From 5ab3ee10faf391ed27dcbac233e4d15dcefe0d4c Mon Sep 17 00:00:00 2001 From: Ondrej Sykora Date: Mon, 4 Dec 2023 09:07:41 +0000 Subject: [PATCH] Updated the CFR JSON analysis notebook. Changes made: - Added a new cell with high level solution metrics, - Added code to compute statistics for parking parties (visits of parking locations by mutliple vehicles) and updated the notebook to show this information, - In parking ping-pong detection, take breaks on the route into account, and do not report a bad parking ping-pong if it is created by a break between visits of the ping-pong, - Added moderate-size synthetic test data for the analysis code, and some tests using this new test data, - Ignore empty files when loading data from a local file system, - When detecting shipments with time windows, also look at pickup time windows, - Show shipment index, shipment type, and pickup and delivery times in the individual vehicle route cell, - Show allowed vehicle indices in the shipment list, - Added definitions of parking issues to the text of the notebook, - Show the list of all parkings in the scenario in the aggregate parking stats cell, - In the parking location list, show the number of all and skipped shipments from each parking. --- python/cfr/analysis/analysis.py | 276 +- python/cfr/analysis/analysis_test.py | 255 + python/cfr/analysis/cfr-json-analysis.ipynb | 376 +- .../analysis/testdata/moderate/parking.json | 1253 + .../analysis/testdata/moderate/scenario.json | 26408 ++++++++++++++ .../scenario.merged_request.60s.180s.json | 28938 ++++++++++++++++ .../scenario.merged_response.60s.180s.json | 28180 +++++++++++++++ 7 files changed, 85583 insertions(+), 103 deletions(-) create mode 100644 python/cfr/analysis/testdata/moderate/parking.json create mode 100644 python/cfr/analysis/testdata/moderate/scenario.json create mode 100644 python/cfr/analysis/testdata/moderate/scenario.merged_request.60s.180s.json create mode 100644 python/cfr/analysis/testdata/moderate/scenario.merged_response.60s.180s.json diff --git a/python/cfr/analysis/analysis.py b/python/cfr/analysis/analysis.py index baa0eaab..bc92978c 100644 --- a/python/cfr/analysis/analysis.py +++ b/python/cfr/analysis/analysis.py @@ -23,13 +23,14 @@ class ParkingLocationData: this set. num_visits_to_parking: The number of times each parking is visited. vehicles_by_parking: For each parking tag, contains a mapping from vehicle - indices to the list of indices of the visits made by this vehicle. + indices to the list of indices of the visits made by this vehicle in + global_visits. consecutive_visits: The per-vehicle list of consecutive visits to a parking location. The key of the mapping is the vehicle index, values are lists of visits to a parking location. Each element of this list is a pair (parking_tag, visit_index) such that - `shipments_by_parking[parking_tag][visit_index]` is the visit that - generated the entry. + `global_visits[vehicle_index][visit_index]` is the visit that generated + the entry. non_consecutive_visits: The per-vehicle list of non-consecutive visits to a parking location. The format is the same as for consecutive_visits. shipments_by_parking: The list of parking location visits, indexed by the @@ -54,12 +55,20 @@ class ParkingLocationData: solution is visited. """ - all_parking_tags: Set[str] - num_visits_to_parking: Mapping[str, int] - vehicles_by_parking: Mapping[str, Mapping[int, Sequence[int]]] - consecutive_visits: Mapping[int, Sequence[tuple[str, int]]] - non_consecutive_visits: Mapping[int, Sequence[tuple[str, int]]] - shipments_by_parking: Mapping[str, Sequence[Sequence[int]]] + all_parking_tags: Set[two_step_routing.ParkingTag] + num_visits_to_parking: Mapping[two_step_routing.ParkingTag, int] + vehicles_by_parking: Mapping[ + two_step_routing.ParkingTag, Mapping[int, Sequence[int]] + ] + consecutive_visits: Mapping[ + int, Sequence[tuple[two_step_routing.ParkingTag, int]] + ] + non_consecutive_visits: Mapping[ + int, Sequence[tuple[two_step_routing.ParkingTag, int]] + ] + shipments_by_parking: Mapping[ + two_step_routing.ParkingTag, Sequence[Sequence[int]] + ] global_visits: Mapping[ int, Sequence[tuple[two_step_routing.ParkingTag | None, int, int]] ] @@ -153,6 +162,16 @@ def routes(self) -> Sequence[cfr_json.ShipmentRoute]: """Returns the list of routes in the scenario.""" return self.solution.get("routes", ()) + @functools.cached_property + def shipments_for_parking( + self, + ) -> Mapping[two_step_routing.ParkingTag, Collection[int]]: + """Returns a map from parking tags to shipment indices delivered from there.""" + shipments_for_parking = collections.defaultdict(set) + for shipment_index, parking_tag in self.parking_for_shipment.items(): + shipments_for_parking[parking_tag].add(shipment_index) + return dict(shipments_for_parking) + @property def skipped_shipments(self) -> Sequence[cfr_json.SkippedShipment]: """Returns the list of skipped shipments in the scenario.""" @@ -251,7 +270,11 @@ def get_parking_location_aggregate_data( ) current_parking_tag = arrival_tag current_parking_arrival_visit = visit_index - parking_visit_index = len(shipments_by_parking[arrival_tag]) + # NOTE(ondrasej): At this point, the entry in global_visits doesn't + # exist yet, it will be added only at the departure from this parking. + # But this is OK, because there will be no other parking arrival before + # that departure. + parking_visit_index = len(global_visits) parking_visit_tuple = (arrival_tag, parking_visit_index) parking_vehicles = vehicles_by_parking[arrival_tag] @@ -352,6 +375,7 @@ def get_num_shipments_in_visit_range( def group_global_visits( scenario: Scenario, vehicle_index: int, + split_by_breaks: bool = False, ) -> Iterable[ tuple[two_step_routing.ParkingTag, int, Sequence[cfr_json.Shipment]] ]: @@ -364,6 +388,14 @@ def group_global_visits( Args: scenario: The scenario from which this data is taken. vehicle_index: The index of the vehicle for which the iteration is done. + split_by_breaks: When True, a break between two visits to the same parking + breaks them up into two groups. When False, breaks are ignored by the + algorithm. For example, when a vehicle visits parking P123 twice, has a + break of 10 minutes, and then visits P123 once more, with `split_by_breaks + == True`, the function would return two global visits for P123 - one for + the first two visits, and another one for the third visit. With + `split_by_breaks == False`, it would return just a single group for all + three visits to the parking. Yields: A sequence of triples `(parking_tag, num_rounds, shipments)` where each @@ -382,6 +414,7 @@ def group_global_visits( parking_data = scenario.parking_location_data route = scenario.routes[vehicle_index] visits = cfr_json.get_visits(route) + transitions = cfr_json.get_transitions(route) shipments = scenario.shipments global_visits = parking_data.global_visits.get(vehicle_index, ()) @@ -414,6 +447,10 @@ def group_global_visits( ) if next_parking_tag != parking_tag: break + if split_by_breaks: + transition = transitions[arrival_visit_index] + if bool(cfr_json.get_transition_break_duration(transition)): + break group_shipments.extend( get_shipments_in_visit_range( scenario.model, @@ -435,6 +472,7 @@ def group_global_visits( def get_num_ping_pongs( scenario: Scenario, vehicle_index: int, + split_by_breaks: bool = False, ) -> tuple[int, int]: """Computes the number of "parking ping-pongs" on a single route. @@ -454,6 +492,10 @@ def get_num_ping_pongs( scenario: The scenario in which the number of ping-pongs is computed. vehicle_index: The index of the vehicle for which the number of ping-pongs is computed. + split_by_breaks: When True, a break between two visits to the same parking + breaks them up into two groups, and parking ping-pongs are looked up in + these two groups independently. When False, breaks are not considered in + parking ping-pong detection. Returns: A tuple `(num_ping_pongs, num_bad_ping_pongs)` where `num_ping_pongs` is the @@ -463,7 +505,7 @@ def get_num_ping_pongs( num_ping_pongs = 0 num_bad_ping_pongs = 0 for parking_tag, num_rounds, group_shipments in group_global_visits( - scenario, vehicle_index + scenario, vehicle_index, split_by_breaks=split_by_breaks ): if num_rounds == 1: # Not a ping-pong: either a shipment delivered directly from the vehicle, @@ -646,6 +688,218 @@ def get_num_sandwiches( return num_sandwiches, num_bad_sandwiches +def _get_parking_visit_timestamps( + routes: Sequence[cfr_json.ShipmentRoute], + visits_by_vehicle: Mapping[int, Sequence[int]], + global_visits: Mapping[ + int, + Sequence[tuple[two_step_routing.ParkingTag | None, int, int]], + ], + buffer_time: datetime.timedelta, + expected_parking_tag: two_step_routing.ParkingTag | None = None, +) -> Iterable[tuple[datetime.datetime, int, int]]: + """Iterates over all the arrival and departure times for a parking location. + + Args: + routes: The list of all visits to the parking. + visits_by_vehicle: Mapping from vehicles to the indices of their global + visits. + global_visits: The list of all global visits, in the same format as + ParkingLocationData.global_visits. + buffer_time: The amount of time added at the beginning and and the end of + each visit. This is subtracted from arrival times, and added to departure + times. + expected_parking_tag: The expected parking tag of all the global visits. + Used only for internal consistency checks. + + Yields: + Tuples (timestamp, vehicle_index, delta) for each arrival and departure to + a parking location, where `timestamp` is the timestamp of the arrival or + departure, `vehicle_index` is the index of the vehicle visiting the parking, + and `delta` is 1 for arrivals and -1 for departures. + """ + for vehicle_index, vehicle_global_visits in visits_by_vehicle.items(): + visits = cfr_json.get_visits(routes[vehicle_index]) + for global_visit_index in vehicle_global_visits: + parking_tag, arrival_visit_index, departure_visit_index = global_visits[ + vehicle_index + ][global_visit_index] + + assert ( + expected_parking_tag is None or expected_parking_tag == parking_tag + ), ( + f"expected_parking_tag = {expected_parking_tag!r}, parking_tag =" + f" {parking_tag!r}" + ) + + # The arrival and departure visits have zero duration, so it is OK to take + # their timestamps for the arrival and departure time for the parking. + arrival_visit = visits[arrival_visit_index] + arrival_time = cfr_json.parse_time_string(arrival_visit["startTime"]) + departure_visit = visits[departure_visit_index] + departure_time = cfr_json.parse_time_string(departure_visit["startTime"]) + yield (arrival_time - buffer_time, vehicle_index, 1) + yield (departure_time + buffer_time, vehicle_index, -1) + + +@dataclasses.dataclass +class OverlappingParkingVisit: + """Represents a time interval when a parking is visited by multiple vehicles. + + Note that this represents the time when exactly `self.vehicles` are in the + parking. When there are more than two vehicles, and unless they all arrive and + leave at the same time, there would be multiple instances of this class + representing the different sets of vehicles at the parking. + + Attributes: + parking_tag: The tag of the parking location in question. + start_time: The start of the interval. This is the time when the last + vehicle of `vehicles` arrives to the parking. + end_time: The end of the interval. This is the time when the first of + `vehicles` leaves the parking. + vehicles: The list of vehicles in the parking. + """ + + parking_tag: two_step_routing.ParkingTag + start_time: datetime.datetime + end_time: datetime.datetime + vehicles: Collection[int] + + +@dataclasses.dataclass(frozen=True) +class ParkingPartyStats: + """Contains parking party statistics for a scenario. + + Attributes: + num_parkings_with_multiple_vehicles: The number of parking locations served + by more than one vehicle. + num_party_visits: The number of local delivery rounds from a parking that + are done by "other" vehicles. For each parking, the algorithm takes the + vehicle with most delivery round and considers it to be the "canonical" + vehicle for this parking. Delivery rounds from all other vehicles are + counted in this statistic. + max_vehicles_at_parking_at_once: The maximal number of vehicles that are + visiting a parking location at any given time. + num_overlapping_visit_pairs: The number of pairs of visits to a parking + location that overlap in time. + overlapping_visits: The list of overlapping visits to a parking location. + """ + + num_parkings_with_multiple_vehicles: int + num_party_visits: int + max_vehicles_at_parking_at_once: int + num_overlapping_visit_pairs: int + overlapping_visits: Collection[OverlappingParkingVisit] + + +def get_parking_party_stats( + scenario: Scenario, buffer_time: datetime.timedelta +) -> ParkingPartyStats: + """Computes statistics for "parking parties" in the scenario. + + A "parking party" is the situation where multiple vehicles visit the same + parking location in the same solution. This is generally not desired for two + reasons: the drivers may perceive this as inefficient, and depending on the + situation, there might not be enough space for multiple vehicles in the + parking. However, parking parties may be inevitable in case there are too many + shipments to be delivered from the parking - or too many shipments that need + to be delivered within a certain time window. + + See the documentation of attributes of ParkingPartyStats for the list of + statistics computed by this function. + + Args: + scenario: The scenario for which the stats are computed. + buffer_time: The amount of time added before and after each visit to a + parking location. This can be used to compute a more conservative version + of overlapping visits where we ensure that small delays on the route would + not put multiple vehicles at the same parking. + + Returns: + Parking party stats for this scenario. + """ + # TODO(ondrasej): Find parking locations that use more vehicles than needed. + num_parkings_with_multiple_vehicles = 0 + num_party_visits = 0 + num_overlapping_visit_pairs = 0 + max_vehicles_at_parking_at_once = 0 + overlapping_visits = [] + + parking_data = scenario.parking_location_data + for parking_tag, vehicles in parking_data.vehicles_by_parking.items(): + if len(vehicles) == 1: + continue + + # Count parking locations visited by multiple vehicles. + num_parkings_with_multiple_vehicles += 1 + + # Count parking party visits. + num_all_visits_by_vehicle = 0 + max_num_visits_by_vehicle = 0 + for vehicle_visits in vehicles.values(): + num_visits_by_vehicle = len(vehicle_visits) + max_num_visits_by_vehicle = max( + max_num_visits_by_vehicle, num_visits_by_vehicle + ) + num_all_visits_by_vehicle += num_visits_by_vehicle + num_party_visits += num_all_visits_by_vehicle - max_num_visits_by_vehicle + + # Parking arrivals and departures, sorted by timestamp. + visit_timestamps = sorted( + _get_parking_visit_timestamps( + scenario.routes, + parking_data.vehicles_by_parking[parking_tag], + global_visits=parking_data.global_visits, + buffer_time=buffer_time, + expected_parking_tag=parking_tag, + ) + ) + # NOTE(ondrasej): With buffer time and repeated visits of the same vehicle + # to the same parking, we might see a vehicle "arrive" to a parking location + # multiple times before its first departure. Since this does not create any + # issues with parking availability, we do not count this as an overlapping + # visit. Only overlaps of different vehicles are counted. + present_vehicles = {} + previous_timestamp = None + + for timestamp, vehicle_index, delta in visit_timestamps: + max_vehicles_at_parking_at_once = max( + max_vehicles_at_parking_at_once, len(present_vehicles) + ) + if len(present_vehicles) > 1: + assert previous_timestamp is not None + overlapping_visits.append( + OverlappingParkingVisit( + parking_tag=parking_tag, + start_time=previous_timestamp, + end_time=timestamp, + vehicles=frozenset(present_vehicles), + ) + ) + if delta > 0: + old_vehicle_count = present_vehicles.get(vehicle_index, 0) + present_vehicles[vehicle_index] = old_vehicle_count + 1 + if len(present_vehicles) > 1: + num_overlapping_visit_pairs += len(present_vehicles) - 1 + else: + old_vehicle_count = present_vehicles.get(vehicle_index, 0) + if old_vehicle_count == 1: + del present_vehicles[vehicle_index] + else: + present_vehicles[vehicle_index] = old_vehicle_count - 1 + + previous_timestamp = timestamp + assert not present_vehicles + + return ParkingPartyStats( + num_parkings_with_multiple_vehicles=num_parkings_with_multiple_vehicles, + num_party_visits=num_party_visits, + num_overlapping_visit_pairs=num_overlapping_visit_pairs, + max_vehicles_at_parking_at_once=max_vehicles_at_parking_at_once, + overlapping_visits=overlapping_visits, + ) + + def get_vehicle_shipment_groups( model: cfr_json.ShipmentModel, ) -> Sequence[tuple[Set[int], Set[int]]]: diff --git a/python/cfr/analysis/analysis_test.py b/python/cfr/analysis/analysis_test.py index dd6bb6a7..ce904769 100644 --- a/python/cfr/analysis/analysis_test.py +++ b/python/cfr/analysis/analysis_test.py @@ -1,10 +1,265 @@ +import datetime +from importlib import resources +import json +import logging import unittest from . import analysis from ..json import cfr_json +# Provides easy access to files under `./testdata`. See `_json()` below for +# example use. +# TODO(ondrasej): Move the test data loading code to a separate library. +_TESTDATA = resources.files(__package__).joinpath("testdata") + + +def _json(path: str): + return json.loads(_TESTDATA.joinpath(path).read_bytes()) + + +_SCENARIO: cfr_json.OptimizeToursRequest = _json( + "moderate/scenario.merged_request.60s.180s.json" +) +_SOLUTION: cfr_json.OptimizeToursResponse = _json( + "moderate/scenario.merged_response.60s.180s.json" +) +_PARKING_JSON = _json("moderate/parking.json") + + +class GroupGlobalVisits(unittest.TestCase): + """Tests for group_global_visits.""" + + def setUp(self): + super().setUp() + self._scenario = analysis.Scenario( + name="moderate", + scenario=_SCENARIO, + solution=_SOLUTION, + parking_json=_PARKING_JSON, + ) + + def test_grouped_shipments_no_breaks(self): + groups = analysis.group_global_visits( + self._scenario, vehicle_index=0, split_by_breaks=False + ) + expected_groups = ( + ( + (None, 1, 1), + ("P0002", 1, 19), + (None, 1, 1), + (None, 1, 1), + ("P0012", 2, 20), + ) + + ((None, 1, 1),) * 6 + + (("P0001", 1, 1),) + + ((None, 1, 1),) * 5 + + (("P0001", 1, 1),) + + ((None, 1, 1),) * 5 + + (("P0012", 1, 14),) + + ((None, 1, 1),) * 4 + + ( + ("P0007", 2, 8), + ("P0009", 1, 12), + (None, 1, 1), + ("P0014", 1, 2), + ) + ) + for group, expected_group in zip(groups, expected_groups, strict=True): + tag, num_rounds, shipments = group + expected_tag, expected_num_rounds, expected_num_shipments = expected_group + with self.subTest(f"group {expected_group!r}"): + self.assertEqual(tag, expected_tag) + self.assertEqual(num_rounds, expected_num_rounds) + self.assertEqual(len(shipments), expected_num_shipments) + + def test_grouped_shipments_with_breaks(self): + groups = analysis.group_global_visits( + self._scenario, vehicle_index=0, split_by_breaks=True + ) + expected_groups = ( + ( + (None, 1, 1), + ("P0002", 1, 19), + (None, 1, 1), + (None, 1, 1), + ("P0012", 2, 20), + ) + + ((None, 1, 1),) * 6 + + (("P0001", 1, 1),) + + ((None, 1, 1),) * 5 + + (("P0001", 1, 1),) + + ((None, 1, 1),) * 5 + + (("P0012", 1, 14),) + + ((None, 1, 1),) * 4 + + ( + ("P0007", 1, 2), + ("P0007", 1, 6), + ("P0009", 1, 12), + (None, 1, 1), + ("P0014", 1, 2), + ) + ) + for group, expected_group in zip(groups, expected_groups, strict=True): + tag, num_rounds, shipments = group + expected_tag, expected_num_rounds, expected_num_shipments = expected_group + with self.subTest(f"group {expected_group!r}"): + self.assertEqual(tag, expected_tag) + self.assertEqual(num_rounds, expected_num_rounds) + self.assertEqual(len(shipments), expected_num_shipments) + + +class GetNumPingPongsTest(unittest.TestCase): + """Tests for get_num_ping_pongs.""" + + def setUp(self): + super().setUp() + self._scenario = analysis.Scenario( + name="moderate", + scenario=_SCENARIO, + solution=_SOLUTION, + parking_json=_PARKING_JSON, + ) + + def test_with_breaks_vehicle_0(self): + num_ping_pongs, num_bad_ping_pongs = analysis.get_num_ping_pongs( + self._scenario, vehicle_index=0, split_by_breaks=True + ) + self.assertEqual(num_ping_pongs, 1) + self.assertEqual(num_bad_ping_pongs, 1) + + def test_without_breaks_vehicle_0(self): + # Vehicle 0 uses breaks, and one of them is in the middle of a bad parking + # ping-pong. + num_ping_pongs, num_bad_ping_pongs = analysis.get_num_ping_pongs( + self._scenario, vehicle_index=0, split_by_breaks=False + ) + self.assertEqual(num_ping_pongs, 2) + self.assertEqual(num_bad_ping_pongs, 2) + + def test_with_breaks_vehicle_1(self): + num_ping_pongs, num_bad_ping_pongs = analysis.get_num_ping_pongs( + self._scenario, vehicle_index=1, split_by_breaks=True + ) + self.assertEqual(num_ping_pongs, 2) + self.assertEqual(num_bad_ping_pongs, 1) + + def test_without_breaks_vehicle_1(self): + # Vehicle 1 does not use breaks. + num_ping_pongs, num_bad_ping_pongs = analysis.get_num_ping_pongs( + self._scenario, vehicle_index=1, split_by_breaks=False + ) + self.assertEqual(num_ping_pongs, 2) + self.assertEqual(num_bad_ping_pongs, 1) + + +class GetParkingPartyStats(unittest.TestCase): + """Tests for get_parking_party_stats.""" + + maxDiff = None + + def setUp(self): + super().setUp() + self._scenario = analysis.Scenario( + name="moderate", + scenario=_SCENARIO, + solution=_SOLUTION, + parking_json=_PARKING_JSON, + ) + + def test_get_parking_party_stats_no_buffer(self): + party_stats = analysis.get_parking_party_stats( + self._scenario, datetime.timedelta() + ) + + expected_party_stats = analysis.ParkingPartyStats( + num_parkings_with_multiple_vehicles=19, + num_party_visits=32, + max_vehicles_at_parking_at_once=2, + num_overlapping_visit_pairs=2, + overlapping_visits=[ + analysis.OverlappingParkingVisit( + parking_tag="P0013", + start_time=datetime.datetime( + 2023, 11, 17, 11, 57, 17, tzinfo=datetime.timezone.utc + ), + end_time=datetime.datetime( + 2023, 11, 17, 12, 40, 55, tzinfo=datetime.timezone.utc + ), + vehicles=frozenset({3, 5}), + ), + analysis.OverlappingParkingVisit( + parking_tag="P0013", + start_time=datetime.datetime( + 2023, 11, 17, 14, 28, 28, tzinfo=datetime.timezone.utc + ), + end_time=datetime.datetime( + 2023, 11, 17, 14, 50, 42, tzinfo=datetime.timezone.utc + ), + vehicles=frozenset({1, 2}), + ), + ], + ) + self.assertEqual(party_stats, expected_party_stats) + + def test_get_parking_party_stats_15m_buffer(self): + party_stats = analysis.get_parking_party_stats( + self._scenario, datetime.timedelta(minutes=15) + ) + + expected_party_stats = analysis.ParkingPartyStats( + num_parkings_with_multiple_vehicles=19, + num_party_visits=32, + max_vehicles_at_parking_at_once=2, + num_overlapping_visit_pairs=4, + overlapping_visits=[ + analysis.OverlappingParkingVisit( + parking_tag="P0012", + start_time=datetime.datetime( + 2023, 11, 17, 14, 43, 45, tzinfo=datetime.timezone.utc + ), + end_time=datetime.datetime( + 2023, 11, 17, 14, 47, 41, tzinfo=datetime.timezone.utc + ), + vehicles=frozenset({0, 5}), + ), + analysis.OverlappingParkingVisit( + parking_tag="P0012", + start_time=datetime.datetime( + 2023, 11, 17, 16, 0, 9, tzinfo=datetime.timezone.utc + ), + end_time=datetime.datetime( + 2023, 11, 17, 16, 9, 21, tzinfo=datetime.timezone.utc + ), + vehicles=frozenset({5, 7}), + ), + analysis.OverlappingParkingVisit( + parking_tag="P0013", + start_time=datetime.datetime( + 2023, 11, 17, 11, 42, 17, tzinfo=datetime.timezone.utc + ), + end_time=datetime.datetime( + 2023, 11, 17, 12, 55, 55, tzinfo=datetime.timezone.utc + ), + vehicles=frozenset({3, 5}), + ), + analysis.OverlappingParkingVisit( + parking_tag="P0013", + start_time=datetime.datetime( + 2023, 11, 17, 14, 13, 28, tzinfo=datetime.timezone.utc + ), + end_time=datetime.datetime( + 2023, 11, 17, 15, 5, 42, tzinfo=datetime.timezone.utc + ), + vehicles=frozenset({1, 2}), + ), + ], + ) + self.assertEqual(party_stats, expected_party_stats) + + class VehicleShipmentGroupsTest(unittest.TestCase): + """Tests for get_vehicle_shipment_groups.""" def test_without_allowed_vehicle_indices(self): model: cfr_json.ShipmentModel = { diff --git a/python/cfr/analysis/cfr-json-analysis.ipynb b/python/cfr/analysis/cfr-json-analysis.ipynb index 99756c2f..ad29100f 100644 --- a/python/cfr/analysis/cfr-json-analysis.ipynb +++ b/python/cfr/analysis/cfr-json-analysis.ipynb @@ -59,6 +59,7 @@ " [local runtime guide](https://research.google.com/colaboratory/local-runtimes.html).\n", " As of 2023-10-23, using the `COLAB_KERNEL_MANAGER_PROXY_PORT` option is\n", " needed to make file upload work correctly.\n", + "-->\n", "\n", "### How to use the colab\n", "\n", @@ -124,10 +125,12 @@ "\n", "# Clone the CFR library from GitHub, and import from there.\n", "!git clone https://github.com/google/cfr\n", + "from cfr.python.cfr import utils\n", "from cfr.python.cfr.analysis import analysis\n", "from cfr.python.cfr.json import cfr_json\n", "from cfr.python.cfr.json import human_readable\n", - "from cfr.python.cfr.two_step_routing import two_step_routing\n" + "from cfr.python.cfr.two_step_routing import two_step_routing\n", + "" ] }, { @@ -282,7 +285,9 @@ " ]\n", "):\n", " display.display(\n", - " data_table.DataTable(dataframe_from_all_scenarios(row_callback))\n", + " data_table.DataTable(\n", + " dataframe_from_all_scenarios(row_callback).fillna(\"\")\n", + " )\n", " )\n", "\n", "\n", @@ -323,48 +328,18 @@ " assert get_glob_substitutions(\"*-bar-*\", \"foo-bar-baz\") == (\"foo\", \"baz\")\n", "\n", "\n", - "test_get_glob_substitutions()\n", - "\n", - "\n", - "def reformat_timestring_or_default(\n", - " value: cfr_json.TimeString | None, default: str\n", - ") -> str:\n", - " \"\"\"Returns a formatted timestamp or `default`, if `value` is `None`.\"\"\"\n", - " # TODO(ondrasej): If the global span of the scenario is \u003c= 24 hours, do not\n", - " # show the date. Also, normalize all timestamps to the same timezone and do\n", - " # not show the timezone suffix.\n", - " if value is not None:\n", - " return str(cfr_json.parse_time_string(value))\n", - " return default\n", + "def duration_for_spreadsheet(value) -> str:\n", + " total_seconds = int(value.total_seconds())\n", + " hours = total_seconds // 3600\n", + " remaining_seconds = total_seconds % 3600\n", + " return f\"{hours}:{remaining_seconds // 60:02d}:{remaining_seconds % 60:02d}\"\n", "\n", "\n", - "def human_readable_time_window(time_window: cfr_json.TimeWindow) -> str:\n", - " \"\"\"Formats a time window as a human readable string.\"\"\"\n", - " parts = []\n", - " start = time_window.get(\"startTime\")\n", - " end = time_window.get(\"endTime\")\n", - " if start is not None or end is not None:\n", - " parts.append(\n", - " f\"{reformat_timestring_or_default(start, '...')} -\"\n", - " f\" {reformat_timestring_or_default(end, '...')}\"\n", - " )\n", - " soft_start = time_window.get(\"softStartTime\")\n", - " soft_end = time_window.get(\"softEndTime\")\n", - " if soft_start is not None or soft_end is not None:\n", - " parts.append(\n", - " f\"soft: {reformat_timestring_or_default(start, '...')} -\"\n", - " f\" {reformat_timestring_or_default(end, '...')}\"\n", - " )\n", - " return \" \".join(parts)\n", + "def duration_string_for_spreadsheet(duration: cfr_json.DurationString) -> str:\n", + " return duration_for_spreadsheet(cfr_json.parse_duration_string(duration))\n", "\n", "\n", - "def human_readable_time_windows(\n", - " time_windows: Sequence[cfr_json.TimeWindow],\n", - ") -> str:\n", - " \"\"\"Formats a list of time windows as a human readable string.\"\"\"\n", - " return \" | \".join(\n", - " human_readable_time_window(time_window) for time_window in time_windows\n", - " )" + "test_get_glob_substitutions()" ] }, { @@ -623,15 +598,17 @@ " parking_not_found = []\n", "\n", " for substitutions, solution_file in solution_matches.items():\n", + " if not utils.is_non_empty_file(solution_file):\n", + " continue\n", " name = _apply_substitutions(name_template, substitutions)\n", " scenario_file = _apply_substitutions(scenario_template, substitutions)\n", - " if not os.path.exists(scenario_file):\n", + " if not utils.is_non_empty_file(scenario_file):\n", " scenario_not_found.append(name)\n", " continue\n", " parking_file = None\n", " if parking_template:\n", " parking_file = _apply_substitutions(parking_template, substitutions)\n", - " if not os.path.exists(parking_file):\n", + " if not utils.is_non_empty_file(parking_file):\n", " parking_not_found.append(name)\n", " continue\n", "\n", @@ -687,6 +664,50 @@ "## Basic request/response data" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "d-rGxu5ewgXl" + }, + "outputs": [], + "source": [ + "# @title Solution metrics\n", + "\n", + "\n", + "def get_metrics(scenario):\n", + " metrics = scenario.solution[\"metrics\"]\n", + " aggregated_metrics = metrics.get(\"aggregatedRouteMetrics\", {})\n", + "\n", + " data = {\n", + " \"total cost\": round(metrics.get(\"totalCost\", 0), 2),\n", + " \"total duration\": duration_string_for_spreadsheet(\n", + " aggregated_metrics.get(\"totalDuration\", \"0s\")\n", + " ),\n", + " \"wait duration\": duration_string_for_spreadsheet(\n", + " aggregated_metrics.get(\"waitDuration\", \"0s\")\n", + " ),\n", + " \"delay duration\": duration_string_for_spreadsheet(\n", + " aggregated_metrics.get(\"delayDuration\", \"0s\")\n", + " ),\n", + " \"travel duration\": duration_string_for_spreadsheet(\n", + " aggregated_metrics.get(\"travelDuration\", \"0s\")\n", + " ),\n", + " \"skipped mandatory shipments\": metrics.get(\n", + " \"skippedMandatoryShipmentCount\", 0\n", + " ),\n", + " \"used vehicles\": metrics.get(\"usedVehicleCount\", 0),\n", + " }\n", + " for cost_name, cost_value in metrics.get(\"costs\", {}).items():\n", + " readable_name = cost_name.split(\".\")[-1]\n", + " data[readable_name] = round(cost_value, 2)\n", + " return data\n", + "\n", + "\n", + "show_table_from_all_scenarios(get_metrics)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -703,6 +724,9 @@ " for delivery in shipment.get(\"deliveries\", ()):\n", " if \"timeWindows\" in delivery:\n", " return True\n", + " for delivery in shipment.get(\"pickups\", ()):\n", + " if \"timeWindows\" in delivery:\n", + " return True\n", " return False\n", "\n", "\n", @@ -782,12 +806,6 @@ "# @title Aggregated vehicle statistics\n", "\n", "\n", - "def _format_duration(value) -> str:\n", - " total_seconds = int(value.total_seconds())\n", - " hours = total_seconds // 3600\n", - " remaining_seconds = total_seconds % 3600\n", - " return f\"{hours}:{remaining_seconds // 60:02d}:{remaining_seconds % 60:02d}\"\n", - "\n", "def _safe_percentage(value, maximum) -> str:\n", " if not maximum:\n", " # The maximum is zero or a zero-like value (e.g. datetime.timedelta of zero\n", @@ -825,10 +843,10 @@ "\n", " return {\n", " \"# vehicles\": len(vehicles),\n", - " \"max working time\": _format_duration(max_working_hours),\n", - " \"soft working time\": _format_duration(soft_working_hours),\n", - " \"actual working time\": _format_duration(actual_working_hours),\n", - " \"wait time\": _format_duration(wait_hours),\n", + " \"max working time\": duration_for_spreadsheet(max_working_hours),\n", + " \"soft working time\": duration_for_spreadsheet(soft_working_hours),\n", + " \"actual working time\": duration_for_spreadsheet(actual_working_hours),\n", + " \"wait time\": duration_for_spreadsheet(wait_hours),\n", " \"max working time %\": _safe_percentage(\n", " actual_working_hours, max_working_hours\n", " ),\n", @@ -859,11 +877,13 @@ "def get_shipment_list(scenario):\n", " shipments = scenario.shipments\n", "\n", + " shipment_visits = collections.defaultdict(list)\n", " shipment_to_vehicle = {}\n", " for route_index, route in enumerate(scenario.routes):\n", - " for visit in route.get(\"visits\", ()):\n", + " for visit_index, visit in enumerate(route.get(\"visits\", ())):\n", " shipment_index = visit.get(\"shipmentIndex\", 0)\n", " shipment_to_vehicle[shipment_index] = route_index\n", + " shipment_visits[shipment_index].append(visit)\n", "\n", " data = []\n", " for shipment_index, shipment in enumerate(shipments):\n", @@ -871,17 +891,15 @@ " pickup_durations = []\n", " pickups = shipment.get(\"pickups\", ())\n", " for pickup in pickups:\n", - " time_windows = pickup.get(\"timeWindows\")\n", - " if time_windows is not None:\n", - " pickup_time_windows.append(human_readable_time_windows(time_windows))\n", + " if (time_windows := pickup.get(\"timeWindows\")) is not None:\n", + " pickup_time_windows.append(human_readable.time_windows(time_windows))\n", " pickup_durations.append(pickup.get(\"duration\", \"0s\"))\n", " delivery_time_windows = []\n", " delivery_durations = []\n", " deliveries = shipment.get(\"deliveries\", ())\n", " for delivery in deliveries:\n", - " time_windows = delivery.get(\"timeWindows\")\n", - " if time_windows is not None:\n", - " delivery_time_windows.append(human_readable_time_windows(time_windows))\n", + " if (time_windows := delivery.get(\"timeWindows\")) is not None:\n", + " delivery_time_windows.append(human_readable.time_windows(time_windows))\n", " delivery_durations.append(delivery.get(\"duration\", \"0s\"))\n", " vehicle_index = shipment_to_vehicle.get(shipment_index)\n", " vehicle_label = (\n", @@ -889,14 +907,28 @@ " if vehicle_index is not None\n", " else \"\"\n", " )\n", + " # Each shipment has at most two visits: a pickup and a delivery.\n", + " pickup_time = \"\"\n", + " delivery_time = \"\"\n", + " for visit in shipment_visits[shipment_index]:\n", + " if visit.get(\"isPickup\"):\n", + " pickup_time = visit[\"startTime\"]\n", + " else:\n", + " delivery_time = visit[\"startTime\"]\n", " data.append({\n", " \"label\": shipment.get(\"label\", \"\"),\n", " \"vehicle\": vehicle_label,\n", + " \"pickup time\": pickup_time,\n", + " \"delivery time\": delivery_time,\n", " \"type\": shipment.get(\"shipmentType\", \"\"),\n", - " \"pickup times\": \" | \".join(pickup_time_windows),\n", + " \"pickup time windows\": \" | \".join(pickup_time_windows),\n", " \"pickup durations\": \" | \".join(pickup_durations),\n", - " \"delivery times\": \" | \".join(delivery_time_windows),\n", + " \"delivery time windows\": \" | \".join(delivery_time_windows),\n", " \"delivery durations\": \" | \".join(delivery_durations),\n", + " \"allowed vehicles\": \", \".join(\n", + " str(vehicle_index)\n", + " for vehicle_index in shipment.get(\"allowedVehicleIndices\", ())\n", + " ),\n", " \"parking tag\": scenario.parking_for_shipment.get(shipment_index, \"\"),\n", " })\n", " return data\n", @@ -919,6 +951,7 @@ "\n", "def get_vehicle_list(scenario):\n", " used_vehicles_only = False # @param{type: \"boolean\"}\n", + " split_by_breaks = True # @param{type: \"boolean\"}\n", " parking_data = scenario.parking_location_data\n", " shipments = scenario.shipments\n", " data = []\n", @@ -967,7 +1000,7 @@ " continue\n", "\n", " num_ping_pongs, num_bad_ping_pongs = analysis.get_num_ping_pongs(\n", - " scenario, vehicle_index\n", + " scenario, vehicle_index, split_by_breaks=split_by_breaks\n", " )\n", " num_sandwiches, num_bad_sandwiches = analysis.get_num_sandwiches(\n", " scenario, vehicle_index\n", @@ -1093,7 +1126,7 @@ " )\n", " for delivery in shipment.get(\"deliveries\", ()):\n", " time_windows = delivery.get(\"timeWindows\", ())\n", - " str_time_window = human_readable_time_windows(time_windows)\n", + " str_time_window = human_readable.time_windows(time_windows)\n", " shipments_by_time_window[allowed_vehicles, str_time_window].append(\n", " shipment\n", " )\n", @@ -1167,7 +1200,7 @@ " scenario.skipped_shipment_indices \u0026 shipment_indices\n", " )\n", " vehicle_labels = sorted(\n", - " scenario.vehicle_label(vehicle_index)\n", + " str(vehicle_index) + \": \" + scenario.vehicle_label(vehicle_index)\n", " for vehicle_index in vehicle_indices\n", " )\n", " data.append({\n", @@ -1248,12 +1281,15 @@ " current_break = breaks.pop(0)\n", " data.append({\n", " \"vehicle\": vehicle_label,\n", + " \"shipment index\": \"\",\n", " \"shipment\": \"\",\n", + " \"shipment type\": \"\",\n", " \"type\": \"break\",\n", " \"latlng\": \"\",\n", " \"start\": str(next_break_start),\n", " \"since start\": str(next_break_start - start_time),\n", " \"duration\": current_break.get(\"duration\", \"0s\"),\n", + " \"distance\": \"\",\n", " \"transition\": \"\",\n", " \"time window\": \"\",\n", " })\n", @@ -1262,18 +1298,23 @@ " add_breaks_before_timestamp(start_time)\n", " data.append({\n", " \"vehicle\": vehicle_label,\n", + " \"shipment index\": \"\",\n", " \"shipment\": \"start\",\n", + " \"shipment type\": \"\",\n", " \"type\": \"\",\n", " \"latlng\": human_readable.vehicle_start_location(vehicle),\n", " \"start\": str(start_time),\n", " \"since start\": \"0:00:00\",\n", " \"duration\": \"0s\",\n", + " \"distance\": f\"{int(transitions[0].get('travelDistanceMeters', 0.0))} m\",\n", " \"transition\": (\n", " human_readable.transition_duration(transitions[0])\n", " if transitions\n", " else \"\"\n", " ),\n", - " \"time window\": vehicle.get(\"startTimeWindows\", \"\"),\n", + " \"time window\": human_readable.time_windows(\n", + " vehicle.get(\"startTimeWindows\")\n", + " ),\n", " })\n", "\n", " for visit_index, visit in enumerate(visits):\n", @@ -1283,25 +1324,30 @@ " shipment = shipments[shipment_index]\n", " visit_start = cfr_json.parse_time_string(visit[\"startTime\"])\n", " visit_type = \"pickup\" if visit.get(\"isPickup\") else \"delivery\"\n", - " visit_request = cfr_json.get_visit_request(scenario.model, visit)\n", "\n", " add_breaks_before_timestamp(visit_start)\n", " data.append({\n", " \"vehicle\": vehicle_label,\n", + " \"shipment index\": visit.get(\"shipmentIndex\", 0),\n", " \"shipment\": visit.get(\"shipmentLabel\", \"\"),\n", + " \"shipment type\": shipment.get(\"shipmentType\", \"\"),\n", " \"type\": visit_type,\n", " \"latlng\": human_readable.visit_request_location(visit_request),\n", " \"start\": str(visit_start),\n", " \"since start\": str(visit_start - start_time),\n", " \"duration\": visit_request.get(\"duration\"),\n", + " \"distance\": f\"{int(transition_out.get('travelDistanceMeters', 0.0))} m\",\n", " \"transition\": human_readable.transition_duration(transition_out),\n", - " \"time window\": shipment[\"deliveries\"][0].get(\"timeWindows\", \"\"),\n", + " \"time window\": human_readable.time_windows(\n", + " visit_request.get(\"timeWindows\")\n", + " ),\n", " })\n", " end_time = cfr_json.parse_time_string(route[\"vehicleEndTime\"])\n", "\n", " add_breaks_before_timestamp(end_time)\n", " data.append({\n", " \"vehicle\": vehicle_label,\n", + " \"shipment index\": \"\",\n", " \"shipment\": \"end\",\n", " \"type\": \"\",\n", " \"latlng\": human_readable.vehicle_end_location(vehicle),\n", @@ -1369,6 +1415,58 @@ "## Parking location data" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "NI5XmIVORcgt" + }, + "source": [ + "### Definitions\n", + "\n", + "* **Parking ping-pong** is the situation where the same parking location is\n", + " visited by a vehicle multiple times in a row. In practice, this corresponds\n", + " to multiple delivery rounds from this parking and it can happen when the\n", + " number of shipments delivered from this parking location exceeds the\n", + " delivery capacity of this parking. This may also happen as an artifact of\n", + " the planning algorithm.\n", + "\n", + " We say that a ping-pong is a **bad ping-pong** when the number of delivery\n", + " rounds in the ping-pong is higher than what would be required by the load\n", + " limits while delivering from the parking. For example, when there are 50\n", + " shipments delivered from parking P123 and the load limit for deliveries from\n", + " this parking is 20, a plan where the shipments are delivered in 4 or more\n", + " rounds scheduled back to back has a case of bad ping-pong. If the shipments\n", + " are delivered in 3 rounds scheduled back to back, this is not a case of bad\n", + " ping-pong.\n", + "\n", + "* **Parking sandwich** is the situation where the same parking location is\n", + " visited multiple times by the same vehicle, but the vehicle leaves the\n", + " parking location between the visits. For example, when the vehicle delivers\n", + " shipments from parking P123, then from parking P456, and then again from\n", + " P123, it is a case of parking sandwich.\n", + "\n", + " Parking sandwiches may appear on a route when shipments delivered from a\n", + " parking have time windows that are far apart, but they may also appear\n", + " without time windows, for example when there are no costs making the solver\n", + " group visits to the same parking.\n", + "\n", + " We say that a sandwich is a **bad sandwich** when the separated visits to\n", + " the parking location are because of other reasons than time windows that are\n", + " far apart.\n", + "\n", + "* **Parking party** is the situation where multiple vehicles visit the same\n", + " parking location. A parking party may happen when there are more shipments\n", + " delivered from the parking location than a single vehicle can handle. It may\n", + " also happen when the costs and constraints of a model are set up in such a\n", + " way that using multiple vehicles leads to a lower cost.\n", + "\n", + " We say that a parking party is a **bad party** when the party happens\n", + " because of reasons other than vehicle/delivery capacity constraints. Note\n", + " that a party may also be created when there are too many shipments with the\n", + " same time window than a single vehicle can deliver. Such a case is also not\n", + " considered a bad party." + ] + }, { "cell_type": "code", "execution_count": null, @@ -1382,14 +1480,23 @@ "\n", "\n", "def get_parking_locations_summary(scenario):\n", + " split_by_breaks = True # @param {type: \"boolean\"}\n", + " buffer_time_seconds = 3600 # @param {type: \"number\"}\n", + " buffer_time = datetime.timedelta(seconds=buffer_time_seconds)\n", " parking_data = scenario.parking_location_data\n", "\n", + " party_stats = analysis.get_parking_party_stats(\n", + " scenario, buffer_time=buffer_time\n", + " )\n", + "\n", " num_ping_pongs = 0\n", " num_bad_ping_pongs = 0\n", " num_sandwiches = 0\n", " num_bad_sandwiches = 0\n", " for vehicle_index in range(len(scenario.routes)):\n", - " ping_pong_stats = analysis.get_num_ping_pongs(scenario, vehicle_index)\n", + " ping_pong_stats = analysis.get_num_ping_pongs(\n", + " scenario, vehicle_index, split_by_breaks\n", + " )\n", " num_ping_pongs += ping_pong_stats[0]\n", " num_bad_ping_pongs += ping_pong_stats[1]\n", "\n", @@ -1398,18 +1505,22 @@ " num_bad_sandwiches += sandwich_stats[1]\n", "\n", " return [{\n", + " \"# distinct parkings\": len(scenario.parking_locations),\n", " \"# distinct visited parkings\": len(parking_data.all_parking_tags),\n", " \"# visits to parking\": parking_data.num_all_visits_to_parking,\n", - " \"# parkings served by multiple vehicles\": sum(\n", - " int(len(parking_vehicles) > 1)\n", - " for parking_vehicles in parking_data.vehicles_by_parking.values()\n", + " \"# parkings served by multiple vehicles\": (\n", + " party_stats.num_parkings_with_multiple_vehicles\n", + " ),\n", + " \"# party visits\": party_stats.num_party_visits,\n", + " \"# overlapping party visits\": party_stats.num_overlapping_visit_pairs,\n", + " \"max overlapping party visits\": (\n", + " party_stats.max_vehicles_at_parking_at_once\n", " ),\n", " \"# ping-pong visits\": sum(\n", " len(visits) for visits in parking_data.consecutive_visits.values()\n", " ),\n", " \"# ping-pongs\": num_ping_pongs,\n", - " \"# bad ping-ponds\": num_bad_ping_pongs,\n", - "\n", + " \"# bad ping-pongs\": num_bad_ping_pongs,\n", " \"# sandwich visits\": sum(\n", " len(visits) for visits in parking_data.non_consecutive_visits.values()\n", " ),\n", @@ -1433,35 +1544,107 @@ "# @title Parking location list\n", "\n", "\n", + "def _merge_overlaps(overlaps):\n", + " \"\"\"Merges back-to-back overlaps by the same vehicle.\n", + "\n", + " Parking ping-pongs may create multiple overlaps that are back-to-back and that\n", + " use the same vehicles. This is hard to read for the user and does not change\n", + " the fact that there are multiple vehicles in the parking, so we just merge\n", + " those into a single overlap.\n", + " \"\"\"\n", + " previous_vehicles = ()\n", + " previous_tag = None\n", + " previous_end_time = None\n", + " start_time = None\n", + " for overlap in overlaps:\n", + " if start_time is None:\n", + " start_time = overlap.start_time\n", + " elif (\n", + " previous_vehicles != overlap.vehicles\n", + " or previous_tag != overlap.parking_tag\n", + " or previous_end_time != overlap.start_time\n", + " ):\n", + " yield analysis.OverlappingParkingVisit(\n", + " parking_tag=previous_tag,\n", + " start_time=start_time,\n", + " vehicles=previous_vehicles,\n", + " end_time=previous_end_time,\n", + " )\n", + " start_time = overlap.start_time\n", + " previous_vehicles = overlap.vehicles\n", + " previous_tag = overlap.parking_tag\n", + " previous_end_time = overlap.end_time\n", + "\n", + " if start_time is not None:\n", + " yield analysis.OverlappingParkingVisit(\n", + " parking_tag=previous_tag,\n", + " start_time=start_time,\n", + " end_time=previous_end_time,\n", + " vehicles=previous_vehicles,\n", + " )\n", + "\n", + "\n", "def get_parking_location_list(scenario):\n", " parking_data = scenario.parking_location_data\n", + " party_stats = analysis.get_parking_party_stats(\n", + " scenario, datetime.timedelta(0)\n", + " )\n", " vehicles = scenario.vehicles\n", " data = []\n", " shipments = scenario.shipments\n", - " for parking_tag, parking_vehicles in sorted(\n", - " parking_data.vehicles_by_parking.items()\n", - " ):\n", - " num_cfr_shipments = 0\n", - " num_actual_shipments = 0\n", - " for shipment_group in parking_data.shipments_by_parking[parking_tag]:\n", - " num_cfr_shipments += len(shipment_group)\n", - " num_actual_shipments += sum(\n", - " shipments[shipment].get(\"label\", \"\").count(\",\") + 1\n", - " for shipment in shipment_group\n", - " )\n", + "\n", + " overlaps_by_parking = collections.defaultdict(list)\n", + " for overlap in party_stats.overlapping_visits:\n", + " overlaps_by_parking[overlap.parking_tag].append(overlap)\n", + "\n", + " for parking_tag in sorted(scenario.parking_locations):\n", + " shipments_for_parking = scenario.shipments_for_parking.get(parking_tag, ())\n", + " parking_vehicles = parking_data.vehicles_by_parking.get(parking_tag, ())\n", + " skipped_shipments_for_parking = set(\n", + " shipment_index\n", + " for shipment_index in shipments_for_parking\n", + " if shipment_index in scenario.skipped_shipment_indices\n", + " )\n", + "\n", + " num_cfr_shipments = len(shipments_for_parking)\n", + " num_actual_shipments = sum(\n", + " shipments[shipment].get(\"label\", \"\").count(\",\") + 1\n", + " for shipment in shipments_for_parking\n", + " )\n", + "\n", + " num_skipped_cfr_shipments = len(skipped_shipments_for_parking)\n", + " num_skipped_actual_shipments = sum(\n", + " shipments[shipment].get(\"label\", \"\").count(\",\") + 1\n", + " for shipment in skipped_shipments_for_parking\n", + " )\n", "\n", " vehicle_labels = sorted(\n", " scenario.vehicle_label(vehicle_index)\n", " for vehicle_index in parking_vehicles\n", " )\n", "\n", + " overlaps = overlaps_by_parking.get(parking_tag, [])\n", + " overlaps.sort(key=lambda overlap: overlap.start_time)\n", + " overlap_parts = []\n", + " for overlap in _merge_overlaps(overlaps):\n", + " vehicles = \", \".join(\n", + " scenario.vehicle_label(vehicle_index)\n", + " for vehicle_index in overlap.vehicles\n", + " )\n", + " overlap_parts.append(\n", + " f\"{overlap.start_time} - {overlap.end_time}: {vehicles}\"\n", + " )\n", + "\n", " data.append({\n", " \"parking_tag\": parking_tag,\n", " \"# visits\": parking_data.num_visits_to_parking[parking_tag],\n", " \"# CFR shipments\": num_cfr_shipments,\n", " \"# actual shipments\": num_actual_shipments,\n", + " \"# skipped CFR shipments\": num_skipped_cfr_shipments,\n", + " \"# skipped actual shipments\": num_skipped_actual_shipments,\n", " \"# vehicles\": len(parking_vehicles),\n", " \"vehicles\": \", \".join(vehicle_labels),\n", + " \"overlaps\": \" | \".join(overlap_parts),\n", " })\n", " return data\n", "\n", @@ -1483,19 +1666,21 @@ "\n", "def _shipment_row(scenario, shipment_index):\n", " shipment = scenario.shipments[shipment_index]\n", - " deliveries = shipment.get(\"deliveries\", ())\n", - " time_windows = ()\n", - " if deliveries:\n", - " time_windows = deliveries[0].get(\"timeWindows\", ())\n", + " time_windows = []\n", + " for delivery in shipment.get(\"deliveries\", ()):\n", + " time_windows.extend(delivery.get(\"timeWindows\", ()))\n", + " vehicle_label = (\n", + " \"\"\n", + " if shipment_index in scenario.skipped_shipment_indices\n", + " else scenario.vehicle_label(scenario.vehicle_for_shipment[shipment_index])\n", + " )\n", " shipment_row = {\n", " \"label\": shipment.get(\"label\", \"\"),\n", " \"allowed vehicles\": \", \".join(\n", " str(vehicle) for vehicle in shipment.get(\"allowedVehicleIndices\", ())\n", " ),\n", - " \"delivery time window\": human_readable_time_windows(time_windows),\n", - " \"used vehicle\": scenario.vehicle_label(\n", - " scenario.vehicle_for_shipment[shipment_index]\n", - " ),\n", + " \"delivery time window\": human_readable.time_windows(time_windows),\n", + " \"used vehicle\": vehicle_label,\n", " }\n", " return shipment_row\n", "\n", @@ -1538,6 +1723,13 @@ " row.update(_shipment_row(scenario, shipment_index))\n", " shipment_data.append(row)\n", "\n", + " for shipment_index in scenario.shipments_for_parking[parking_tag]:\n", + " if shipment_index not in scenario.skipped_shipment_indices:\n", + " continue\n", + " row = {\"index in group\": \"\", \"group\": \"\"}\n", + " row.update(_shipment_row(scenario, shipment_index))\n", + " shipment_data.append(row)\n", + "\n", " output.clear(output_tags=[\"table_container\"])\n", " with output.use_tags(\"table_container\", append=True):\n", " display.display(data_table.DataTable(pd.DataFrame(shipment_data)))\n", diff --git a/python/cfr/analysis/testdata/moderate/parking.json b/python/cfr/analysis/testdata/moderate/parking.json new file mode 100644 index 00000000..d1f43dcc --- /dev/null +++ b/python/cfr/analysis/testdata/moderate/parking.json @@ -0,0 +1,1253 @@ +{ + "parking_locations": [ + { + "coordinates": { + "latitude": 48.869699340243145, + "longitude": 2.3724342341489235 + }, + "tag": "P0001", + "travel_mode": 1, + "travel_duration_multiple": 1.0, + "delivery_load_limits": { + "shipment_count": 20 + }, + "max_round_duration": null, + "arrival_duration": null, + "departure_duration": null, + "reload_duration": null, + "arrival_cost": 0.0, + "departure_cost": 0.0, + "reload_cost": 0.0 + }, + { + "coordinates": { + "latitude": 48.86928189492348, + "longitude": 2.3598743264313997 + }, + "tag": "P0002", + "travel_mode": 1, + "travel_duration_multiple": 1.0, + "delivery_load_limits": { + "shipment_count": 20 + }, + "max_round_duration": null, + "arrival_duration": null, + "departure_duration": null, + "reload_duration": null, + "arrival_cost": 0.0, + "departure_cost": 0.0, + "reload_cost": 0.0 + }, + { + "coordinates": { + "latitude": 48.881586142340005, + "longitude": 2.3113265123264704 + }, + "tag": "P0003", + "travel_mode": 1, + "travel_duration_multiple": 1.0, + "delivery_load_limits": { + "shipment_count": 20 + }, + "max_round_duration": null, + "arrival_duration": null, + "departure_duration": null, + "reload_duration": null, + "arrival_cost": 0.0, + "departure_cost": 0.0, + "reload_cost": 0.0 + }, + { + "coordinates": { + "latitude": 48.890884722848405, + "longitude": 2.3088450726380954 + }, + "tag": "P0004", + "travel_mode": 1, + "travel_duration_multiple": 1.0, + "delivery_load_limits": { + "shipment_count": 20 + }, + "max_round_duration": null, + "arrival_duration": null, + "departure_duration": null, + "reload_duration": null, + "arrival_cost": 0.0, + "departure_cost": 0.0, + "reload_cost": 0.0 + }, + { + "coordinates": { + "latitude": 48.8751776812848, + 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