Variables in optimization problem using the python library Xpress

Question

My optimization problem reads an excel file into a dataframe, and is to minimize the total value of all cells (16 x 27 cells). It is 16 vessels that have the opportunity of being retrofitted and reduce emissions by 5% if retrofitted. The optimization model is to determine the timeline of the vessel fleet; if and when each vessel should be retrofitted to minimize the total fleet's emissions in 2050, with a bunch of contraints (some implemented, some not yet). I am struggling with one of the constraints part, trying to tell the problem that if a vessel have been retrofitted, it will forever have the benefits of being retrofitted, using binary variable for 'being retrofitted' and 'have been retrofitted'.

**This is my constraint section that I am struggling with: **

# Constrain the binary variables is_retrofitted and retrofitted so that they are consistent
for i in vessels:
    m.addConstraint(retrofitted[i] <= is_retrofitted[np.ravel_multi_index((i, year_retrofitted[i]), (len(vessels), len(time)))])
    m.addConstraint(xp.Sum(is_retrofitted[i, t] for t in time) == retrofitted[i])

**Below is my total code **

# Importing packages
import xpress as xp
import pandas as pd
import numpy as np

# Create path to excel file with fleet information
path = r'path_to_excel_file'
df = pd.read_excel(path, sheet_name = 'sheet_name') # Create dataframe from excel sheet
df.index = df['Vessel'] # Set index equal to the vessels

# Adjusting the dataframe
df = df.drop('Total') # Delete unnecessary row
df = df.drop('Unit') # Delete unnecessary row
df.drop('Total', axis=1, inplace=True) # Delete unnecessary column

# Define sets
V = df['Vessel'] # Set of vessels
df = df.drop('Vessel', axis = 1) # Delete unnecessary column

# Define the number of vessels and the time horizon
num_vessels = 16
time_horizon = 2050 - 2023 + 1

# Create sets for vessels and time
vessels = range(num_vessels)
time = range(time_horizon)

# Create the emissions parameter
emissions = [xp.var("emissions_{}_{}".format(i, t), lb=0) for i in vessels for t in time]

# Create indices to map each value to a specific vessel and year
index = [(i, t) for i in vessels for t in time]

# Iterate over the rows and columns of the dataframe
for (i, t), e in zip(index, emissions):
    e.lb = df.iloc[i, t + 2023 - 2023]

# Define a binary variable for each vessel, indicating whether or not it is retrofitted
retrofitted = [xp.var("retrofitted_{}".format(i), vartype=xp.binary) for i in vessels]

# Define a binary variable for each vessel and year, indicating whether or not the vessel is retrofitted in that year
is_retrofitted = [xp.var("is_retrofitted_{}_{}".format(i, t), vartype=xp.binary) for i in vessels for t in time]

# Define an integer variable for each vessel, indicating the year in which it is retrofitted
year_retrofitted = [xp.var("year_retrofitted_{}".format(i), vartype=xp.integer) for i in vessels]

# Create the objective function to minimize the total CO2 emissions of the vessel fleet
m = xp.problem("Minimize CO2 emissions")

# Add variable to problem
m.addVariable(emissions)
m.addVariable(retrofitted)
m.addVariable(is_retrofitted)
m.addVariable(year_retrofitted)

# Set objective funtion
m.setObjective(xp.Sum(emissions[np.ravel_multi_index((i, t), (len(vessels), len(time)))] * (1 - 0.05 * is_retrofitted[i])
                      for i, vessel in enumerate(vessels) for t, year in enumerate(time)), sense=xp.minimize)

# Constrain the binary variables is_retrofitted and retrofitted so that they are consistent
for i in vessels:
    m.addConstraint(retrofitted[i] <= is_retrofitted[np.ravel_multi_index((i, year_retrofitted[i]), (len(vessels), len(time)))])
    m.addConstraint(xp.Sum(is_retrofitted[i, t] for t in time) == retrofitted[i])

# Constrain the year_retrofitted variables to be between 0 (indicating no retrofit) and the time horizon
for i in vessels:
    m.addConstraint(year_retrofitted[i] >= 0)
    m.addConstraint(year_retrofitted[i] <= time_horizon)

# Solve the optimization problem
m.solve()

# Get the optimal values of the binary variables
is_retrofitted_opt = [is_retrofitted[i].getValues() for i in vessels]
retrofitted_opt = [retrofitted[i].getValues() for i in vessels]
year_retrofitted_opt = [year_retrofitted[i].getValues() for i in vessels]

# Get the optimal value of the objective function
obj_value = m.getObjVal()

# Print the results
print("The minimum total CO2 emissions of the vessel fleet is:", obj_value)
print("The optimal retrofit decisions are:", retrofitted_opt)
print("The optimal years for retrofitting are:", year_retrofitted_opt)