dump work

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

README

@@ -1,8 +1,3 @@
-# Create EKG Schedule
-
-based on https://python.plainenglish.io/solving-the-resource-constrained-project-scheduling-problem-rcpsp-with-python-and-pyomo-001cffd5344a
-
-$ python -m venv ekg
-$ source ekg/bin/activate
-$ pip install -r requirements.txt
-
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt

data/blocks.csv

@@ -0,0 +1,33 @@
+day,time,available
+03/12/2024,08:00:00,1
+03/12/2024,08:20:00,1
+03/12/2024,08:40:00,1
+03/12/2024,09:00:00,1
+03/12/2024,09:20:00,1
+03/12/2024,09:40:00,1
+03/12/2024,10:00:00,1
+03/12/2024,10:20:00,1
+03/12/2024,10:40:00,0
+03/12/2024,11:00:00,1
+03/12/2024,11:20:00,1
+03/12/2024,11:40:00,0
+03/12/2024,12:00:00,1
+03/12/2024,12:20:00,1
+03/12/2024,12:40:00,1
+03/12/2024,13:00:00,1
+04/12/2024,08:00:00,0
+04/12/2024,08:20:00,0
+04/12/2024,08:40:00,0
+04/12/2024,09:00:00,1
+04/12/2024,09:20:00,1
+04/12/2024,09:40:00,1
+04/12/2024,10:00:00,1
+04/12/2024,10:20:00,1
+04/12/2024,10:40:00,1
+04/12/2024,11:00:00,0
+04/12/2024,11:20:00,1
+04/12/2024,11:40:00,1
+04/12/2024,12:00:00,1
+04/12/2024,12:20:00,1
+04/12/2024,12:40:00,1
+04/12/2024,13:00:00,0

data/cases.csv

@@ -1,9 +0,0 @@
-CaseID,PatientID,Station,EKT,Erhaltung,Ket,Date
-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/patients.csv

@@ -1,12 +1,17 @@
-Id,PatientID,Patient,Station
-1,21-239,Hans Zimmer,1
-2,21-237,Lina Gruber,0
-3,21-238,Victoria J,3
-4,21-229,Hilmario Maggio,3
-5,21-249,Egon Schiele,2
-6,21-219,Hans Landa,1
-7,21-213,Gregor Samsa,1
-8,21-222,Johanne,2
-9,21-223,Angela Merkel,1
-10,21-240,Britney Spears,3
-11,21-241,Madonna,0
+patient,id,station
+Hr. Y (9),21-237,08b
+Hr. X (5),21-239,16A
+Hr. Q (12),21-235,08B
+Hr. N (9),21-238,16A
+Hr. Li (16/4E),21-228,16A
+Hr. J,21-176,16A
+Hr. Ä (17/3E),21-223,16A
+Fr.O (1),21-242,16b
+Fr. S (18/1E),21-227,08B
+Fr. R (15/3E),21-220,16B
+Fr. P (8E),21-213,16B
+Fr. M (7E),21-216,16A
+Fr. L (7E),21-214,16B
+Fr. F (12/1E),21-234,08b
+Fr. E (13),21-233,16B
+Fr. A (7),21-236,16A

data/sessions.csv

@@ -1,13 +0,0 @@
-SessionID,Date,Start,End,Duration,ConsultantID,Specialty
-1001,29/11/2024,08:00:00,08:20:00,20,11,Ophthalmology
-1002,29/11/2024,08:20:00,08:40:00,20,11,Ophthalmology
-1003,29/11/2024,08:40:00,09:00:00,20,11,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

data/tasks.csv

@@ -0,0 +1,12 @@
+id,patient,priority,date,time,duration,station
+1001,21-237,1,03/12/2024,08:30:00,1,0
+1002,21-239,1,04/12/2024,08:30:00,1,2
+1003,21-235,11,03/12/2024,08:30:00,1,1
+1004,21-227,1,03/12/2024,08:30:00,1,0
+1005,21-238,2,03/12/2024,08:30:00,1,1
+1006,21-240,1,04/12/2024,08:30:00,1,3
+1007,21-236,1,03/12/2024,08:30:00,1,2
+1008,21-228,1,03/12/2024,08:30:00,1,1
+1009,21-249,1,03/12/2024,08:30:00,1,2
+1010,21-241,1,03/12/2024,08:30:00,1,3
+1011,21-247,1,03/12/2024,08:30:00,1,3

opt.py

@@ -1,175 +1,121 @@
+import pyomo
 import pandas as pd
-import pyomo.environ as pyo
-import datetime
-import matplotlib.cm as cm
-import matplotlib.pyplot as plt
+from pulp import *
 
 class Scheduler:
-    def __init__(self, case_file_path, session_file_path, patient_file_path):
+    def __init__(self,
+                 task_path='data/tasks.csv',
+                 block_path='data/blocks.csv',
+                 patient_path='data/patients.csv'):
         """
         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
+            session_file_path (str): path to theatre session data in CSV format 
         """
         try:
-            self.df_cases = pd.read_csv(case_file_path)
+            self.df_tasks = pd.read_csv(task_path)
         except FileNotFoundError:
-            print(f"Case data not found. {case_file_path}")
+            print("Task data not found.")
         try:
-            self.df_sessions = pd.read_csv(session_file_path)
+            self.df_blocks = pd.read_csv(block_path)
         except FileNotFoundError:
             print("Session data not found")
         try:
-            self.df_patients = pd.read_csv(patient_file_path)
+            self.df_patients = pd.read_csv(patient_path)
         except FileNotFoundError:
             print("Patient data not found")
-        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)
+        self.create_lists()
 
-    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_lists(self):
+        self.s = self.df_tasks['priority'].tolist()
+        self.d = self.df_tasks['duration'].tolist()
+        self.dates = self.df_tasks['date'].tolist()
+        self.b = self.df_blocks['available'].tolist()
+        self.block_dates= self.df_blocks['day'].tolist()
 
-    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()
+        self.patient_ids = self.df_patients['patient'].tolist()
+        self.task_patient= self.df_tasks['patient'].tolist()
+        # convert dates of tasks and blocks to (week,day)
+        self.convert_dates()
         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 convert_dates(self):
+        self.week_day = [self.date2int(date) for date in self.dates]
+        self.block_week_day = [self.date2int(date) for date in self.block_dates]
 
-    def set_options(self):
-        # Add solver parameters (time limit)
-        options = {"seconds": 6}
-        for key, value in options.items():
-          self.solver.options[key] = value
+    def date2int(self, date):
+        d0 = pd.to_datetime("25/11/2024", dayfirst=True)
+        delta = (pd.to_datetime(date, dayfirst=True) - d0).days
+        day = delta%7
+        week = int((delta - day)/7)
+        return week,day
 
-    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)
-    delta = (pd.to_datetime(date, dayfirst=True) - d0).days
-    day = delta%7
-    week = int((delta - day)/7)
-    return week,day
-
-def int2date(week, day):
-    d0 = pd.to_datetime("25/11/2024", dayfirst=True)
-    delta = datetime.timedelta(days = 7*week + day)
-    date = d0 + delta
-    return date
-
-path = '/home/hmag/git/octopusx/ekt/data'
-my = Scheduler(path+'/cases.csv', path+'/sessions.csv', path+'/patients.csv')
-
-print(my.df_cases)
-print(my.df_sessions)
-#print(my.df_patients)
-#for i in my.df_patients:
-#    print(i)
-print(my.df_cases['Date'][0])
-date = my.df_cases['Date'][0]
-
-print(date2int(date))
-print(int2date(1,0))
+    def int2date(week, day):
+        d0 = pd.to_datetime("25/11/2024", dayfirst=True)
+        delta = datetime.timedelta(days = 7*week + day)
+        date = d0 + delta
+        return date
 
 
-#my.set_constraint()
-#my.set_obj()
-my.solve_model()
+sc = Scheduler()
+print(sc.df_tasks)
+print(sc.df_blocks)
+print(sc.df_patients)
 
-my.extract_results()
-print(my.df_times)
-my.draw_gantt()
+# Declare variables for optimization
+s = sc.s
+print(f's = {s}')
+
+d = sc.d
+print(f'd = {d}')
+
+b =  sc.b
+print(f'b = {b}')
+
+B = len(b)
+n = len(s)
+A = sum(b)
+
+# Import PuLP
+
+# Define the problem
+prob = LpProblem("Schedule_Tasks", LpMaximize)
+
+# Define y
+y = LpVariable.dicts('Block', [(i, t) for i in range(n) for t in range(B)], cat = 'Binary')
+
+# Definite objective function
+prob += lpSum(s[i]*b[t]*y[(i,t)] for i in range(n) for t in range(B))
+
+# CONSTRAINTS
+# Constraint #1
+prob += lpSum(y[(i,t)] for i in range(n) for t in range(B)) <= A
+
+# Constraint #2
+for i in range(n):
+    prob += lpSum(y[(i,t)] for t in range(B)) <= d[i]
+
+# Constraint #3
+for t in range(B):
+    prob += lpSum(y[(i,t)] for i in range(n)) <= 1
+
+prob.solve()
+
+# Visualize solution
+tasks_blocks = pd.DataFrame(columns=['Task', 'Block'])
+
+for i in range(n):
+    for t in range(B):
+        if y[(i,t)].varValue == 1:
+            tasks_blocks = pd.concat([tasks_blocks,
+                                     pd.DataFrame({'Task': [i], 'Block': [t], 'Patient': sc.task_patient[i] })],
+                                     ignore_index=True)
+
+print(tasks_blocks)
+
+print(sc.dates)
+print(sc.week_day)
+print(sc.block_dates)
+print(sc.block_week_day)

requirements.txt

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