for i in range(NumberOfDays):
j = randrange(10) #today's random scenario
jj = randrange(10) #tomorrow's random scenario
for k in [2, 4, NumberOfTrucks]:
Trucks.append(k)
for demand_is in demands:
if demand_is == 'Unknown':
ConfigIni = [InitialNoOfBikesPerStation] * NumberOfStations
#Simulation
t1_sim = time.time()
ConfigFin, LostDemand = Simulation.Simulate(
InitialConfiguration=ConfigIni, scenario=j)
t2_sim = time.time()
UnknownSimTime.append(round(t2_sim - t1_sim, 2))
UnknownConfigIniList.append(ConfigIni)
UnknownConfigFinList.append(ConfigFin)
UnknownLostDemandList.append(LostDemand)
#Rebalancing operations
data_dict = Rebalancing.getInputData(nSt=NumberOfStations,
nTr=k,
CapTr=TruckCapacity,
desiredConfig=ConfigIni,
currentConfig=ConfigFin)
t1_reb = time.time()
UnknownCost.append(Rebalancing.Solve(data_dict, TimeLimit))
