• Multiple pickup for one delivery point.

  

Example 1

We plan 12 different points. Half the points are pickup , and the other half are delivery . In total, we get 6 “door-to-door” deliveries.

API request (JSON) ⋅ API response ⋅ View on map

Example 2

We set 8 different addresses for pickup points and specify the same values for them in the delivery_to field. As a result, we get a plan in which a courier must pick up orders from different places and bring them all to one place.

API request (JSON) ⋅ API response ⋅ View on map

Example 3

Delivery is planned from 1 address to 6 different addresses. We set the pickup type for 16 points with the same address and the delivery type for the other 16 points.

API request (JSON) ⋅ API response ⋅ View on map

When to use this

This scenario is used when multiple depots have a ufficient quantity of the same goods, and you need to choose which depot to deliver them from when planning.

How to implement this scenario

When planning, specify multiple depots and which depot each courier is assigned to (which depots they can start from and visit). For more information, see Delivery from one of several depots and Start from one of several depots.

For this scenario, you can also use the function for starting at an arbitrary point without visiting the depot (learn more in Start or end of the route at an arbitrary point when start_at is defined and visit_depot_at_start = false). If you specify the depot as the starting point where shipping must begin for each courier, the planning result will be the same as when planning from several depots.

This scenario can be implemented without the service functions, that is, if you create rigid links between an order and a depot. There are several ways to do this:

• Nearest depot

  Calculate the distance between the coordinates of the order address and all depots (use the Distance matrix service for making the calculation). The nearest depot is selected for the order.

• Delivery zone

  Each depot has a pre-defined delivery zone (drawn using Yandex Map Constructor). Each order is pre-checked to see which depot's delivery zone it falls into. Each depot is planned separately and independently following the Delivery from a single depot scenario.

Diagram

Example 1

In this example, the depot_id parameter is used to specify which depot each courier is linked to. This way, couriers deliver orders from their depots only.

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Example 2

In this example, couriers don't start their route from the depot. They start from one of two garage points. The start point of the route is defined using start_at.

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Example 3

Three depots, three orders, and three vehicles are given. Each vehicle starts from its own depot. Orders are assigned to depots using location.depot_id:

• Order 1 is not assigned to a depot. It can be delivered from any depot.

• Order 2 is assigned to depots 1 and 2. This order can be delivered from one of the two specified depots.

• Order 3 is assigned to depot 3. It is located in a specific depot.

As a result, the algorithm built two routes:

1. One courier serves orders 1 and 2 from depot 2.
2. Another courier serves order 3 from depot 3.

API request (JSON) ⋅ API response ⋅ View on map

When to use this

Use this scenario in cases where cargo can be delivered to one of several possible points and you need to choose the optimal one:

• Collecting medical tests from clients' homes to take them to the nearest lab.

• Collecting garbage or scrap metal to take it to the nearest disposal site.

• Picking up a parcel from a client to send it via a third-party transport company, which is why you need to take it to the nearest receiving terminal.

• Courier delivery. You need to plan a meeting between a regular courier and a courier with a fixed route that collects urgent shipments from regular couriers.

How to implement this scenario

Implement the delivery to one of multiple possible points scenario using the delivery_to_any parameter.

You can see where the order was taken in the API response: for depots or drop_off orders in the delivered_orders field.

Diagram

Example

In this example, a set of pickup orders is implemented. For each order, several identical points are specified indelivery_to_any. After collecting orders, the courier will take them to the nearest drop_off point at the end of their shift. For the first courier, it will be “Lab 4”, for the second, “Lab 2”. In the API response, orders delivered to these points are listed in the delivered_orders fields.

API request (JSON) ⋅ API response ⋅ View on map

When to use this

Use this scenario in situations where certain cargo can be received at several points and you need to choose the optimal one among them. For example:

• The goods to be delivered are located at different points (for example, in one of the grocery stores or pharmacies).

• The cargo can be received at one of the terminals of a third-party transport company.

• It's necessary to plan a meeting between a regular courier and a courier with a fixed route that takes urgent shipments to regular couriers.

How to implement this scenario

Learn more in the description of the pickup_from_any parameter.

Diagram

Example

In this example, there are 2 delivery orders with pickup_from_any set to true. 2 pickup orders are associated with each of these orders.

Please note that all pickup orders and the linked delivery order must be the same weight.

API request (JSON) ⋅ API response ⋅ View on map

When to use this

• The shipment volume exceeds available load capacity

  In this case, all or some of the vehicles must make more than one run to ship all the orders.

• It's impossible to collect all orders by the time of shipment

  Sometimes depot capacity doesn't allow you to collect all orders by the time shipment starts, but delivery must start immediately to ensure that order windows are met. In this case, couriers will make their first run not fully loaded, so that they can deliver the orders with earlier delivery windows on time. Meanwhile, the depot will collect orders with later delivery windows, which will be delivered during future runs.

How to implement this scenario

• To enable multiple runs, use the max_runs parameter (for more information, see Multiple vehicle runs per day).

• To take into account that not all orders will be collected by the time shipment starts, use the depot_ready_time parameter (for more information, see Time when the order is ready at the depot).

Diagram

Example 1

In this example, the total weight of the three orders (3000 kg) exceeds the total load capacity of two vehicles (2500 kg). Courier 2 will make two runs (max_runs), delivering the third order during the second run.

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Example 2

In this example, the courier will first deliver the earlier orders (time_window), depot_ready_time determines if they're ready or not. The max_runs parameter will allow them to return to the depot to collect the later orders.

API request (JSON) ⋅ API response ⋅ View on map

When to use this

Use this scenario when some orders have already been assigned to vehicles and you want to maintain this distribution.

The following additional planning situations are possible:

1. Some of the orders are already assigned to routes, and there are unassigned orders, but the vehicles haven't departed from the depot yet .

2. Additional orders are received (such as pickup orders), but the vehicles have already departed from the depot.

In both cases, the previously planned orders have to be maintained while also assigning new (“unallocated”) orders to vehicles with free timeslots or cargo space.

Generally speaking, additional planning is needed when you can't solve the routing task completely in one iteration, so you run it stepwise, passing the previous iteration results to the next planning.

How to implement this scenario

In each case, it's important to know whether to keep the current sequence of assigned orders. To keep the order sequence, use the vehicles.visited_locations field. Otherwise, use the vehicles.planned_route field. In the lists, you can specify orders and depots for reloading that the courier needs to visit when traveling along the route.

Case 1 (vehicles haven't departed yet)

To generate a request, you need to know:

• Already allocated orders for each vehicle.

• New orders received since the last planning.

In the new request for additional planning for each vehicle, use the planned_route field (visited_locations) to specify the IDs (locations or id) assigned orders (from the previous planning iteration).

Case 2 (vehicles already departed)

To generate a request, you need to know:

• Already allocated orders for each vehicle.

• Location of each vehicle at the time of additional planning (this may also be the last delivered or the next order in the sequence).

• List of already completed orders.

• New orders received since the last planning.

In the new request for additional planning:

1. For each vehicle, use the planned_route field (visited_locations) to specify the IDs (locations or id) of assigned orders (from the previous planning iteration) that were not completed before the additional planning.

2. Add “dummy” orders to the list: these orders should correspond to the current location points for each vehicle. You can skip this step if the current location is the last order delivered or the next order in the sequence.

3. For each vehicle, specify the ID of its current location and the time of departure from it in the first element in the visited_locations field.

   To specify the time of departure from a location, use the planned_route.start_time parameter (or visited_locations.N.time).

   Possible time formats:

   • relative: [D.]HH[:MM[:SS]], where D is the date shift relative to the start date of the route.
   • absolute: In ISO 8601 format (for example, 2018-09-06T10:15:00+03:00).

   Learn more about time formats

   Example

   Here, departure from route location 1001 will be scheduled for 10:20.

   "visited_locations": [
       {
         "id": "1001",
         "shift_id": "1",
         "time": "10:20"
       },
       ...
   ]

   Note.

   For previously unused vehicles, set the correct departure parameters: either move the start time of the shift or specify the current depot location in visited_locations.

4. From the list of orders, delete the already completed ones (taking note of item 2: if the last delivered order is used as the current location, don't delete it).

Notes

The initial allocation subject to additional planning must be done so that the vehicles have free timeslots/cargo space (otherwise, the additional orders won't fit in). To do this, decrease the vehicle shift time using the balanced groups option (balanced_groups) or limited capacity (specify a lower capacity for the vehicles).

Use the tag functionality if you want to prevent the selected vehicles from additional orders. For more information, see Vehicle tags.

Basic task

The task includes 70 orders and 9 available couriers, the balanced groups option is used balanced_groups.

Get 5 couriers in the solution.

Planning result:

API request (JSON) ⋅ API response ⋅ View on map

Now let's look at the methods of additional planning for the basic task.

Example 1

Additional planning at the depot, changing the order sequence in the routes.

In the request, enter 110 orders (40 of them are new with regards to the basic task) and 9 available couriers. For 5 couriers, specify the allocation from the basic task in  planned_route. Also, use the balanced group option (balanced_groups).

Get 7 couriers in the solution.

Planning result:

API request (JSON) ⋅ API response ⋅ View on map

Example 2

Additional planning at the depot, without changing the order sequence in the routes.

In the request, enter 110 orders (40 of them are new with regards to the basic task) and 9 available couriers. For 5 couriers, specify the allocation from the basic task in  visited_locations. Also, use the balanced group option (balanced_groups).

Get 7 couriers in the solution.

Planning result:

API request (JSON) ⋅ API response ⋅ View on map

Example 3

Additional planning when couriers have already departed from the depot (without changing the sequence of orders in the routes).

At the time of planning, assume that 15 orders were delivered (3 orders for each of the 5 couriers from the basic task), and delete them from the additional planning task.

In total, enter 100 orders in the request (40 new orders, 55 non-delivered orders from the basic task, and 5 courier start points), and 9 available couriers. For 5 couriers, specify the allocation from the basic task in  visited_locations. Also, use the balanced group option (balanced_groups).

Get 8 couriers in the solution (3 of them are new couriers who start from the depot).

Planning result:

*: without the completed orders and start point.

API request (JSON) ⋅ API response ⋅ View on map

A cross-dock is a depot for the transshipment, reconsolidation, or reloading of goods. Specify the crossdock value in the type field for such depots.

Cross-docking can be used for some orders to ensure the speed of delivery and reduce transport costs: orders are taken to a cross-dock closer to distant locations in advance. For example, a depot is located north of the city, and delivery is carried out in the daytime. In this case, to deliver orders to the southern part of the city, a cross-dock is created in the south, where some orders are delivered at night by large vehicles.

Orders aren't stored at cross-docks. Instead, one or more vehicles bring the orders, and then one or more vehicles pick them up and deliver them to clients. At the moment, different vehicles must transport orders to and from the cross-dock.

For vehicles, the restriction on working with cross-docks is set using the crossdock_mode parameter. It can take the following values:

• allow: The vehicle can take orders both to clients and to a cross-dock (default value).
• deny: The vehicle can only take orders to clients.
• mandatory: The vehicle can only take orders to a cross-dock.

To specify which depot or cross-dock the vehicle starts from, use the vehicle.depot_id field.

For orders, the crossdock_mode parameter is also set with the following values:

• allow: The order can be delivered both directly from the depot and via a cross-dock (default value).
• deny: The order can only be delivered directly from the depot.
• mandatory: The order can only be delivered via a cross-dock.

To specify which depot the order is stored at or via which cross-dock it must be delivered, use the location.depot_id field. If multiple depots and cross-docks are specified, the order can be picked up from any of them.

For pickup orders, you can set the may_drop_in_crossdock parameter with the following values:

• true: The order can be left in the cross-dock (default value).
• false: The order must be delivered to a regular depot.

The may_drop_in_crossdock parameter can only be used in the Delivery to one of multiple locations scenario. If there are pickup&delivery pairs with the possibility of transloading at a cross-dock, use the Pickup & delivery via a cross-dock scenario.

Handling time

For orders delivered via a cross-dock, location.crossdock_service_duration_s is taken into account. When a vehicle arrives from a regular depot to a cross-dock, the handling time for it is calculated using the formula:

depot.service_duration_s + Total{ location.crossdock_service_duration_s} for all orders that the vehicle brought.

When using cross-docking, it's useful to have a time buffer depot.crossdock_safety_waiting_duration_s: the minimum amount of time that must pass between unloading an order at the cross-dock and when it can be picked up for delivery to the client (by default, 3600 seconds, or 1 hour). This buffer is necessary so that the potential delay of the first vehicle (carrying orders to the cross-dock) won't affect the routes of vehicles that pick up orders from the cross-dock.

Diagram

Example 1

4 couriers and 6 orders that need to be delivered from 2 depots and partially via a cross-dock. The depots are located in the west of Moscow, and the cross-dock is in the east. Orders 1, 2 and 3 are located in the west and are delivered from depots. Orders 4, 5, and 6 are in the east and are delivered via a cross-dock.

Planning result:

API request (JSON) ⋅ API response ⋅ View on map

Example 2

The planning task has 2 couriers, 1 depot in the south of Moscow, and a cross-dock in the east. The following conditions are set for orders:

Planning result:

API request (JSON) ⋅ API response ⋅ View on map

Example 3

The same depots and couriers as in example 2. The following conditions are set for orders:

Planning result:

API request (JSON) ⋅ API response ⋅ View on map

When to use this

The scenario is used when there are several closely located orders along the route. The courier can arrive by vehicle, park, and deliver orders on foot. If it's not possible to carry all orders in one run, the courier makes several runs. At the start of each run, the courier takes some of the orders, delivers them, and then gets back to the vehicle and takes the next batch of the orders.

The route may include a few walking parts.

How to implement this scenario

Set the parameters:

• location.parking_service_duration_s: The amount of time that the courier spends parking the vehicle.

  Note.

  For the walking and transit routing modes, the location.parking_service_duration_s parameter isn't included in the calculations of the time spent.

  For other modes, the time specified in the location.parking_service_duration_s parameter will be taken into account only once: before the start of the walking part of the route.

• vehicle.walking_courier.capacity.units: The maximum number of units that the courier can carry. To use this feature, specify the number of units for the order using the location.shipment_size parameter.

  If the courier needs to carry more units than given for vehicle.walking_courier.capacity.units, the algorithm can build the route so that the courier makes multiple runs. For example, if the courier needs to visit 10 locations during the walking delivery, and vehicle.walking_courier.capacity.units = 5, the courier delivers 5 units first, goes back to the vehicle, and then picks up the remaining 5 units.

  Note.

  RouteQ supports scenarios where only one courier leaves the vehicle and delivers orders on foot.

• vehicle.walking_courier.cost.hour and/or vehicle.walking_courier.cost.km: The cost of walking delivery per hour and per kilometer. The cost of walking delivery is calculated the same way as the vehicle or courier cost.
• vehicle.walking_courier.restrictions: Restrictions for walking parts of routes:

  • max_walking_edge_distance_m.value and max_walking_edge_distance_m.penalty: The maximum distance (in meters) between two points on walking parts of the route and the penalty for violating the restriction.
  • max_parking_walking_distance_m.value and max_parking_walking_distance_m.penalty: The maximum walking distance (in meters) in a parking session and the penalty for violating the restriction.
  • max_parking_duration_s.value and max_parking_duration_s.penalty: The maximum duration (in seconds) of a parking session and the penalty for violating the restriction.
  • close_locations: Array of restrictions for nearby order locations:

    • duration_till_service_s.value and duration_till_service_s.penalty: The maximum time (in seconds) between handling two nearby orders and the penalty for violating the restriction.
    • distance_till_service_m.value and distance_till_service_m.penalty: The maximum distance (in meters) walked between two nearby orders and the penalty for violating the restriction.
    • search_radius_m: The distance between two orders, which are considered nearby and subject to restrictions.
    • soft_search_radius_m: If the value of this parameter exceeds the value of the corresponding search_radius_m , orders at a distance from search_radius_m to soft_search_radius_m are also subject to restrictions, but their size decreases linearly depending on the distance. For example, at the distance (search_radius_m + soft_search_radius_m) / 2, a restriction of a weight of 0.5 applies.

    Example of using the settings of nearby orders

    {
    ...
        "walking_courier": {
            ...
            "restrictions": {
                "close_locations": [
                    {
                        "search_radius_m": 400,
                        "soft_search_radius_m": 500,
                        "distance_till_service_m": {
                            "penalty": 0.02,
                            "value": 1500
                        }
                    },
                    {
                        "search_radius_m": 100,
                        "soft_search_radius_m": 160,
                        "duration_till_service_s": {
                            "penalty": 0.02,
                            "value": 1500
                        }
                    }   
                ],
                ...
            }
            ...
        },
    ...
    }

    In the example above, the first restriction is that orders up to 400 meters away from each other must be handled within the next 1500 meters of movement. The penalty for violating the restriction is 0.02 for every meter. For orders 400 to 500 meters away from each other, this restriction also applies, but it decreases linearly depending on the distance. For example, for an order 430 meters away, a penalty equal to 0.7 of the main penalty is applied.

    The second restriction is similar to the first one, but it sets the time between handling orders. Orders within a radius of 100 meters must be handled within 25 minutes with a 0.02 penalty for each second over. Orders 100 to 160 meters away are subject to the restriction, which decreases linearly as the distance grows.

To balance the cost of visiting orders on foot and vehicle, use options.average_projection_walking_part_distance_m. This option sets the average walking distance from the road to the order. When the option is used, for each visit to an order by vehicle, the courier is subject to twice the penalty cost for walking.

For example, if average_projection_walking_part_distance_m = 15, the cost of 30 meters of the walking route is added to the cost of each visit. You can use this setting to compensate for the cost of walking part of the way in driving mode compared to the same distance in walking mode.

Diagram

Example 1

The route includes 10 orders. Some orders are near each other:

• Orders 1, 2, 3, and 4.
• Orders 5, 7, and 9.
• Orders 8 and 10.

Because the cost of delivery on foot is less than the cost of driving, the courier parks the vehicle and delivers nearby orders on foot. To deliver the remaining orders, the courier travels by vehicle.

API request (JSON) ⋅ API response ⋅ View on map

Example 2

The same as example 1, but vehicle.walking_courier.capacity.units = 2. To deliver orders located close to each other, the courier parks the vehicle and delivers them in no more than two units per run.

API request (JSON) ⋅ API response ⋅ View on map

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