{"item":[{"name":"Plan","description":"规划——根据订单、表演者和交通创建旅行。\n\n计划请求可以以同步和异步模式执行。\n","item":[{"id":"19b0ce22-6329-42fa-8405-b3a80bbaec6f","name":"Planning (ASYNC)","request":{"name":"Planning (ASYNC)","description":{"content":"Starting trip planning - after loading and checking the data, the `process_code` is returned.\n\nUsing the `process_code`, you can find out [calculation state](#operation/read_plan_state) and [get result](#operation/read_plan_result),\nand also [cancel calculation](#operation/cancel_plan_calculation) and [delete temporary data](#operation/delete_plan_result) (otherwise they will be automatically deleted according to the ttl specified in the calculation settings).\n","type":"text/plain"},"url":{"path":["universal","plan","calculation-async"],"host":["{{baseUrl}}"],"query":[],"variable":[]},"header":[{"key":"Content-Type","value":"application/json"},{"key":"Accept","value":"application/json"}],"method":"POST","body":{"mode":"raw","raw":"{\n \"locations\": [\n {\n \"key\": \"location_client_1\",\n \"geopoint\": {\n \"latitude\": 35.7048259,\n \"longitude\": 139.7297071\n }\n },\n {\n \"key\": \"location_client_2\",\n \"geopoint\": {\n \"latitude\": 35.71861,\n \"longitude\": 139.7190533\n }\n },\n {\n \"key\": \"location_client_3\",\n \"geopoint\": {\n \"latitude\": 35.7099049,\n \"longitude\": 139.7265958\n }\n },\n {\n \"key\": \"location_storage\",\n \"geopoint\": {\n \"latitude\": 35.7042346,\n \"longitude\": 139.6531996\n }\n }\n ],\n \"orders\": [\n {\n \"key\": \"order_1\",\n \"demands\": [\n {\n \"key\": \"demand_1_drop\",\n \"demand_type\": \"DROP\",\n \"target_cargos\": [\n \"order_1_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_1_drop_event\",\n \"location_key\": \"location_client_1\",\n \"duration\": \"PT10M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n },\n {\n \"key\": \"demand_1_pickup\",\n \"demand_type\": \"PICKUP\",\n \"target_cargos\": [\n \"order_1_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_1_pickup_event\",\n \"location_key\": \"location_storage\",\n \"duration\": \"P0D\",\n \"reward\": 0,\n \"hard_time_window\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n }\n }\n ]\n }\n ],\n \"cargos\": [\n {\n \"key\": \"order_1_cargos\",\n \"capacity\": {\n \"mass\": 1\n }\n }\n ]\n },\n {\n \"key\": \"order_2\",\n \"demands\": [\n {\n \"key\": \"demand_2_drop\",\n \"demand_type\": \"DROP\",\n \"target_cargos\": [\n \"order_2_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_2_drop_event\",\n \"location_key\": \"location_client_2\",\n \"duration\": \"PT10M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n },\n {\n \"key\": \"demand_2_pickup\",\n \"demand_type\": \"PICKUP\",\n \"target_cargos\": [\n \"order_2_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_2_pickup_event\",\n \"location_key\": \"location_storage\",\n \"duration\": \"P0D\",\n \"reward\": 0,\n \"hard_time_window\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n }\n }\n ]\n }\n ],\n \"cargos\": [\n {\n \"key\": \"order_2_cargos\",\n \"capacity\": {\n \"mass\": 1\n }\n }\n ]\n },\n {\n \"key\": \"order_3\",\n \"demands\": [\n {\n \"key\": \"demand_3_work\",\n \"demand_type\": \"WORK\",\n \"possible_events\": [\n {\n \"key\": \"demand_3_work_event\",\n \"location_key\": \"location_client_3\",\n \"duration\": \"PT15M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n }\n ]\n }\n ],\n \"performers\": [\n {\n \"key\": \"performer_1\",\n \"shifts\": [\n {\n \"key\": \"performer_1_shift\",\n \"availability_time\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n },\n \"working_time\": {\n \"from\": 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1\n }\n }\n ]\n },\n {\n \"key\": \"order_2\",\n \"demands\": [\n {\n \"key\": \"demand_2_drop\",\n \"demand_type\": \"DROP\",\n \"target_cargos\": [\n \"order_2_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_2_drop_event\",\n \"location_key\": \"location_client_2\",\n \"duration\": \"PT10M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n },\n {\n \"key\": \"demand_2_pickup\",\n \"demand_type\": \"PICKUP\",\n \"target_cargos\": [\n \"order_2_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_2_pickup_event\",\n \"location_key\": \"location_storage\",\n \"duration\": \"P0D\",\n \"reward\": 0,\n \"hard_time_window\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n }\n }\n ]\n }\n ],\n \"cargos\": [\n {\n \"key\": \"order_2_cargos\",\n \"capacity\": {\n \"mass\": 1\n }\n }\n ]\n },\n {\n \"key\": \"order_3\",\n \"demands\": [\n {\n \"key\": \"demand_3_work\",\n \"demand_type\": \"WORK\",\n \"possible_events\": [\n {\n \"key\": \"demand_3_work_event\",\n \"location_key\": \"location_client_3\",\n \"duration\": \"PT15M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n }\n ]\n }\n ],\n \"performers\": [\n {\n \"key\": \"performer_1\",\n \"shifts\": [\n {\n \"key\": \"performer_1_shift\",\n \"availability_time\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n },\n \"working_time\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n },\n \"tariff\": {\n \"cost_per_shift\": 1000,\n \"constraints\": [\n {\n \"stage_length\": \"PT10H\",\n \"cost_per_unit\": 1\n }\n ]\n }\n }\n ]\n }\n ],\n \"transports\": [\n {\n \"key\": \"transport_1\",\n \"shifts\": [\n {\n \"key\": \"transport_1_shift\",\n \"availability_time\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n },\n \"tariff\": {\n \"cost_per_shift\": 1000,\n \"constraints\": [\n {\n \"stage_length\": 1000000,\n \"cost_per_unit\": 0.01\n }\n ]\n }\n }\n ],\n \"transport_type\": \"CAR\",\n \"boxes\": [\n {\n \"key\": \"box_1\",\n \"capacity\": {\n \"mass\": 300\n }\n }\n ]\n }\n ],\n \"plan_settings\": {\n \"trips_settings\": {\n \"configuration\": \"optimize_distance\"\n },\n \"geo_settings\": {\n \"toll_roads\": true,\n \"traffic_jams\": false\n }\n }\n}","options":{"raw":{"headerFamily":"json","language":"json"}}}},"status":"OK","code":200,"header":[{"key":"Content-Type","value":"application/json"}],"body":"{\n \"tracedata\": {\n \"process_code\": \"11111111-2222-3333-4444-555555555555\",\n \"request_code\": \"aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee\",\n \"username\": \"username_for_login\",\n \"company\": \"smart_company\",\n \"service\": \"UNIVERSAL\",\n \"operation\": \"run_plan_calculation\",\n \"env\": \"edge7\",\n \"pod\": \"11111111-2222-3333-4444-555555555555\",\n \"time\": \"2025-03-21T09:30:00+03:00\"\n },\n \"process_code\": \"11111111-2222-3333-4444-555555555555\"\n}","cookie":[],"_postman_previewlanguage":"json"},{"id":"e654a408-0513-4a56-a26f-172a5eea4a1e","name":"PlanTaskMoscow","originalRequest":{"url":{"path":["universal","plan","calculation-async"],"host":["{{baseUrl}}"],"query":[],"variable":[]},"header":[{"key":"Content-Type","value":"application/json"},{"key":"Accept","value":"application/json"},{"description":{"content":"Added as a part of security scheme: bearer","type":"text/plain"},"key":"Authorization","value":"Bearer "}],"method":"POST","body":{"mode":"raw","raw":"{\n \"locations\": [\n {\n \"key\": \"location_client_1\",\n \"geopoint\": {\n \"latitude\": 55.713699,\n \"longitude\": 37.621839\n }\n },\n {\n \"key\": \"location_client_2\",\n \"geopoint\": {\n \"latitude\": 55.630721,\n \"longitude\": 37.62493\n }\n },\n {\n \"key\": \"location_client_3\",\n \"geopoint\": {\n \"latitude\": 55.722801,\n \"longitude\": 37.674984\n }\n },\n {\n \"key\": \"location_storage\",\n \"geopoint\": {\n \"latitude\": 55.685853,\n \"longitude\": 37.43181\n }\n }\n ],\n \"orders\": [\n {\n \"key\": \"order_1\",\n \"demands\": [\n {\n \"key\": \"demand_1_drop\",\n \"demand_type\": \"DROP\",\n \"target_cargos\": [\n \"order_1_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_1_drop_event\",\n \"location_key\": \"location_client_1\",\n \"duration\": \"PT10M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n },\n {\n \"key\": \"demand_1_pickup\",\n \"demand_type\": \"PICKUP\",\n \"target_cargos\": [\n \"order_1_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_1_pickup_event\",\n \"location_key\": \"location_storage\",\n \"duration\": \"P0D\",\n \"reward\": 0,\n \"hard_time_window\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n }\n }\n ]\n }\n ],\n \"cargos\": [\n {\n \"key\": \"order_1_cargos\",\n \"capacity\": {\n \"mass\": 1\n }\n }\n ]\n },\n {\n \"key\": \"order_2\",\n \"demands\": [\n {\n \"key\": \"demand_2_drop\",\n \"demand_type\": \"DROP\",\n \"target_cargos\": [\n \"order_2_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_2_drop_event\",\n \"location_key\": \"location_client_2\",\n \"duration\": \"PT10M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n },\n {\n \"key\": \"demand_2_pickup\",\n \"demand_type\": \"PICKUP\",\n \"target_cargos\": [\n \"order_2_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_2_pickup_event\",\n \"location_key\": \"location_storage\",\n \"duration\": \"P0D\",\n \"reward\": 0,\n \"hard_time_window\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n }\n }\n ]\n }\n ],\n \"cargos\": [\n {\n \"key\": \"order_2_cargos\",\n \"capacity\": {\n \"mass\": 1\n }\n }\n ]\n },\n {\n \"key\": \"order_3\",\n \"demands\": [\n {\n \"key\": \"demand_3_work\",\n \"demand_type\": \"WORK\",\n \"possible_events\": [\n {\n \"key\": \"demand_3_work_event\",\n \"location_key\": \"location_client_3\",\n \"duration\": \"PT15M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n }\n ]\n }\n ],\n \"performers\": [\n {\n \"key\": \"performer_1\",\n \"shifts\": [\n {\n \"key\": \"performer_1_shift\",\n \"availability_time\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n },\n \"working_time\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n },\n \"tariff\": {\n \"cost_per_shift\": 1000,\n \"constraints\": [\n {\n \"stage_length\": \"PT10H\",\n \"cost_per_unit\": 1\n }\n ]\n }\n }\n ]\n }\n ],\n \"transports\": [\n {\n \"key\": \"transport_1\",\n \"shifts\": [\n {\n \"key\": \"transport_1_shift\",\n \"availability_time\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n },\n \"tariff\": {\n \"cost_per_shift\": 1000,\n \"constraints\": [\n {\n \"stage_length\": 1000000,\n \"cost_per_unit\": 0.01\n }\n ]\n }\n }\n ],\n \"transport_type\": \"CAR\",\n \"boxes\": [\n {\n \"key\": \"box_1\",\n \"capacity\": {\n \"mass\": 300\n }\n }\n ]\n }\n ],\n \"plan_settings\": {\n \"trips_settings\": {\n \"configuration\": \"optimize_distance\"\n },\n \"geo_settings\": {\n \"toll_roads\": true,\n \"traffic_jams\": false\n }\n }\n}","options":{"raw":{"headerFamily":"json","language":"json"}}}},"status":"OK","code":200,"header":[{"key":"Content-Type","value":"application/json"}],"body":"{\n \"tracedata\": {\n \"process_code\": \"11111111-2222-3333-4444-555555555555\",\n \"request_code\": \"aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee\",\n \"username\": \"username_for_login\",\n \"company\": \"smart_company\",\n \"service\": \"UNIVERSAL\",\n \"operation\": 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},\n {\n \"key\": \"location_client_3\",\n \"geopoint\": {\n \"latitude\": 39.745829,\n \"longitude\": -105.0013897\n }\n },\n {\n \"key\": \"location_storage\",\n \"geopoint\": {\n \"latitude\": 39.82519,\n \"longitude\": -105.1185757\n }\n }\n ],\n \"orders\": [\n {\n \"key\": \"order_1\",\n \"demands\": [\n {\n \"key\": \"demand_1_drop\",\n \"demand_type\": \"DROP\",\n \"target_cargos\": [\n \"order_1_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_1_drop_event\",\n \"location_key\": \"location_client_1\",\n \"duration\": \"PT10M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n },\n {\n \"key\": \"demand_1_pickup\",\n \"demand_type\": \"PICKUP\",\n \"target_cargos\": [\n \"order_1_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_1_pickup_event\",\n \"location_key\": \"location_storage\",\n \"duration\": \"P0D\",\n \"reward\": 0,\n \"hard_time_window\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n }\n }\n ]\n }\n ],\n \"cargos\": [\n {\n \"key\": \"order_1_cargos\",\n \"capacity\": {\n \"mass\": 1\n }\n }\n ]\n },\n {\n \"key\": \"order_2\",\n \"demands\": [\n {\n \"key\": \"demand_2_drop\",\n \"demand_type\": \"DROP\",\n \"target_cargos\": [\n \"order_2_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_2_drop_event\",\n \"location_key\": \"location_client_2\",\n \"duration\": \"PT10M\",\n \"hard_time_window\": {\n \"from\": \"2025-03-08T05:00:00+00:00\",\n \"to\": \"2025-03-08T07:00:00+00:00\"\n }\n }\n ]\n },\n {\n \"key\": \"demand_2_pickup\",\n \"demand_type\": \"PICKUP\",\n \"target_cargos\": [\n \"order_2_cargos\"\n ],\n \"possible_events\": [\n {\n \"key\": \"demand_2_pickup_event\",\n \"location_key\": \"location_storage\",\n \"duration\": \"P0D\",\n \"reward\": 0,\n \"hard_time_window\": {\n \"from\": \"2025-03-07T21:00:00+00:00\",\n \"to\": \"2025-03-08T20:59:59+00:00\"\n }\n }\n ]\n 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Orders that cannot be completed remain assigned to the contractor and end up in the `waitlist`.\n\nData clearing:\n\n* Entities that are not referenced by input trips - locations, performers, transport, destinations, orders, facts - are removed from the dataset.\n* If several facts refer to one entity, only the latest one in the time field is taken into account; if the time coincides, a random one is taken into account.\n\nAccounting for existing trips:\n\n* Trips are transformed into `hardlink` destinations - i.e. Changes of performer and transport assigned to a flight cannot be used on other trips.\n* Replacement events are removed from orders and the execution order is fixed using `precedence_in_trip`; if orders already had an execution order specified, it will be overwritten.\n\nCurrent time tracking:\n\n* If the current time `actualize_settings.current_time` is not specified in the data, then the time the server received the request is used.\n* Only those facts that occurred before `actualize_settings.current_time` are taken into account.\n* If the current time is greater than the left border of the time windows of all events and shifts, then the left border is shifted to the current time.\n* The allowed delay time `actualize_settings.max_delay_duration` is added to the right border of the time windows of all events and shifts.\n* Soft time windows are adjusted only if the duration of the order does not allow the order to be completed within the soft window.\n\nAccounting for the fact of changing the location `NEW_LOCATION`:\n\n* To take this fact into account, the list of locations `locations` must contain a new location that describes the current coordinate of the performer.\n* If the flight began (the actual time is later than the planned start time of the flight) - the transport is considered to be in the same location as the performer - i.e. `start_location_key` of the executor and transport is changed.\n* If the flight has not started (the actual time is before the planned start time of the flight) - only the initial location of the performer changes, the location of the transport remains from the original data.\n* If there are several facts, only the latest one in the `time` field is taken into account.\n\nAccounting for the fact of order completion `ORDER_DONE`:\n\n* The order, its applications and associated locations are removed from the data for updating.\n* The cargo is considered unloaded from the transport.\n\nAccounting for facts about partial fulfillment of orders `DEMAND_START` and `DEMAND_DONE`:\n\n* If all orders have been completed for an order, it is considered completed (actions are similar to processing the `ORDER_DONE` fact).\n* If an order has an order with the `PICKUP` type, then the corresponding order with the `DROP` type will be converted into an order with the `DROP_FROM_BOX` type.\n* For each flight, only one open fact `DEMAND_START` is allowed (closed by the fact `DEMAND_DONE`) - since the contractor 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processing](#operation/run_replan_refine).\n","type":"text/plain"},"url":{"path":["universal","replan","calculation-async"],"host":["{{baseUrl}}"],"query":[],"variable":[]},"header":[{"key":"Content-Type","value":"application/json"},{"key":"Accept","value":"application/json"}],"method":"POST","body":{"mode":"raw","raw":"{\n \"locations\": [\n {\n \"key\": \"location_client_1\",\n \"geopoint\": {\n \"latitude\": 55.713699,\n \"longitude\": 37.621839\n }\n },\n {\n \"key\": \"location_client_2\",\n \"geopoint\": {\n \"latitude\": 55.630721,\n \"longitude\": 37.62493\n }\n },\n {\n \"key\": \"location_client_3\",\n \"geopoint\": {\n \"latitude\": 55.722801,\n \"longitude\": 37.674984\n }\n },\n {\n \"key\": \"location_storage\",\n \"geopoint\": {\n \"latitude\": 55.685853,\n \"longitude\": 37.43181\n }\n }\n ],\n \"orders\": [\n {\n \"key\": \"order_1\",\n \"demands\": [\n {\n \"key\": \"demand_1_drop\",\n \"demand_type\": \"DROP\",\n \"target_cargos\": [\n \"order_1_cargos\"\n ],\n 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described object:\n\n\n## Performers\n\nThe name of the sheet is `performers`.\n\nList of available performers.\n\nThe described object:\n\n\n## Transports\n\nThe name of the sheet `transports`.\n\nList of available transport.\n\nThe described object:\n\n\n## Boxes\n\nThe name of the sheet is `transports.boxes`.\n\nList of transport boxes that can accommodate the load.\nThe table is mandatory if there are orders with applications of types of `PICKUP` and `DROP`.\nIn the description of the boxes, those fields that describe cargo are required.\n\nThe described object:\n\n\n## Orders\n\nThe name of the sheet is `orders`.\n\nList of orders.\n\nThe described object:\n\n\n## Cargos\n\nThe name of the sheet is `orders.cargos`.\n\nList of goods. It may contain one load for `DROP`, a list for `PICKUP`, empty for `WORK`.\nThe table is not obligatory if all applications have the type `WORK`.\n\nThe described object:\n\n\n## Hardlinks\n\nThe name of the sheet is `hardlinks`.\n\nList of hardlinks.\n\nThe described object:\n\n\n## Trips\n\nThe name of the sheet `trips`.\n\nList of trips.\n\nThe described object:\n\n\n## Facts\n\nThe name of the sheet is `facts`.\n\nList of facts.\n\nThe described object:\n\n\n## Plan settings\n\nThe name of the sheet `plan_settings`.\n\nThe described object:\n\n\n## Actualize settings\n\nThe name of the sheet is `actualize_settings`.\n\nThe described object:\n\n\n## Replan settings\n\nThe name of the sheet is `replan_settings`.\n\nThe described object:\n\n\n## Total Statistics\n\nThe name of the sheet is `total_statistics`.\n\nTotal Statistics.\n\nThe described object:\n\n","item":[]}],"auth":{"type":"bearer","bearer":[{"type":"any","value":"{{bearerToken}}","key":"token"}]},"event":[],"variable":[{"key":"baseUrl","value":"https://api.edge7.veeroute.cloud"}],"info":{"_postman_id":"4afdadfb-3c70-435d-9075-c04561a84469","name":"VRt.Universal [UV]","schema":"https://schema.getpostman.com/json/collection/v2.1.0/collection.json","description":{"content":"# Description\n\nSoftware interface for universal trip planning.\n\n## Features\n\n* Ability to pick up cargo from any location\n* Possibility of unloading in any location\n* Pair orders of several types: `PICKUP` (loading), `DROP` (unloading)\n* Single requests of several types: `DROP_FROM_BOX` (unloading cargo that is already in the body), `PICKUP_TO_BOX` (cargo pickup into the body without subsequent unloading), `WORK` (working at the location without moving the cargo)\n* A complex order can consist of any number of orders of any type\n* Transport and performers are divided into different entities, when planning, the optimal assignment of the performer to the transport occurs\n* The transport has several boxes - each of which can accommodate cargo and has its own characteristics\n* Accounting for the compatibility of cargo with transport in terms of cargo dimensions (length, width, height, additional capacity parameters)\n* Taking into account the compatibility of the cargo-box of transport (the ability to take into account the features of the box: refrigerator, thermal bag, fasteners, etc.)\n* Substitute applications, i.e. the ability to execute one of the substitute applications, the choice of which is based on its geographic location and time window\n\n## Restrictions support\n\n**Performer** restrictions:\n\n* Start/finish location\n* Accounting for the performer's way to the transport location\n* Performer's availability schedule is a list of time windows when the performer can move and work on locations\n* The maximum duration of the performer's work during the specified time period\n\n**Transport** restrictions:\n\n* Start/finish location\n* Transport availability schedule is a list of time windows when the transport is available\n* The maximum route distance\n* Several boxes in the transport, each with its own parameters\n* Capacity upper limit (weight, volume, number of orders, number of demands)\n\n**Order** restrictions:\n\n* Strict time windows\n* Ability to specify different valid time windows for a location and time windows to fulfil the desired demand\n* Accounting for the requests fulfillment order within the route\n* A list of desired time windows with different associated costs\n\n## Compatibilities\n\nEntities are compatible if the capabilities list of one entity corresponds to the list of restrictions of another entity (example: fleet parameters corresponds to cargo parameters to be delivered).\n\nSupported compatibilities:\n\n| Name | Restrictions | Features |\n|-------------------------|----------------------------------|------------------------------|\n| Order - Performer | order.performer_restrictions | performer.performer_features |\n| Order - Not a performer | order.performer_blacklist | performer.performer_features |\n| Cargo - Box | order.cargo.box_restrictions | transport.box.box_features |\n| Location - Transport | location.transport_restrictions | transport.transport_features |\n| Transport - Performer | transport.performer_restrictions | performer.performer_features |\n| Performer - Transport | performer.transport_restrictions | transport.transport_features |\n| Order - Order | order.order_restrictions | order.order_features |\n\nBusiness rule examples:\n\n| Name | Business rule example |\n|-------------------------|---------------------------------------------------------------------------------------------|\n| Order - Performer | The driver must have a special license to fulfil the order |\n| Order - Not a performer | The driver is in the blacklist |\n| Cargo - Box | For transportation of frozen products, a box with a special temperature profile is required |\n| Location - Transport | Restrictions on the transport height |\n| Transport - Performer | The truck driver must have the class C driving license |\n| Performer - Transport | The driver is allowed to work on a specific transport |\n| Order - Order | It is not allowed to transport fish and fruits in the same box |\n\n## Cargo placement\n\nList of possibilities of a object rotations (90 degree step):\n\n* `ALL` - can rotate by any axis\n* `YAW` - can yaw\n* `PITCH` - can pitch\n* `ROLL` - can roll\n \n![rotation](../images/universal_cargo_yaw_pitch_roll.svg)\n\n## Trip model\n\nA trip is described by a list of states of the performer, while at the same time the performer can be in several states (for example, being inside the working time window of a location and fulfilling an order at the same location).\n\nThe meanings of the flags responsible for the geographical location:\n\n* `AROUND_LOCATION` - the performer is located near the location - in the process of parking or leaving it.\n* `INSIDE_LOCATION` - the performer is located at the location.\n\nThe values ​​of the flags responsible for being in time windows:\n\n* `INSIDE_WORKING_WINDOW` - the performer is inside the working time window.\n* `INSIDE_LOCATION_WINDOW` - the performer is located inside the location's operating time.\n* `INSIDE_EVENT_HARD_WINDOW` - the performer is inside a hard time window.\n* `INSIDE_EVENT_SOFT_WINDOW` - the performer is inside a soft time window.\n\nThe values ​​of the flags responsible for the actions:\n\n* `ON_DEMAND` - the performer is working on the request.\n* `WAITING` - the performer is in standby mode.\n* `RELOCATING` - the performer moves to the next stop.\n* `BREAK` - the performer is on a break.\n* `REST` - the performer is on a long vacation.\n\n### An example of a route with multiple states at each point in time\n\n| time | set of active flags | location / order / application / event | comment |\n|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------|:-----------------------------------------------------------------------------------------------|\n| 10:00 | INSIDE_LOCATION
AROUND_LOCATION | 2 / - / - / - | starting location |\n| 10:10 | RELOCATING | - / - / - / - | we go to the first order |\n| 10:20 | AROUND_LOCATION | 2 / - / - / - | arrived at the first order |\n| 10:40 | AROUND_LOCATION
INSIDE_LOCATION
WAITING | 2 / - / - / - | parked |\n| 11:00 | AROUND_LOCATION
INSIDE_LOCATION
INSIDE_LOCATION_WINDOW
WAITING
INSIDE_EVENT_HARD_WINDOW | 2 / - / - / - | waited for the start of the location window and at the same time the availability of the order |\n| 11:25 | AROUND_LOCATION
INSIDE_LOCATION
INSIDE_LOCATION_WINDOW
ON_DEMAND
INSIDE_WORKING_WINDOW
INSIDE_EVENT_HARD_WINDOW | 2 / 1 / 2 / 3 | waited for the change of artist |\n| 11:30 | AROUND_LOCATION
INSIDE_LOCATION
INSIDE_LOCATION_WINDOW
ON_DEMAND
INSIDE_WORKING_WINDOW
INSIDE_EVENT_HARD_WINDOW
INSIDE_EVENT_SOFT_WINDOW | 2 / 1 / 2 / 3 | while working - a soft window happened |\n| 11:40 | AROUND_LOCATION
INSIDE_LOCATION
INSIDE_LOCATION_WINDOW
INSIDE_WORKING_WINDOW | 2 / - / - / - | finished working |\n| 11:45 | AROUND_LOCATION
INSIDE_WORKING_WINDOW | 2 / - / - / - | drove out of the parking lot |\n| 11:45 | RELOCATING
INSIDE_WORKING_WINDOW | - / - / - / - | we go to the next order |\n\n## Planning configuration\n\nFor each planning, it is possible to specify a planning configuration that defines the objective function, the desired quality of the routes, and the calculation speed.\n\nThe name of the scheduling configuration is passed in the `trips_settings.configuration` field.\n\nMain configurations:\n\n| Title | Task |\n|---------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **optimize_distance** | Arrange as many orders as possible, then optimize the total mileage (the number of vehicles is selected based on the mileage), used by default |\n| **optimize_transports** | Place as many orders as possible, while using as little transport as possible, ceteris paribus, optimize the work time of performers |\n| **optimize_locality_grouping** | Place as many orders as possible, while striving to optimize the visual grouping of routes, but not their number |\n| **optimize_cars_then_distance** | Arrange as many orders as possible, then optimize the number of vehicles, then the mileage |\n| **optimize_time** | Place as many orders as possible, then optimize the total work time of performers |\n| **optimize_cars_then_time** | Arrange as many orders as possible, then optimize the number of transport, then the total time of the performers |\n| **optimize_money** | Optimize the value of \"profit - costs\", consists of rewards for applications and costs for performers and transports (optimized value is non-negative) |\n\nAdditional configurations:\n\n| Title | Task |\n|-----------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **visual_grouping** | Arrange as many orders as possible while using as little transport as possible and routes should be visually grouped |\n| **optimize_visual_grouping** | Arrange as many orders as possible, then evenly distribute orders taking into account transport accessibility zones (similar to visual_grouping, but visual grouping is calculated differently) |\n| **optimize_cars_then_locality_grouping** | Arrange as many orders as possible, then optimize the number of vehicles, then visually group the routes |\n| **optimize_cars_then_single_location_grouping_sequenced** | Place as many orders as possible, then optimize the number of machines, then reliability |\n\nIn addition to the existing planning options, it is possible to create an objective function directly for the client's business processes ([request configuration](mailto:servicedesk@veeroute.com)).\n\nFor development, it is recommended to use **optimize_cars_then_distance**, since this configuration does not require detailed selection of rates and order values.\n\n## Data validation\n\nInput data validation consists of several steps, which are described below.\n\nValidation of planning results (including the search for possible reasons why orders were not planned) is located in the `analytics` method.\n\n### 1. Schema check\n\nIf the request does not follow the schema, then scheduling is not fully started and such an error is returned along with a 400 code in `schema_errors`.\n\nWe recommend validating the request against the schema (or yaml file) before sending it to the server.\n\n### 2. Check for logical errors that prevent planning from continuing\n\nSchema-correct data passes the second stage of checking for the possibility of starting planning.\n\nAn example of errors at this stage are keys leading to empty entities, or if all orders are incompatible with all performers, i.e. something that makes the planning task pointless.\n\nThese errors are returned along with a 400 code in `logical_errors`.\n\n### 3. Check for logical errors that prevent planning from continuing\n\nAt the third stage, each entity is checked separately.\n\nAll entities that have not passed validation are cut out from the original task and are not sent for planning.\n\nDepending on the setting of `treat_warnings_as_errors`, the results of this type of validation are returned to `warnings` either with a 400 code or with the scheduling result.\n\n### 4. Checks in the planning process\n\nPart of the checks can only be carried out in the planning process.\n\nFor example - that according to the specified tariffs and according to the current traffic forecast, it is physically impossible to reach a certain point.\n\nThe results of these checks are returned in `warnings` or together with the scheduling result.\n\n## Entity relationship diagram\n\n![erd](../uml/universal.svg)\n\n\nContact Support:\n Name: Veeroute Support Team\n Email: servicedesk@veeroute.com","type":"text/plain"}}}