I have asked about a year ago about a dispatch scheduler I've written using Gekko. Over the previous months I have modified the original code several times to fit what I want and needed adding new constraints and more variables. The code arrives at a solution that is acceptable. However, the run time takes about 10-12 mins, sometimes more. I would like to ask if there is any part in my code that I can change to significantly reduce run time.
I have tried simplifying everything but I still cannot get it to arrive at a solution faster. I understand it could be that the problem is too large and that the long run time is expected. However, as I am not a Gekko expert and this is the first problem I've really used this on, there might be workarounds that I am not aware of. I have tried using sum instead of m.sum as Ive read it as a solution in another question, but it didnt really reduce the runtime by much. I have posted the code below.
from gekko import GEKKO
import numpy as np
import pandas as pd
import itertools
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
m = GEKKO(remote=False) # Initialize gekko
#Sample Demand Without RE
# grid_load = [94.17, 83.35, 79.16, 78.43, 91.74, 75.26, 93.83, 70.09, 81.09, 85.11, 99.24, 97.26, 94.66, 97.86, 96.67, 95.06, 82.66, 84.26, 110.97, 104.45, 102.45, 108.59, 97.7, 90.86]
# grid_load = [34.17, 32.35, 31.16, 30.43, 29.74, 30.26, 31.83, 34.09, 35.09, 36.11, 37.24, 38.26, 37.66, 36.86, 37.67, 37.06, 36.66, 38.26, 45.97, 46.45, 45.45, 43.59, 38.7, 35.86]
grid_load = [57.34, 54.16, 48.35, 45.55, 44.04, 44.33, 45.18, 45.43, 52.61, 56.56, 60.64, 63.12, 67.55, 68.33, 69.09, 68.58, 67.15, 63.55, 63.14, 71.09, 71.67, 68.24, 66.14, 60.45,]
renewables = [
[6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3.7, 3.7, 3.7, 3.7], #HYDRO1
[5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7], #HYDRO2
[10, 9, 8, 7, 6.5, 6.5, 7.5, 9, 9, 9, 9.5, 9.5, 9.5, 9.5, 9.5, 9, 9, 9.5, 12, 12, 12, 12, 12, 10.5] #WIND
]
demand = grid_load - np.sum(renewables,axis=0)
# print(demand)
n_units_per_plant = [4,5,5,5,7,4,5,4,4,5,4,7,6]
num_plants = len(n_units_per_plant)
pmax = [3.75,3.75,3.75,3.75, #DMCI 4 units
1.33,1.33,1.33,1.33,1.33, #ORMIN 5 units
2.8,3.5,1.8,1.8,1.8, #PONE 5 units
2,1.4,1.4,1.4,1.4, #MHEC 5 units
0.7,1,0.9,1,1,0.35,0.8, #TTR 7 units
1,1,1,1, #MSS 4 units
0.8,0.8,0.8,0.8,1.1, #PSI69 5 units
0.8,0.8,0.8,0.5, #PSI138 4 units
1,1,1,1, #PP 4 units
0.9,0.9,0.9,0.9,0.9, #UPRC 5 units
0.85,0.85,0.85,0.85, #UPRV 4 units
0.35,0.35,0.6,0.4,0.85,0.85,0.85, #TCGR 7 units
0.85,0.85,0.85,0.85,0.75,1, #TTC 6 units
]
pmin = [2.5,2.5,2.5,2.5, #DMCI 4 units (15 MW)
1.33,1.33,1.33,1.33,1.33, #ORMIN 5 units (9 MW)
2.8,3.5,1.8,1.8,1.8, #PONE 5 units
2,1.4,1.4,1.4,1.4, #MHEC 5 units
0.7,1,0.9,1,1,0.35,0.8, #TTR 7 units
1,1,1,1, #MSS 4 units
0.8,0.8,0.8,0.8,1.1, #PSI69 5 units
0.8,0.8,0.8,0.5, #PSI138 4 units
1,1,1,1, #PP 4 units
0.9,0.9,0.9,0.9,0.9, #UPRC 5 units
0.85,0.85,0.85,0.85, #UPRV 4 units
0.35,0.35,0.6,0.4,0.85,0.85,0.85, #TCGR 7 units
0.85,0.85,0.85,0.85,0.75,1, #TTC 6 units
]
v_cost = [14.52,14.52,14.52,14.52,
16.545,16.545,16.545,16.545,16.545,
18.000178,18.000178,18.000178,18.000178,18.000178,
17.953,17.953,17.953,17.953,17.953,
18.531,18.531,18.531,18.531,18.531,18.531,18.531, #TTR
18.182,18.182,18.182,18.182, #MSS
18.12,18.12,18.12,18.12,18.12, #PSI69
18.12,18.12,18.12,18.12, #PSI138
18.23,18.23,18.23,18.23,
22.23,22.23,22.23,22.23,22.23, #UPRC
22.23,22.23,22.23,22.23, #UPRV
18.531,18.531,18.531,18.531,18.531,18.531,18.531, #TCGR
18.531,18.531,18.531,18.531,18.531,18.531, #TTC
]
startup_cost = [200] * len(pmax)
meots = [180,160,221,0,59.5,59.5,59.5,72,51,59.5,59.5,76.5,43.35]
min_ol = [2,1,1,1,0,0,0,0,0,0,0,0,0]
availability = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #DMCI
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #OPI
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], #PONE
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #MHEC
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #TTR
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #MSS
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #PSI69
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #PSI138
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #PP
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #UPRC
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #UPRV
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #TCGR
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], #TTC
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
]
availability = np.array(availability).T
# print(availability.T)
n_plants = len(pmax)
hours_tot = len(demand)
unit_indexes = []
start_index = 0
start_index = 0
for num_units in n_units_per_plant:
end_index = start_index + num_units
unit_indexes.append([start_index, end_index])
start_index = end_index
unit_indexes = np.array(list(map(list, zip(*unit_indexes)))) # Transpose the list
# print(unit_indexes[0,1])
print('Total Dependable Demand', sum(pmax))
def solver(slice,demandido,meoty):
hours = len(demandido)
print(meoty)
m.options.SOLVER=1 # APOPT is an MINLP solver
m.options.IMODE= 3
# Initialize Variables
prod = m.Array(m.Var,(hours,n_plants),lb=0, integer=False)
switch_on = m.Array(m.Var,(hours,n_plants),lb=0,ub=1,integer=True)
plant_stat = m.Array(m.Var,(hours,n_plants),lb=0,ub=1,integer=True)
# meot_stat = m.Array(m.Var,(1,num_plants),lb=0,ub=1,integer=True)
for i in range(hours):
#DEMAND CONSTRAINT
# print('DEMAND:',demandido[i])
m.Equation(m.sum(prod[i,:]) >= demandido[i])
#RESERVE CONSTRAINT: Current Dependable Capacity Requires a 2MW reserve on the Regulating Plant
m.Equation(m.sum(np.multiply(pmax[0:4]-prod[i,0:4],plant_stat[i,0:4])) >= 2)
#MINIMUM ONLINE UNITS CONSTRAINT: ORMIN AND POWER ONE
for g in range(num_plants):
if(min_ol[g]!=0):
m.Equation(m.sum(np.multiply(plant_stat[i,unit_indexes[0,g]:unit_indexes[1,g]],availability[i,unit_indexes[0,g]:unit_indexes[1,g]])) >= min_ol[g])
for j in range(n_plants):
#CHECK IF THE UNITS ARE AVAILABLE. IF A AVAILABLE SET THE CONSTRAINTS. ELSE MAKE IT ZERO
m.Equation(prod[i,j] <= pmax[j]*availability[i,j]*plant_stat[i,j]*1.02) #2% tolerance
m.Equation(prod[i,j] >= pmin[j]*availability[i,j]*plant_stat[i,j]*0.98) #2% tolerance
#Plant State constraint
q = m.if2(prod[i,j],0,1)
m.Equation(plant_stat[i,j] == q)
#SWITCHING CONSTRAINT
if i == 0:
m.Equation(switch_on[i,j] == plant_stat[i,j])
else:
m.Equation(switch_on[i,j] >= plant_stat[i,j] - plant_stat[i-1,j])
m.Equation(switch_on[i,j] <= 1 - plant_stat[i-1,j])
for g in range(num_plants):
if(min_ol[g]!=0):
m.Equation(m.sum(np.multiply(plant_stat[i,unit_indexes[0,g]:unit_indexes[1,g]],availability[i,unit_indexes[0,g]:unit_indexes[1,g]])) >= min_ol[g])
if(meots[g]>0):
start = unit_indexes[0,g]
end = unit_indexes[1,g]
#DAILY MEOT CONSTRAINT
if g == 2: #PONE
pia = (np.sum(prod[0:hours, unit_indexes[0, g]:unit_indexes[1, g]]) + np.sum(prod[0:hours, unit_indexes[0, g+1]:unit_indexes[1, g+1]]))
rig = sum(sum(np.multiply(availability[0:hours,unit_indexes[0,g]:unit_indexes[1,g]], pmax[unit_indexes[0,g]:unit_indexes[1,g]]))) +\
sum(sum(np.multiply(availability[0:hours,unit_indexes[0,g+1]:unit_indexes[1,g+1]], pmax[unit_indexes[0,g+1]:unit_indexes[1,g+1]])))
else:
pia = np.sum(prod[0:hours,start:end]) #24 hours production
rig = sum(sum(np.multiply(availability[0:hours,start:end], pmax[start:end]))) #maximum allowable dispatch as per unit pmax and availability
mik = meoty[g]/(hours_tot/hours) #plant meot per demand slice
# print("START, END, RIG, MIK:", start, end, rig, mik)
#Sanity Checker for MEOT
#Check if its possible to reach meot constraint. Waive if not possible
if rig >= mik:
m.Equation(pia >= mik)
else:
m.Equation(pia >= rig)
#OBJECTIVE FUNCTION
abc = [np.sum(prod[:, unit_indexes[0,g]:unit_indexes[1,g]]) for g in range(num_plants)]
meot_stat = [m.if2(meots[i]-abc[i], 0, 1) for i in range(num_plants)] #if prod of plant is greater than their meot its zero
# start_up_cost = m.Intermediate(m.sum([switch_on[t,i]*0.1*v_cost[i]*pmax[i] for i in range(n_plants) for t in range(hours)])) #starup cost is 10% of the fullload cost
start_up_cost = m.Intermediate(m.sum([switch_on[t,i]*startup_cost[i] for i in range(n_plants) for t in range(hours)])) #starup cost is 10% of the fullload cost
prod_cost = m.Intermediate(m.sum([prod[t,i]*v_cost[i] for i in range(n_plants) for t in range(hours)]))
meot_cost = m.Intermediate(m.sum([(meots[i] - abc[i])*meot_stat[i]*v_cost[unit_indexes[0,i]] for i in range(num_plants)]))
# reserve - m.sum(reserve)
total_cost = start_up_cost +\
prod_cost
m.Minimize(total_cost)
m.Minimize(meot_cost)
m.solve(disp=True) # Solve
print("Total Cost", m.options.OBJFCNVAL-start_up_cost.value[0])
# meot_cost_value = meot_cost.value[0]
#OUTPUTS
prody = np.round(np.array([prod[i][j].value[0] for j in range(n_plants) for i in range(hours)]).reshape(n_plants,hours,),4)
plant_stat = np.array([plant_stat[i][j].value[0] for j in range(n_plants) for i in range(hours)]).reshape(n_plants,hours,)
switch_on = np.array([switch_on[i][j].value[0] for j in range(n_plants) for i in range(hours)]).reshape(n_plants,hours,)
return(plant_stat,switch_on,prody)
slice = len(demand)
# # print(hours_tot)
reshaped_prody_list = []
updated_meots = meots
x, y, prody = solver(slice, demand, updated_meots)
# # Convert dispatch_array to a DataFrame
dispatch_schedule = pd.DataFrame(prody)
A couple simple methods to potentially improve solve time is to:
m.options.REDUCE = 3
use a prior solution as a warm-start for the solver. This happens automatically with successive calls to m.solve().
enable diagnostics to look for ways to improve the solve time. Here is information about the problem size from the solver output:
Objects : 1576
Constants : 0
Variables : 19186
Intermediates: 3
Connections : 6282
Equations : 14560
Residuals : 14557
Setting m.options.DIAGLEVEL=1 also gives additional timing information.
Timer # 1 260.49/ 1 = 260.49 Total system time
Timer # 2 152.00/ 1 = 152.00 Total solve time
Timer # 3 0.28/ 96 = 0.00 Objective Calc: apm_p
Timer # 4 0.21/ 53 = 0.00 Objective Grad: apm_g
Timer # 5 0.31/ 96 = 0.00 Constraint Calc: apm_c
Timer # 6 0.00/ 0 = 0.00 Sparsity: apm_s
Timer # 7 0.00/ 0 = 0.00 1st Deriv #1: apm_a1
Timer # 8 0.09/ 53 = 0.00 1st Deriv #2: apm_a2
Timer # 9 60.35/ 1 = 60.35 Custom Init: apm_custom_init
Timer # 10 0.02/ 1 = 0.02 Mode: apm_node_res::case 0
Timer # 11 0.00/ 1 = 0.00 Mode: apm_node_res::case 1
Timer # 12 0.16/ 1 = 0.16 Mode: apm_node_res::case 2
Timer # 13 0.00/ 1 = 0.00 Mode: apm_node_res::case 3
Timer # 14 1.54/ 197 = 0.01 Mode: apm_node_res::case 4
Timer # 15 8.78/ 106 = 0.08 Mode: apm_node_res::case 5
Timer # 16 0.00/ 0 = 0.00 Mode: apm_node_res::case 6
Timer # 17 0.18/ 53 = 0.00 Base 1st Deriv: apm_jacobian
Timer # 18 0.03/ 53 = 0.00 Base 1st Deriv: apm_cond_jacobian
Timer # 19 0.00/ 1 = 0.00 Non-zeros: apm_nnz
Timer # 20 0.00/ 0 = 0.00 Count: Division by zero
Timer # 21 0.00/ 0 = 0.00 Count: Argument of LOG10 negative
Timer # 22 0.00/ 0 = 0.00 Count: Argument of LOG negative
Timer # 23 0.00/ 0 = 0.00 Count: Argument of SQRT negative
Timer # 24 0.00/ 0 = 0.00 Count: Argument of ASIN illegal
Timer # 25 0.00/ 0 = 0.00 Count: Argument of ACOS illegal
Timer # 26 0.00/ 1 = 0.00 Extract sparsity: apm_sparsity
Timer # 27 0.00/ 13 = 0.00 Variable ordering: apm_var_order
Timer # 28 0.00/ 1 = 0.00 Condensed sparsity
Timer # 29 0.00/ 0 = 0.00 Hessian Non-zeros
Timer # 30 0.00/ 1 = 0.00 Differentials
Timer # 31 0.00/ 0 = 0.00 Hessian Calculation
Timer # 32 0.00/ 0 = 0.00 Extract Hessian
Timer # 33 0.01/ 1 = 0.01 Base 1st Deriv: apm_jac_order
Timer # 34 0.05/ 1 = 0.05 Solver Setup
Timer # 35 140.34/ 1 = 140.34 Solver Solution
Timer # 36 0.13/ 107 = 0.00 Number of Variables
Timer # 37 0.09/ 59 = 0.00 Number of Equations
Timer # 38 0.18/ 14 = 0.01 File Read/Write
Timer # 39 0.00/ 0 = 0.00 Dynamic Init A
Timer # 40 0.00/ 0 = 0.00 Dynamic Init B
Timer # 41 0.00/ 0 = 0.00 Dynamic Init C
Timer # 42 7.18/ 1 = 7.18 Init: Read APM File
Timer # 43 0.00/ 1 = 0.00 Init: Parse Constants
Timer # 44 6.27/ 1 = 6.27 Init: Model Sizing
Timer # 45 0.00/ 1 = 0.00 Init: Allocate Memory
Timer # 46 5.19/ 1 = 5.19 Init: Parse Model
Timer # 47 1.16/ 1 = 1.16 Init: Check for Duplicates
Timer # 48 15.60/ 1 = 15.60 Init: Compile Equations
Timer # 49 0.00/ 1 = 0.00 Init: Check Uninitialized
Timer # 50 0.17/ 26628 = 0.00 Evaluate Expression Once
From your problem, I found an issue with Timer #47 that was taking too long on the Check for Duplicates task. I switched to a more efficient algorithm and lowered the time from 69.8 sec to 1.16 sec as part of the model initialization. The total time is now 261.2 sec with 120.9 sec from model compilation and 140.34 sec from the solver. The updated executable will be available with the next Gekko release (v1.1.2) with APMonitor executable (v1.0.3).
IMODE=6 for time-based problems. This compiles a version of the model once and distributes it for each time point in the horizon. This would require a major rewrite of your model, so I don't recommend that.