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7a3d380 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | """OR-Tools routing model for daily claim assignment.
Model summary
-------------
- Each adjuster is a "vehicle" that starts and ends its route at their home.
- Objective: minimize total travel minutes + penalties for dropped claims.
- Time dimension tracks the clock (minutes since midnight). The transit
between two stops = service time at the first stop + drive time. Slack in
the dimension lets an adjuster wait for a policyholder's window to open.
- Constraints:
* policyholder availability window on each claim's arrival time
* adjuster shift window on each route's start and end
* skill matching via SetAllowedVehiclesForIndex
* every claim is in a disjunction, so it can be dropped (rescheduled to
a later day) at a priority-scaled penalty; MUST-TODAY claims carry a
penalty so large they are only dropped when physically infeasible.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from ortools.constraint_solver import pywrapcp, routing_enums_pb2
import config
import data_gen
from data_gen import Adjuster, Claim
@dataclass
class Stop:
claim: Claim
arrival_min: int # service starts on arrival (within the window)
departure_min: int
travel_miles_from_prev: float
travel_min_from_prev: int
@dataclass
class Route:
adjuster: Adjuster
stops: list[Stop] = field(default_factory=list)
start_min: int = 0
end_min: int = 0
total_miles: float = 0.0
total_travel_min: int = 0
total_service_min: int = 0
@dataclass
class Solution:
routes: list[Route]
dropped: list[Claim]
unservable: list[Claim] # no adjuster has the required skill at all
objective: int
total_miles: float = 0.0
total_travel_min: int = 0
@property
def dropped_must_today(self) -> list[Claim]:
return [c for c in self.dropped
if c.priority == config.PRIORITY_MUST_TODAY]
def solve(adjusters: list[Adjuster], claims: list[Claim],
miles: list[list[float]], travel_min: list[list[int]],
time_limit_s: int = config.SOLVER_TIME_LIMIT_SECONDS,
lunch_break: bool = False, balance: bool = False,
) -> Solution | None:
n_adj = len(adjusters)
n_nodes = n_adj + len(claims)
# Service time by node (0 at adjuster homes).
service_min = [0] * n_adj + [c.service_minutes for c in claims]
starts = list(range(n_adj))
ends = list(range(n_adj))
manager = pywrapcp.RoutingIndexManager(n_nodes, n_adj, starts, ends)
routing = pywrapcp.RoutingModel(manager)
# --- Objective: pure travel time on arcs (service time is a constant
# for every served claim, so keeping it out of the cost makes the
# objective easier to interpret).
def travel_cb(from_index: int, to_index: int) -> int:
f = manager.IndexToNode(from_index)
t = manager.IndexToNode(to_index)
return travel_min[f][t]
travel_cb_idx = routing.RegisterTransitCallback(travel_cb)
routing.SetArcCostEvaluatorOfAllVehicles(travel_cb_idx)
# --- Time dimension: clock advances by service-at-origin + drive time.
def time_cb(from_index: int, to_index: int) -> int:
f = manager.IndexToNode(from_index)
t = manager.IndexToNode(to_index)
return service_min[f] + travel_min[f][t]
time_cb_idx = routing.RegisterTransitCallback(time_cb)
routing.AddDimension(
time_cb_idx,
config.MAX_WAIT_MINUTES, # slack: allowed waiting at a stop
config.TIME_HORIZON_MINUTES, # dimension upper bound
False, # don't force start cumul to zero
"Time",
)
time_dim = routing.GetDimensionOrDie("Time")
# Policyholder availability: arrival (= service start) inside the window.
# The window is tightened so the visit also *finishes* by window end.
for i, claim in enumerate(claims):
idx = manager.NodeToIndex(n_adj + i)
ranges = data_gen.arrival_ranges(claim)
cumul = time_dim.CumulVar(idx)
cumul.SetRange(ranges[0][0], ranges[-1][1])
for (_, hi), (lo2, _) in zip(ranges, ranges[1:]):
cumul.RemoveInterval(hi + 1, lo2 - 1)
# Adjuster shifts bound each route's start and end times.
for v, adj in enumerate(adjusters):
time_dim.CumulVar(routing.Start(v)).SetRange(adj.shift_start, adj.shift_end)
time_dim.CumulVar(routing.End(v)).SetRange(adj.shift_start, adj.shift_end)
# Optional 30-min lunch break inside a configurable window.
if lunch_break:
cp = routing.solver()
visit_transits = [service_min[manager.IndexToNode(i)]
for i in range(routing.Size())]
for v in range(n_adj):
interval = cp.FixedDurationIntervalVar(
config.LUNCH_BREAK["earliest_start"],
config.LUNCH_BREAK["latest_start"],
config.LUNCH_BREAK["duration"], False, f"lunch_{v}")
time_dim.SetBreakIntervalsOfVehicle([interval], v,
visit_transits)
# Optional workload balancing: charge each minute of route span so
# stops spread across adjusters instead of piling onto one.
if balance:
time_dim.SetSpanCostCoefficientForAllVehicles(
config.BALANCE_COEFFICIENT)
# Prefer schedules that start late / end early when travel cost ties.
for v in range(n_adj):
routing.AddVariableMinimizedByFinalizer(time_dim.CumulVar(routing.End(v)))
routing.AddVariableMaximizedByFinalizer(time_dim.CumulVar(routing.Start(v)))
# --- Skill matching + droppable claims.
# Skill matching constrains each claim's VehicleVar to qualified
# adjusters; -1 stays in the domain so the claim can still be dropped
# (the disjunction below decides whether that's worth the penalty).
unservable: list[Claim] = []
for i, claim in enumerate(claims):
idx = manager.NodeToIndex(n_adj + i)
allowed = [v for v, adj in enumerate(adjusters)
if config.is_eligible(adj, claim, miles[v][n_adj + i])]
if not allowed:
unservable.append(claim)
routing.VehicleVar(idx).SetValues([-1] + allowed)
routing.AddDisjunction(
[idx], config.effective_penalty(claim.priority, claim.age_days))
# --- Search strategy.
params = pywrapcp.DefaultRoutingSearchParameters()
params.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
params.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
params.time_limit.FromSeconds(time_limit_s)
assignment = routing.SolveWithParameters(params)
if assignment is None:
return None
# --- Extract solution.
routes: list[Route] = []
served_nodes: set[int] = set()
for v, adj in enumerate(adjusters):
route = Route(adjuster=adj)
index = routing.Start(v)
route.start_min = assignment.Value(time_dim.CumulVar(index))
prev_node = manager.IndexToNode(index)
while not routing.IsEnd(index):
index = assignment.Value(routing.NextVar(index))
node = manager.IndexToNode(index)
leg_miles = miles[prev_node][node]
leg_min = travel_min[prev_node][node]
if routing.IsEnd(index):
route.end_min = assignment.Value(time_dim.CumulVar(index))
route.total_miles += leg_miles
route.total_travel_min += leg_min
break
claim = claims[node - n_adj]
arrival = assignment.Value(time_dim.CumulVar(index))
route.stops.append(Stop(
claim=claim,
arrival_min=arrival,
departure_min=arrival + claim.service_minutes,
travel_miles_from_prev=leg_miles,
travel_min_from_prev=leg_min,
))
route.total_miles += leg_miles
route.total_travel_min += leg_min
route.total_service_min += claim.service_minutes
served_nodes.add(node)
prev_node = node
routes.append(route)
dropped = [c for i, c in enumerate(claims) if (n_adj + i) not in served_nodes]
sol = Solution(
routes=routes,
dropped=dropped,
unservable=unservable,
objective=assignment.ObjectiveValue(),
total_miles=sum(r.total_miles for r in routes),
total_travel_min=sum(r.total_travel_min for r in routes),
)
return sol
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