dump progresss

Signed-off-by: Hilmar Magnusson <hilmar.magnusson@bisdn.de>

data/cases.csv

@@ -2,3 +2,8 @@
 1,21-239,1,15,0,no,28/11/2024
 2,21-237,0,22,1,no,29/11/2024
 3,21-238,3,12,1,no,30/11/2024
+4,21-248,2,12,1,no,30/11/2024
+5,21-243,3,12,1,no,30/11/2024
+6,21-233,3,12,1,no,30/11/2024
+7,21-218,3,12,1,no,30/11/2024
+8,21-208,3,12,1,no,30/11/2024

data/sessions.csv

@@ -1,5 +1,13 @@
 SessionID,Date,Start,End,Duration,ConsultantID,Specialty
-1001,03/06/2020,08:30:00,18:00:00,570,11,Ophthalmology
+1001,29/11/2024,08:00:00,08:20:00,20,11,Ophthalmology
-1002,10/06/2020,08:30:00,18:00:00,570,11,Ophthalmology
+1002,29/11/2024,08:20:00,08:40:00,20,11,Ophthalmology
-1003,17/06/2020,08:30:00,18:00:00,570,11,Ophthalmology
+1003,29/11/2024,08:40:00,09:00:00,20,11,Ophthalmology
-1004,25/06/2020,08:30:00,13:00:00,270,11,Ophthalmology
+1004,29/11/2024,09:00:00,09:20:00,20,11,Ophthalmology
+1005,29/11/2024,09:20:00,09:40:00,20,11,Ophthalmology
+1006,29/11/2024,09:40:00,10:00:00,20,11,Ophthalmology
+1007,29/11/2024,10:00:00,10:20:00,20,11,Ophthalmology
+1008,29/11/2024,10:20:00,10:40:00,20,11,Ophthalmology
+1009,29/11/2024,10:40:00,11:00:00,20,11,Ophthalmology
+1010,29/11/2024,11:00:00,11:20:00,20,11,Ophthalmology
+1011,29/11/2024,11:20:00,11:40:00,20,11,Ophthalmology
+1012,29/11/2024,11:40:00,12:00:00,20,11,Ophthalmology

opt.py

@@ -1,30 +1,8 @@
-from pyomo.environ import SolverFactory
-solver = SolverFactory('glpk')
-
 import pandas as pd
 import pyomo.environ as pyo
 import datetime
-# Create a model
+import matplotlib.cm as cm
-model = pyo.ConcreteModel()
+import matplotlib.pyplot as plt
-
-## Define variables
-#model.x = pyo.Var(within=pyo.NonNegativeReals)
-#model.y = pyo.Var(within=pyo.NonNegativeReals)
-## Define objective
-#model.obj = pyo.Objective(expr=model.x + model.y, sense=pyo.minimize)
-## Define constraints
-#model.con1 = pyo.Constraint(expr=model.x + 2 * model.y >= 4)
-#model.con2 = pyo.Constraint(expr=model.x - model.y <= 1)
-## Select solver
-#solver = pyo.SolverFactory('glpk')
-## Solve the problem
-#result = solver.solve(model)
-## Display results
-#print('Status:', result.solver.status)
-#print('Termination Condition:', result.solver.termination_condition)
-#print('Optimal x:', pyo.value(model.x))
-#print('Optimal y:', pyo.value(model.y))
-#print('Optimal Objective:', pyo.value(model.obj))
 
 class Scheduler:
     def __init__(self, case_file_path, session_file_path, patient_file_path):
@@ -37,7 +15,7 @@ class Scheduler:
         try:
             self.df_cases = pd.read_csv(case_file_path)
         except FileNotFoundError:
-            print("Case data not found.")
+            print(f"Case data not found. {case_file_path}")
         try:
             self.df_sessions = pd.read_csv(session_file_path)
         except FileNotFoundError:
@@ -46,7 +24,119 @@ class Scheduler:
             self.df_patients = pd.read_csv(patient_file_path)
         except FileNotFoundError:
             print("Patient data not found")
-        #self.model = self.create_model()
+        self.solver = pyo.SolverFactory('glpk')
+        self.model = self.create_model()
+        self.build_model()
+
+    def solve_model(self):
+        self.solver_results = self.solver.solve(self.model, tee=True)
+
+    def extract_results(self):
+        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)
+
+    def create_model(self):
+        return pyo.ConcreteModel()
+
+    def set_constraint(self):
+        self.model.c = pyo.ConstraintList()
+
+    def solve(self):
+        result = self.solver.solve(self.model)
+        print(f'result was {result}')
+
+    def build_model(self):
+        self.add_cases()
+        #self.set_options()
+        self.add_sessions()
+        self.add_tasks()
+        self.set_decisions()
+        self.set_obj()
+        return
+
+    def add_cases(self):
+        # List of case IDs in surgical waiting list
+        self.model.CASES = pyo.Set(initialize=self.df_cases["CaseID"].tolist())
+        return
+
+    def set_options(self):
+        # Add solver parameters (time limit)
+        options = {"seconds": 6}
+        for key, value in options.items():
+          self.solver.options[key] = value
+
+    def add_sessions(self):
+        # List of sessions IDs
+        # TODO: Generate more sessions based on EKT Erhaltung
+        self.model.SESSIONS = pyo.Set(initialize=self.df_sessions["SessionID"].tolist())
+        return
+
+    # List of job shop tasks
+    # all possible combinations of cases and sessions
+    def add_tasks(self):
+        self.model.TASKS = pyo.Set(initialize=self.model.CASES * self.model.SESSIONS, dimen=2)
+        return
+    # Decision Variables
+
+    ## Upper bound (minutes in a day)
+    #ub = 1440
+    ## Upper bound of session utilisation set to 85%
+    #max_util = 0.85
+    def set_decisions(self):
+
+        # Binary flag, 1 if case is assigned to session, 0 otherwise
+        self.model.SESSION_ASSIGNED = pyo.Var(self.model.TASKS, domain=pyo.Binary)
+        # Start time of a case
+        #self.model.CASE_START_TIME = pe.Var(self.model.TASKS, bounds=(0, ub), within=pe.PositiveReals)
+        # Session utilisation
+        num_cases = self.df_cases.shape[0]
+        self.model.CASES_IN_SESSION = pyo.Var(self.model.SESSIONS, bounds=(0, num_cases), within=pyo.PositiveReals)
+        self.model.UTILISATION = pyo.Var(self.model.SESSIONS, bounds=(0, 1), within=pyo.PositiveReals)
+
+    def set_obj(self):
+
+        # Objective
+        def objective_function(model):
+            return pyo.summation(model.CASES_IN_SESSION)
+        self.model.OBJECTIVE = pyo.Objective(rule=objective_function, sense=pyo.maximize)
+
+    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()
 
 def date2int(date):
     d0 = pd.to_datetime("25/11/2024", dayfirst=True)
@@ -61,7 +151,7 @@ def int2date(week, day):
     date = d0 + delta
     return date
 
-path = '/home/hmag/code/pyomo/data'
+path = '/home/hmag/git/octopusx/ekt/data'
 my = Scheduler(path+'/cases.csv', path+'/sessions.csv', path+'/patients.csv')
 
 print(my.df_cases)
@@ -75,3 +165,11 @@ date = my.df_cases['Date'][0]
 print(date2int(date))
 print(int2date(1,0))
 
+
+#my.set_constraint()
+#my.set_obj()
+my.solve_model()
+
+my.extract_results()
+print(my.df_times)
+my.draw_gantt()

requirements.txt

@@ -1,2 +1,3 @@
 pyomo
 pandas
+matplotlib