242 lines
11 KiB
Python
242 lines
11 KiB
Python
|
import pandas as pd
|
||
|
|
import numpy as np
|
||
|
|
import pyomo.environ as pe
|
||
|
|
import pyomo.gdp as pyogdp
|
||
|
|
import os
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import matplotlib.cm as cm
|
||
|
|
from itertools import product
|
||
|
|
|
||
|
|
|
||
|
|
# check with Taha if code is too similar to Alstom?
|
||
|
|
class TheatreScheduler:
|
||
|
|
|
||
|
|
def __init__(self, case_file_path, session_file_path):
|
||
|
|
"""
|
||
|
|
Read case and session data into Pandas DataFrames
|
||
|
|
Args:
|
||
|
|
case_file_path (str): path to case data in CSV format
|
||
|
|
session_file_path (str): path to theatre session data in CSV format
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
self.df_cases = pd.read_csv(case_file_path)
|
||
|
|
except FileNotFoundError:
|
||
|
|
print("Case data not found.")
|
||
|
|
|
||
|
|
try:
|
||
|
|
self.df_sessions = pd.read_csv(session_file_path)
|
||
|
|
except FileNotFoundError:
|
||
|
|
print("Session data not found")
|
||
|
|
|
||
|
|
self.model = self.create_model()
|
||
|
|
|
||
|
|
def _generate_case_durations(self):
|
||
|
|
"""
|
||
|
|
Generate mapping of cases IDs to median case time for the procedure
|
||
|
|
Returns:
|
||
|
|
(dict): dictionary with CaseID as key and median case time (mins) for procedure as value
|
||
|
|
"""
|
||
|
|
return pd.Series(self.df_cases["Median Duration"].values, index=self.df_cases["CaseID"]).to_dict()
|
||
|
|
|
||
|
|
def _generate_session_durations(self):
|
||
|
|
"""
|
||
|
|
Generate mapping of all theatre sessions IDs to session duration in minutes
|
||
|
|
Returns:
|
||
|
|
(dict): dictionary with SessionID as key and session duration as value
|
||
|
|
"""
|
||
|
|
return pd.Series(self.df_sessions["Duration"].values, index=self.df_sessions["SessionID"]).to_dict()
|
||
|
|
|
||
|
|
def _generate_session_start_times(self):
|
||
|
|
"""
|
||
|
|
Generate mapping from SessionID to session start time
|
||
|
|
Returns:
|
||
|
|
(dict): dictionary with SessionID as key and start time in minutes since midnight as value
|
||
|
|
"""
|
||
|
|
# Convert session start time from HH:MM:SS format into seconds elapsed since midnight
|
||
|
|
self.df_sessions.loc[:, "Start"] = pd.to_timedelta(self.df_sessions["Start"])
|
||
|
|
self.df_sessions.loc[:, "Start"] = self.df_sessions["Start"].dt.total_seconds() / 60
|
||
|
|
return pd.Series(self.df_sessions["Start"].values, index=self.df_sessions["SessionID"]).to_dict()
|
||
|
|
|
||
|
|
def _generate_disjunctions(self):
|
||
|
|
"""
|
||
|
|
#TODO
|
||
|
|
Returns:
|
||
|
|
disjunctions (list): list of tuples containing disjunctions
|
||
|
|
"""
|
||
|
|
cases = self.df_cases["CaseID"].to_list()
|
||
|
|
sessions = self.df_sessions["SessionID"].to_list()
|
||
|
|
disjunctions = []
|
||
|
|
for (case1, case2, session) in product(cases, cases, sessions):
|
||
|
|
if (case1 != case2) and (case2, case1, session) not in disjunctions:
|
||
|
|
disjunctions.append((case1, case2, session))
|
||
|
|
|
||
|
|
return disjunctions
|
||
|
|
|
||
|
|
def create_model(self):
|
||
|
|
model = pe.ConcreteModel()
|
||
|
|
|
||
|
|
# Model Data
|
||
|
|
model.CASES = pe.Set(initialize=self.df_cases["CaseID"].tolist())
|
||
|
|
model.SESSIONS = pe.Set(initialize=self.df_sessions["SessionID"].tolist())
|
||
|
|
model.TASKS = pe.Set(initialize=model.CASES * model.SESSIONS, dimen=2)
|
||
|
|
model.CASE_DURATION = pe.Param(model.CASES, initialize=self._generate_case_durations())
|
||
|
|
model.SESSION_DURATION = pe.Param(model.SESSIONS, initialize=self._generate_session_durations())
|
||
|
|
model.SESSION_START_TIME = pe.Param(model.SESSIONS, initialize=self._generate_session_start_times())
|
||
|
|
model.DISJUNCTIONS = pe.Set(initialize=self._generate_disjunctions(), dimen=3)
|
||
|
|
|
||
|
|
ub = 1440 # seconds in a day
|
||
|
|
model.M = pe.Param(initialize=1e3*ub) # big M
|
||
|
|
max_util = 0.85
|
||
|
|
num_sessions = self.df_sessions.shape[0]
|
||
|
|
|
||
|
|
# Decision Variables
|
||
|
|
model.SESSION_ASSIGNED = pe.Var(model.TASKS, domain=pe.Binary)
|
||
|
|
model.CASE_START_TIME = pe.Var(model.TASKS, bounds=(0, ub), within=pe.PositiveReals)
|
||
|
|
model.UTILISATION = pe.Var(model.SESSIONS, bounds=(0, 1), within=pe.PositiveReals)
|
||
|
|
model.MEDIAN_UTIL = pe.Var(bounds=(0, ub), within=pe.PositiveReals)
|
||
|
|
model.DUMMY_BINARY = pe.Var(model.SESSIONS, domain=pe.Binary)
|
||
|
|
model.CANCEL_SESSION = pe.Var(model.SESSIONS, domain=pe.Binary, within=pe.PositiveReals)
|
||
|
|
|
||
|
|
# Objective
|
||
|
|
def objective_function(model):
|
||
|
|
#return pe.summation(model.UTILISATION)
|
||
|
|
return model.MEDIAN_UTIL
|
||
|
|
model.OBJECTIVE = pe.Objective(rule=objective_function, sense=pe.maximize)
|
||
|
|
|
||
|
|
# Constraints
|
||
|
|
|
||
|
|
# TODO add constraint to complete before deadline if it is assigned
|
||
|
|
# TODO add constraint to make tasks follow each other without gaps?
|
||
|
|
|
||
|
|
# Case start time must be after start time of assigned theatre session
|
||
|
|
def case_start_time(model, case, session):
|
||
|
|
return model.CASE_START_TIME[case, session] >= model.SESSION_START_TIME[session] - \
|
||
|
|
((1 - model.SESSION_ASSIGNED[(case, session)])*model.M)
|
||
|
|
model.CASE_START = pe.Constraint(model.TASKS, rule=case_start_time)
|
||
|
|
|
||
|
|
# Case end time must be before end time of assigned theatre session
|
||
|
|
def case_end_time(model, case, session):
|
||
|
|
return model.CASE_START_TIME[case, session] + model.CASE_DURATION[case] <= model.SESSION_START_TIME[session] + \
|
||
|
|
model.SESSION_DURATION[session]*max_util + ((1 - model.SESSION_ASSIGNED[(case, session)]) * model.M)
|
||
|
|
model.CASE_END_TIME = pe.Constraint(model.TASKS, rule=case_end_time)
|
||
|
|
|
||
|
|
# Cases can be assigned to a maximum of one session
|
||
|
|
def session_assignment(model, case):
|
||
|
|
return sum([model.SESSION_ASSIGNED[(case, session)] for session in model.SESSIONS]) <= 1
|
||
|
|
model.SESSION_ASSIGNMENT = pe.Constraint(model.CASES, rule=session_assignment)
|
||
|
|
|
||
|
|
def no_case_overlap(model, case1, case2, session):
|
||
|
|
return [model.CASE_START_TIME[case1, session] + model.CASE_DURATION[case1] <= model.CASE_START_TIME[case2, session] + \
|
||
|
|
((2 - model.SESSION_ASSIGNED[case1, session] - model.SESSION_ASSIGNED[case2, session])*model.M),
|
||
|
|
model.CASE_START_TIME[case2, session] + model.CASE_DURATION[case2] <= model.CASE_START_TIME[case1, session] + \
|
||
|
|
((2 - model.SESSION_ASSIGNED[case1, session] - model.SESSION_ASSIGNED[case2, session])*model.M)]
|
||
|
|
|
||
|
|
model.DISJUNCTIONS_RULE = pyogdp.Disjunction(model.DISJUNCTIONS, rule=no_case_overlap)
|
||
|
|
|
||
|
|
def theatre_util(model, session):
|
||
|
|
return model.UTILISATION[session] == (1 / model.SESSION_DURATION[session]) * \
|
||
|
|
sum([model.SESSION_ASSIGNED[case, session]*model.CASE_DURATION[case] for case in model.CASES])
|
||
|
|
model.THEATRE_UTIL = pe.Constraint(model.SESSIONS, rule=theatre_util)
|
||
|
|
|
||
|
|
def cancel_sessions(model, session): # TODO
|
||
|
|
return model.CANCEL_SESSION[session] <= 1 - model.M*sum([model.SESSION_ASSIGNED[case, session] for case in model.CASES])
|
||
|
|
model.SET_CANCEL_SESSIONS = pe.Constraint(model.SESSIONS, rule=cancel_sessions)
|
||
|
|
|
||
|
|
def force_cancel_sessions(model, session):
|
||
|
|
return sum([model.SESSION_ASSIGNED[case, session] for case in model.CASES]) <= 0 + model.M*(1-model.CANCEL_SESSION[session])
|
||
|
|
#model.FORCE_CANCEL_SESSIONS = pe.Constraint(model.SESSIONS, rule=force_cancel_sessions)
|
||
|
|
|
||
|
|
def set_dummy_variable(model):
|
||
|
|
return sum([model.DUMMY_BINARY[session] for session in model.SESSIONS]) == np.floor(num_sessions/2)
|
||
|
|
model.FLOOR = pe.Constraint(rule=set_dummy_variable)
|
||
|
|
|
||
|
|
def set_median_util(model, session):
|
||
|
|
return model.MEDIAN_UTIL <= model.UTILISATION[session] + model.DUMMY_BINARY[session]*model.M
|
||
|
|
model.SET_MEDIAN_UTIL = pe.Constraint(model.SESSIONS, rule=set_median_util)
|
||
|
|
|
||
|
|
pe.TransformationFactory("gdp.bigm").apply_to(model)
|
||
|
|
|
||
|
|
return model
|
||
|
|
|
||
|
|
def solve(self, solver_name, options=None, solver_path=None):
|
||
|
|
|
||
|
|
if solver_path is not None:
|
||
|
|
solver = pe.SolverFactory(solver_name, executable=solver_path)
|
||
|
|
else:
|
||
|
|
solver = pe.SolverFactory(solver_name)
|
||
|
|
|
||
|
|
# TODO remove - too similar to alstom
|
||
|
|
if options is not None:
|
||
|
|
for key, value in options.items():
|
||
|
|
solver.options[key] = value
|
||
|
|
|
||
|
|
solver_results = solver.solve(self.model, tee=True)
|
||
|
|
|
||
|
|
results = [{"Case": case,
|
||
|
|
"Session": session,
|
||
|
|
"Start": self.model.CASE_START_TIME[case, session](),
|
||
|
|
"Assignment": self.model.SESSION_ASSIGNED[case, session]()}
|
||
|
|
for (case, session) in self.model.TASKS]
|
||
|
|
|
||
|
|
self.df_times = pd.DataFrame(results)
|
||
|
|
|
||
|
|
all_cases = self.model.CASES.value_list
|
||
|
|
cases_assigned = []
|
||
|
|
cases_missed = []
|
||
|
|
for (case, session) in self.model.SESSION_ASSIGNED:
|
||
|
|
if self.model.SESSION_ASSIGNED[case, session] == 1:
|
||
|
|
cases_assigned.append(case)
|
||
|
|
|
||
|
|
cases_missed = list(set(all_cases).difference(cases_assigned))
|
||
|
|
print("Number of cases assigned = {} out of {}:".format(len(cases_assigned), len(all_cases)))
|
||
|
|
print("Cases assigned: ", cases_assigned)
|
||
|
|
print("Number of cases missed = {} out of {}:".format(len(cases_missed), len(all_cases)))
|
||
|
|
print("Cases missed: ", cases_missed)
|
||
|
|
self.model.UTILISATION.pprint()
|
||
|
|
print("Total Utilisation = {}".format(sum(self.model.UTILISATION.get_values().values())))
|
||
|
|
print("Number of constraints = {}".format(solver_results["Problem"].__getitem__(0)["Number of constraints"]))
|
||
|
|
#self.model.SESSION_ASSIGNED.pprint()
|
||
|
|
#print(self.df_times.to_string())
|
||
|
|
self.draw_gantt()
|
||
|
|
|
||
|
|
def draw_gantt(self):
|
||
|
|
|
||
|
|
df = self.df_times[self.df_times["Assignment"] == 1]
|
||
|
|
cases = sorted(list(df['Case'].unique()))
|
||
|
|
sessions = sorted(list(df['Session'].unique()))
|
||
|
|
|
||
|
|
bar_style = {'alpha': 1.0, 'lw': 25, 'solid_capstyle': 'butt'}
|
||
|
|
text_style = {'color': 'white', 'weight': 'bold', 'ha': 'center', 'va': 'center'}
|
||
|
|
colors = cm.Dark2.colors
|
||
|
|
|
||
|
|
df.sort_values(by=['Case', 'Session'])
|
||
|
|
df.set_index(['Case', 'Session'], inplace=True)
|
||
|
|
|
||
|
|
fig, ax = plt.subplots(1, 1)
|
||
|
|
for c_ix, c in enumerate(cases, 1):
|
||
|
|
for s_ix, s in enumerate(sessions, 1):
|
||
|
|
if (c, s) in df.index:
|
||
|
|
xs = df.loc[(c, s), 'Start']
|
||
|
|
xf = df.loc[(c, s), 'Start'] + \
|
||
|
|
self.df_cases[self.df_cases["CaseID"] == c]["Median Duration"]
|
||
|
|
ax.plot([xs, xf], [s] * 2, c=colors[c_ix % 7], **bar_style)
|
||
|
|
ax.text((xs + xf) / 2, s, c, **text_style)
|
||
|
|
|
||
|
|
ax.set_title('Session Schedule')
|
||
|
|
ax.set_xlabel('Time')
|
||
|
|
ax.set_ylabel('Sessions')
|
||
|
|
ax.grid(True)
|
||
|
|
|
||
|
|
fig.tight_layout()
|
||
|
|
plt.show()
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
case_path = os.path.join(os.path.dirname(os.getcwd()), "data", "case_data_long.csv")
|
||
|
|
session_path = os.path.join(os.path.dirname(os.getcwd()), "data", "session_data.csv")
|
||
|
|
cbc_path = "C:\\Users\\LONLW15\\Documents\\Linear Programming\\Solvers\\cbc.exe"
|
||
|
|
|
||
|
|
options = {"seconds": 30}
|
||
|
|
scheduler = TheatreScheduler(case_file_path=case_path, session_file_path=session_path)
|
||
|
|
scheduler.solve(solver_name="cbc", solver_path=cbc_path, options=options)
|