diff --git a/modelling/scheduler_median.py b/modelling/scheduler_median.py deleted file mode 100644 index a1d7c24..0000000 --- a/modelling/scheduler_median.py +++ /dev/null @@ -1,241 +0,0 @@ -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)