207 lines
9.4 KiB
Python
207 lines
9.4 KiB
Python
import pandas as pd
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import pyomo.environ as pe
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import pyomo.gdp as pyogdp
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import os
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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from itertools import product
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#import numpy as np
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class TheatreScheduler:
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def __init__(self, case_file_path, session_file_path):
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"""
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Read case and session data into Pandas DataFrames
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Args:
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case_file_path (str): path to case data in CSV format
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session_file_path (str): path to theatre session data in CSV format
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"""
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try:
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self.df_cases = pd.read_csv(case_file_path)
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except FileNotFoundError:
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print("Case data not found.")
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try:
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self.df_sessions = pd.read_csv(session_file_path)
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except FileNotFoundError:
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print("Session data not found")
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self.model = self.create_model()
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def _generate_case_durations(self):
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"""
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Generate mapping of cases IDs to median case time for the procedure
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Returns:
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(dict): dictionary with CaseID as key and median case time (mins) for procedure as value
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"""
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#return pd.Series(self.df_cases["Median Duration"].values, index=self.df_cases["CaseID"]).to_dict()
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return
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def _generate_session_durations(self):
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"""
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Generate mapping of all theatre sessions IDs to session duration in minutes
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Returns:
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(dict): dictionary with SessionID as key and session duration as value
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"""
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#return pd.Series(self.df_sessions["Duration"].values, index=self.df_sessions["SessionID"]).to_dict()
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return
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def _generate_session_start_times(self):
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"""
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Generate mapping from SessionID to session start time
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Returns:
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(dict): dictionary with SessionID as key and start time in minutes since midnight as value
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"""
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# Convert session start time from HH:MM:SS format into seconds elapsed since midnight
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print("print df sessions type")
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print(self.df_sessions.loc[:, "Start"])
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print('done')
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#self.df_sessions.loc[:, "Start"] = pd.to_datetime(self.df_sessions["Start"])
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self.df_sessions.loc[:, "Start"] = pd.to_timedelta(self.df_sessions["Start"])
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print("print AFTER")
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print(self.df_sessions.loc[:, "Start"])
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print('done')
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for i in range(0,len(self.df_sessions.loc[:, "Start"])):
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print(self.df_sessions.loc[:, "Start"][i])
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print(type(self.df_sessions.loc[:, "Start"][i]))
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print(f'total sceconds = {self.df_sessions.loc[:, "Start"][i].total_seconds()}')
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self.df_sessions.loc[i, "Start"] = self.df_sessions.loc[:, "Start"][i].total_seconds()/60
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# for session in self.df_sessions.loc[:, "Start"]:
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# print(session)
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# print(session.total_seconds())
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# session = session.total_seconds()
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print('test values;')
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for session in self.df_sessions.loc[:, "Start"]:
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print(session)
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#self.df_sessions.loc[:, "Start"] = self.df_sessions["Start"].dt.total_seconds() / 60
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return pd.Series(self.df_sessions["Start"].values, index=self.df_sessions["SessionID"]).to_dict()
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def _get_ordinal_case_deadlines(self):
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"""
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#TODO
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Returns:
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"""
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self.df_cases.loc[:, "TargetDeadline"] = pd.to_datetime(self.df_cases["TargetDeadline"], format="%d/%m/%Y")
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self.df_cases.loc[:, "TargetDeadline"] = self.df_cases["TargetDeadline"].apply(lambda date: date.toordinal())
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return pd.Series(self.df_cases["TargetDeadline"].values, index=self.df_cases["CaseID"]).to_dict()
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def _get_ordinal_session_dates(self):
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"""
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#TODO
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Returns:
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"""
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self.df_sessions.loc[:, "Date"] = pd.to_datetime(self.df_sessions["Date"], format="%d/%m/%Y")
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self.df_sessions.loc[:, "Date"] = self.df_sessions["Date"].apply(lambda date: date.toordinal())
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return pd.Series(self.df_sessions["Date"].values, index=self.df_sessions["SessionID"]).to_dict()
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def create_model(self):
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model = pe.ConcreteModel()
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return model
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# # Model Data
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# # List of case IDs in surgical waiting list
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# model.CASES = pe.Set(initialize=self.df_cases["CaseID"].tolist())
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# # List of sessions IDs
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# model.SESSIONS = pe.Set(initialize=self.df_sessions["SessionID"].tolist())
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# # List of sessions IDs
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# # List of tasks - all possible (caseID, sessionID) combination
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# model.TASKS = pe.Set(initialize=model.CASES * model.SESSIONS, dimen=2)
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# # The duration (median case time) for each operation
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# model.CASE_DURATION = pe.Param(model.CASES, initialize=self._generate_case_durations())
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# # The duration of each theatre session
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# model.SESSION_DURATION = pe.Param(model.SESSIONS, initialize=self._generate_session_durations())
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# # The start time of each theatre session
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# model.SESSION_START_TIME = pe.Param(model.SESSIONS, initialize=self._generate_session_start_times())
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# # The deadline of each case
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# model.CASE_DEADLINES = pe.Param(model.CASES, initialize=self._get_ordinal_case_deadlines())
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# # The date of each theatre session
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# model.SESSION_DATES = pe.Param(model.SESSIONS, initialize=self._get_ordinal_session_dates())
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# model.DISJUNCTIONS = pe.Set(initialize=self._generate_disjunctions(), dimen=3)
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# ub = 1440 # seconds in a day
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# model.M = pe.Param(initialize=1e3*ub) # big M
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# max_util = 0.85
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# num_cases = self.df_cases.shape[0]
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# # Decision Variables
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# model.SESSION_ASSIGNED = pe.Var(model.TASKS, domain=pe.Binary)
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# model.CASE_START_TIME = pe.Var(model.TASKS, bounds=(0, ub), within=pe.PositiveReals)
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# model.CASES_IN_SESSION = pe.Var(model.SESSIONS, bounds=(0, num_cases), within=pe.PositiveReals)
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# # Objective
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# def objective_function(model):
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# return pe.summation(model.CASES_IN_SESSION)
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# #return sum([model.SESSION_ASSIGNED[case, session] for case in model.CASES for session in model.SESSIONS])
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# model.OBJECTIVE = pe.Objective(rule=objective_function, sense=pe.maximize)
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# # Constraints
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# # Case start time must be after start time of assigned theatre session
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# def case_start_time(model, case, session):
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# return model.CASE_START_TIME[case, session] >= model.SESSION_START_TIME[session] - \
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# ((1 - model.SESSION_ASSIGNED[(case, session)])*model.M)
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# model.CASE_START = pe.Constraint(model.TASKS, rule=case_start_time)
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# # Case end time must be before end time of assigned theatre session
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# def case_end_time(model, case, session):
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# return model.CASE_START_TIME[case, session] + model.CASE_DURATION[case] <= model.SESSION_START_TIME[session] + \
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# model.SESSION_DURATION[session]*max_util + ((1 - model.SESSION_ASSIGNED[(case, session)]) * model.M)
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# model.CASE_END_TIME = pe.Constraint(model.TASKS, rule=case_end_time)
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# # Cases can be assigned to a maximum of one session
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# def session_assignment(model, case):
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# return sum([model.SESSION_ASSIGNED[(case, session)] for session in model.SESSIONS]) <= 1
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# model.SESSION_ASSIGNMENT = pe.Constraint(model.CASES, rule=session_assignment)
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# def set_deadline_condition(model, case, session):
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# return model.SESSION_DATES[session] <= model.CASE_DEADLINES[case] + ((1 - model.SESSION_ASSIGNED[case, session])*model.M)
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# model.APPLY_DEADLINE = pe.Constraint(model.TASKS, rule=set_deadline_condition)
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# def no_case_overlap(model, case1, case2, session):
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# return [model.CASE_START_TIME[case1, session] + model.CASE_DURATION[case1] <= model.CASE_START_TIME[case2, session] + \
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# ((2 - model.SESSION_ASSIGNED[case1, session] - model.SESSION_ASSIGNED[case2, session])*model.M),
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# model.CASE_START_TIME[case2, session] + model.CASE_DURATION[case2] <= model.CASE_START_TIME[case1, session] + \
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# ((2 - model.SESSION_ASSIGNED[case1, session] - model.SESSION_ASSIGNED[case2, session])*model.M)]
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# model.DISJUNCTIONS_RULE = pyogdp.Disjunction(model.DISJUNCTIONS, rule=no_case_overlap)
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# def theatre_util(model, session):
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# return model.CASES_IN_SESSION[session] == \
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# sum([model.SESSION_ASSIGNED[case, session] for case in model.CASES])
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# model.THEATRE_UTIL = pe.Constraint(model.SESSIONS, rule=theatre_util)
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# pe.TransformationFactory("gdp.bigm").apply_to(model)
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# return model
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def solve(self, solver_name, options=None, solver_path=None, local=True):
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return
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def draw_gantt(self):
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return
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if __name__ == "__main__":
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print('tet')
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case_path = os.path.join(os.path.dirname(os.getcwd()), "data", "cases.csv")
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#case_path = "/home/hmag/code/theatre-scheduling/data/cases.csv"
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patient_path = os.path.join(os.path.dirname(os.getcwd()), "data", "patients.csv")
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session_path = os.path.join(os.path.dirname(os.getcwd()), "data", "sessions.csv")
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schedule_path = os.path.join(os.path.dirname(os.getcwd()), "data", "EKT-Plan.docx")
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cbc_path = "/usr/bin/cbc"
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options = {"seconds": 10}
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scheduler = TheatreScheduler(case_file_path=case_path, session_file_path=session_path)
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scheduler.solve(solver_name="cbc", solver_path=cbc_path, options=options)
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#scheduler.solve(solver_name="cbc", local=False, options=None)
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