import pandas as pd 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 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 _get_ordinal_case_deadlines(self): """ #TODO Returns: """ self.df_cases.loc[:, "TargetDeadline"] = pd.to_datetime(self.df_cases["TargetDeadline"], format="%d/%m/%Y") self.df_cases.loc[:, "TargetDeadline"] = self.df_cases["TargetDeadline"].apply(lambda date: date.toordinal()) return pd.Series(self.df_cases["TargetDeadline"].values, index=self.df_cases["CaseID"]).to_dict() def _get_ordinal_session_dates(self): """ #TODO Returns: """ self.df_sessions.loc[:, "Date"] = pd.to_datetime(self.df_sessions["Date"], format="%d/%m/%Y") self.df_sessions.loc[:, "Date"] = self.df_sessions["Date"].apply(lambda date: date.toordinal()) return pd.Series(self.df_sessions["Date"].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 # List of case IDs in surgical waiting list model.CASES = pe.Set(initialize=self.df_cases["CaseID"].tolist()) # List of sessions IDs model.SESSIONS = pe.Set(initialize=self.df_sessions["SessionID"].tolist()) # List of tasks - all possible (caseID, sessionID) combination model.TASKS = pe.Set(initialize=model.CASES * model.SESSIONS, dimen=2) # The duration (median case time) for each operation model.CASE_DURATION = pe.Param(model.CASES, initialize=self._generate_case_durations()) # The duration of each theatre session model.SESSION_DURATION = pe.Param(model.SESSIONS, initialize=self._generate_session_durations()) # The start time of each theatre session model.SESSION_START_TIME = pe.Param(model.SESSIONS, initialize=self._generate_session_start_times()) # The deadline of each case model.CASE_DEADLINES = pe.Param(model.CASES, initialize=self._get_ordinal_case_deadlines()) # The date of each theatre session model.SESSION_DATES = pe.Param(model.SESSIONS, initialize=self._get_ordinal_session_dates()) 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_cases = self.df_cases.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.CASES_IN_SESSION = pe.Var(model.SESSIONS, bounds=(0, num_cases), within=pe.PositiveReals) # Objective def objective_function(model): return pe.summation(model.CASES_IN_SESSION) #return sum([model.SESSION_ASSIGNED[case, session] for case in model.CASES for session in model.SESSIONS]) model.OBJECTIVE = pe.Objective(rule=objective_function, sense=pe.maximize) # Constraints # 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 set_deadline_condition(model, case, session): return model.SESSION_DATES[session] <= model.CASE_DEADLINES[case] + ((1 - model.SESSION_ASSIGNED[case, session])*model.M) model.APPLY_DEADLINE = pe.Constraint(model.TASKS, rule=set_deadline_condition) 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.CASES_IN_SESSION[session] == \ sum([model.SESSION_ASSIGNED[case, session] for case in model.CASES]) model.THEATRE_UTIL = pe.Constraint(model.SESSIONS, rule=theatre_util) pe.TransformationFactory("gdp.bigm").apply_to(model) return model def solve(self, solver_name, options=None, solver_path=None, local=True): 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 if local: solver_results = solver.solve(self.model, tee=True) else: solver_manager = pe.SolverManagerFactory("neos") solver_results = solver_manager.solve(self.model, opt=solver) results = [{"Case": case, "Session": session, "Session Date": self.model.SESSION_DATES[session], "Case Deadline": self.model.CASE_DEADLINES[case], "Days before deadline": self.model.CASE_DEADLINES[case] - self.model.SESSION_DATES[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 = [] 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.CASES_IN_SESSION.pprint() print("Total Objective = {}".format(sum(self.model.CASES_IN_SESSION.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[self.df_times["Assignment"] == 1].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('Assigning Ophthalmology Cases to Theatre Sessions') 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", "cases.csv") session_path = os.path.join(os.path.dirname(os.getcwd()), "data", "sessions.csv") cbc_path = "C:\\Users\\LONLW15\\Documents\\Linear Programming\\Solvers\\cbc.exe" options = {"seconds": 300} scheduler = TheatreScheduler(case_file_path=case_path, session_file_path=session_path) scheduler.solve(solver_name="cbc", solver_path=cbc_path, options=options) #scheduler.solve(solver_name="cbc", local=False, options=None)