LICENSE

@@ -1,29 +0,0 @@
-BSD 3-Clause License
-
-Copyright (c) 2020, Lewis Woolfson
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its
-   contributors may be used to endorse or promote products derived from
-   this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

data/case_data.csv

@@ -0,0 +1,26 @@
+CaseID,ConsultantID,Procedure,Speciality,Median Duration,TargetDeadline
+1,11,Cataract Surgery,Ophthalmology,45,2020-06-07
+2,11,Vitrectomy ,Ophthalmology,70,2020-07-17
+3,11,Cataract Surgery,Ophthalmology,45,2020-06-05
+4,11,Cataract Surgery,Ophthalmology,45,2020-06-28
+5,11,Cataract Surgery,Ophthalmology,45,2020-07-22
+6,11,Cataract Surgery,Ophthalmology,45,2020-07-03
+7,11,Cataract Surgery,Ophthalmology,45,2020-07-03
+8,11,Eyelid Lesion Excision,Ophthalmology,30,2020-06-11
+9,11,Vitrectomy ,Ophthalmology,70,2020-07-04
+10,11,Cataract Surgery,Ophthalmology,45,2020-07-14
+11,11,Cataract Surgery,Ophthalmology,45,2020-07-06
+12,11,Cataract Surgery,Ophthalmology,45,2020-07-16
+13,11,Trabeculectomy,Ophthalmology,100,2020-07-11
+14,11,Vitrectomy ,Ophthalmology,70,2020-06-15
+15,11,Vitrectomy ,Ophthalmology,70,2020-06-20
+16,11,Cataract Surgery,Ophthalmology,45,2020-06-18
+17,11,Cataract Surgery,Ophthalmology,45,2020-07-20
+18,11,Trabeculectomy,Ophthalmology,100,2020-07-04
+19,11,Eyelid Skin Graft,Ophthalmology,120,2020-06-15
+20,11,Cataract Surgery,Ophthalmology,45,2020-07-13
+21,11,Cataract Surgery,Ophthalmology,45,2020-07-04
+22,11,Cataract Surgery,Ophthalmology,45,2020-06-28
+23,11,Cataract Surgery,Ophthalmology,45,2020-07-22
+24,11,Cataract Surgery,Ophthalmology,45,2020-07-06
+25,11,Vitrectomy ,Ophthalmology,70,2020-06-08

data/cases.csv

@@ -1,31 +1,31 @@
 CaseID,ConsultantID,Procedure,Speciality,Median Duration,TargetDeadline
-1,C011,Cataract Surgery,Ophthalmology,45,07/07/2020
-2,C011,Vitrectomy ,Ophthalmology,70,17/07/2020
-3,C011,Cataract Surgery,Ophthalmology,45,05/06/2020
-4,C011,Cataract Surgery,Ophthalmology,45,28/06/2020
-5,C011,Cataract Surgery,Ophthalmology,45,22/07/2020
-6,C011,Cataract Surgery,Ophthalmology,45,03/07/2020
-7,C011,Cataract Surgery,Ophthalmology,45,03/07/2020
-8,C011,Eyelid Lesion Excision,Ophthalmology,30,11/06/2020
-9,C011,Vitrectomy,Ophthalmology,70,04/07/2020
-10,C011,Cataract Surgery,Ophthalmology,45,14/07/2020
-11,C011,Cataract Surgery,Ophthalmology,45,06/07/2020
-12,C011,Cataract Surgery,Ophthalmology,45,16/07/2020
-13,C011,Trabeculectomy,Ophthalmology,100,01/08/2020
-14,C011,Vitrectomy,Ophthalmology,70,15/06/2020
-15,C011,Vitrectomy,Ophthalmology,70,20/06/2020
-16,C011,Cataract Surgery,Ophthalmology,45,18/06/2020
-17,C011,Cataract Surgery,Ophthalmology,45,20/07/2020
-18,C011,Trabeculectomy,Ophthalmology,100,04/07/2020
-19,C011,Eyelid Skin Graft,Ophthalmology,120,15/06/2020
-20,C011,Cataract Surgery,Ophthalmology,45,13/07/2020
-21,C011,Cataract Surgery,Ophthalmology,45,04/07/2020
-22,C011,Cataract Surgery,Ophthalmology,45,28/06/2020
-23,C011,Cataract Surgery,Ophthalmology,45,22/07/2020
-24,C011,Cataract Surgery,Ophthalmology,45,06/07/2020
-25,C011,Vitrectomy,Ophthalmology,70,08/06/2020
-26,C011,Cataract Surgery,Ophthalmology,45,14/07/2020
-27,C011,Cataract Surgery,Ophthalmology,45,06/07/2020
-28,C011,Cataract Surgery,Ophthalmology,45,16/07/2020
-29,C011,Trabeculectomy,Ophthalmology,100,11/07/2020
-30,C011,Vitrectomy,Ophthalmology,70,27/06/2020
+1,11,Cataract Surgery,Ophthalmology,45,07/07/2020
+2,11,Vitrectomy ,Ophthalmology,70,17/07/2020
+3,11,Cataract Surgery,Ophthalmology,45,05/06/2020
+4,11,Cataract Surgery,Ophthalmology,45,28/06/2020
+5,11,Cataract Surgery,Ophthalmology,45,22/07/2020
+6,11,Cataract Surgery,Ophthalmology,45,03/07/2020
+7,11,Cataract Surgery,Ophthalmology,45,03/07/2020
+8,11,Eyelid Lesion Excision,Ophthalmology,30,11/06/2020
+9,11,Vitrectomy,Ophthalmology,70,04/07/2020
+10,11,Cataract Surgery,Ophthalmology,45,14/07/2020
+11,11,Cataract Surgery,Ophthalmology,45,06/07/2020
+12,11,Cataract Surgery,Ophthalmology,45,16/07/2020
+13,11,Trabeculectomy,Ophthalmology,100,01/08/2020
+14,11,Vitrectomy,Ophthalmology,70,15/06/2020
+15,11,Vitrectomy,Ophthalmology,70,20/06/2020
+16,11,Cataract Surgery,Ophthalmology,45,18/06/2020
+17,11,Cataract Surgery,Ophthalmology,45,20/07/2020
+18,11,Trabeculectomy,Ophthalmology,100,04/07/2020
+19,11,Eyelid Skin Graft,Ophthalmology,120,15/06/2020
+20,11,Cataract Surgery,Ophthalmology,45,13/07/2020
+21,11,Cataract Surgery,Ophthalmology,45,04/07/2020
+22,11,Cataract Surgery,Ophthalmology,45,28/06/2020
+23,11,Cataract Surgery,Ophthalmology,45,22/07/2020
+24,11,Cataract Surgery,Ophthalmology,45,06/07/2020
+25,11,Vitrectomy,Ophthalmology,70,08/06/2020
+26,11,Cataract Surgery,Ophthalmology,45,14/07/2020
+27,11,Cataract Surgery,Ophthalmology,45,06/07/2020
+28,11,Cataract Surgery,Ophthalmology,45,16/07/2020
+29,11,Trabeculectomy,Ophthalmology,100,11/07/2020
+30,11,Vitrectomy,Ophthalmology,70,27/06/2020

modelling/scheduler_median.py

@@ -1,4 +1,5 @@
 import pandas as pd
+import numpy as np
 import pyomo.environ as pe
 import pyomo.gdp as pyogdp
 import os
@@ -7,6 +8,7 @@ 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):
@@ -51,33 +53,10 @@ class TheatreScheduler:
             (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
-        print(self.df_sessions.loc[:, "Start"])
         self.df_sessions.loc[:, "Start"] = pd.to_timedelta(self.df_sessions["Start"])
-        #  pd.to_timedelta(s.dt.time.astype(str)).dt.total_seconds()
-        print(self.df_sessions.loc[:, "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
@@ -97,57 +76,38 @@ class TheatreScheduler:
         model = pe.ConcreteModel()
 
         # Model Data
-
-        print('test4')
-        # List of case IDs in surgical waiting list
         model.CASES = pe.Set(initialize=self.df_cases["CaseID"].tolist())
-        print('test5')
-        print(model.CASES)
-        # List of sessions IDs
         model.SESSIONS = pe.Set(initialize=self.df_sessions["SessionID"].tolist())
-        print('test6')
-        print(model)
-        # List of sessions IDs
-        # List of tasks - all possible (caseID, sessionID) combination
         model.TASKS = pe.Set(initialize=model.CASES * model.SESSIONS, dimen=2)
-        print(model.TASKS)
-        print('test7')
-        # The duration (median case time) for each operation
         model.CASE_DURATION = pe.Param(model.CASES, initialize=self._generate_case_durations())
-        print('test8')
-        # The duration of each theatre session
         model.SESSION_DURATION = pe.Param(model.SESSIONS, initialize=self._generate_session_durations())
-        print('test9')
-        # The start time of each theatre session
         model.SESSION_START_TIME = pe.Param(model.SESSIONS, initialize=self._generate_session_start_times())
-        print('te0')
-        # The deadline of each case
-        model.CASE_DEADLINES = pe.Param(model.CASES, initialize=self._get_ordinal_case_deadlines())
-        print('te1')
-        # 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]
+        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.CASES_IN_SESSION = pe.Var(model.SESSIONS, bounds=(0, num_cases), 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.CASES_IN_SESSION)
-            #return sum([model.SESSION_ASSIGNED[case, session] for case in model.CASES for session in model.SESSIONS])
+            #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] - \
@@ -165,10 +125,6 @@ class TheatreScheduler:
             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),
@@ -178,16 +134,31 @@ class TheatreScheduler:
         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])
-
+            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, local=True):
+    def solve(self, solver_name, options=None, solver_path=None):
 
         if solver_path is not None:
             solver = pe.SolverFactory(solver_name, executable=solver_path)
@@ -199,17 +170,10 @@ class TheatreScheduler:
             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)
+        solver_results = solver.solve(self.model, tee=True)
 
         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]
@@ -218,6 +182,7 @@ class TheatreScheduler:
 
         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)
@@ -227,11 +192,11 @@ class TheatreScheduler:
         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())))
+        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[self.df_times["Assignment"] == 1].to_string())
+        #print(self.df_times.to_string())
         self.draw_gantt()
 
     def draw_gantt(self):
@@ -257,7 +222,7 @@ class TheatreScheduler:
                     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_title('Session Schedule')
         ax.set_xlabel('Time')
         ax.set_ylabel('Sessions')
         ax.grid(True)
@@ -267,13 +232,10 @@ class TheatreScheduler:
 
 
 if __name__ == "__main__":
-    print('tet')
-    case_path = os.path.join(os.path.dirname(os.getcwd()), "data", "cases.csv")
-    case_path = "/home/hmag/code/theatre-scheduling/data/cases.csv"
-    session_path = "/home/hmag/code/theatre-scheduling/data/sessions.csv"
-    # cbc_path = "C:\\Users\\LONLW15\\Documents\\Linear Programming\\Solvers\\cbc.exe"
+    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": 300}
+    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)
-    #scheduler.solve(solver_name="cbc", local=False, options=None)

pyomo_env.yml

@@ -1,54 +1,143 @@
-name: lp_pyomo
+name: collider_env_v1
 channels:
+  - plotly
+  - anaconda
   - conda-forge
   - defaults
 dependencies:
+  - aniso8601=8.0.0=py_0
   - appdirs=1.4.3=py_1
+  - attrs=19.3.0=py_0
+  - backcall=0.1.0=py37_0
   - blas=1.0=mkl
-  - ca-certificates=2020.4.5.1=hecc5488_0
-  - certifi=2020.4.5.1=py38h32f6830_0
-  - cycler=0.10.0=py_2
-  - freetype=2.10.2=hd328e21_0
+  - bleach=3.1.0=py37_0
+  - ca-certificates=2020.1.1=0
+  - click=7.0=py37_0
+  - colorama=0.4.3=py_0
+  - coverage=5.0.3=py37h8055547_1
+  - cycler=0.10.0=py37_0
+  - decorator=4.4.1=py_0
+  - defusedxml=0.6.0=py_0
+  - entrypoints=0.3=py37_0
+  - flask=1.1.1=py_0
+  - flask-restplus=0.13.0=py37_0
+  - freetype=2.9.1=ha9979f8_1
   - glpk=4.65=h2fa13f4_1002
   - icc_rt=2019.0.0=h0cc432a_1
-  - icu=64.2=he025d50_1
-  - intel-openmp=2020.1=216
-  - jpeg=9c=hfa6e2cd_1001
-  - kiwisolver=1.2.0=py38heaebd3c_0
-  - libclang=9.0.1=default_hf44288c_0
-  - libpng=1.6.37=hfe6a214_1
-  - matplotlib=3.2.1=0
-  - matplotlib-base=3.2.1=py38h1626042_0
-  - mkl=2020.1=216
-  - mkl-service=2.3.0=py38hb782905_0
-  - mkl_fft=1.0.15=py38h14836fe_0
-  - mkl_random=1.1.0=py38hf9181ef_0
-  - nose=1.3.7=py38h32f6830_1004
-  - numpy=1.18.1=py38h93ca92e_0
-  - numpy-base=1.18.1=py38hc3f5095_1
+  - icu=58.2=vc14hc45fdbb_0
+  - importlib_metadata=1.5.0=py37_0
+  - intel-openmp=2020.0=166
+  - ipopt=3.11.1=2
+  - ipykernel=5.1.4=py37h39e3cac_0
+  - ipython=7.12.0=py37h5ca1d4c_0
+  - ipython_genutils=0.2.0=py37_0
+  - ipywidgets=7.5.1=py_0
+  - itsdangerous=1.1.0=py37_0
+  - jedi=0.16.0=py37_0
+  - jinja2=2.11.1=py_0
+  - joblib=0.14.1=py_0
+  - jpeg=9b=vc14h4d7706e_1
+  - json5=0.9.1=py_0
+  - jsonschema=3.2.0=py37_0
+  - jupyter=1.0.0=py37_7
+  - jupyter_client=5.3.4=py37_0
+  - jupyter_console=6.1.0=py_0
+  - jupyter_core=4.6.1=py37_0
+  - jupyterlab=1.2.6=pyhf63ae98_0
+  - jupyterlab_server=1.0.6=py_0
+  - kiwisolver=1.1.0=py37ha925a31_0
+  - libpng=1.6.37=h2a8f88b_0
+  - libsodium=1.0.16=h9d3ae62_0
+  - m2w64-gcc-libgfortran=5.3.0=6
+  - m2w64-gcc-libs=5.3.0=7
+  - m2w64-gcc-libs-core=5.3.0=7
+  - m2w64-gmp=6.1.0=2
+  - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+  - markupsafe=1.1.1=py37he774522_0
+  - matplotlib=3.1.3=py37_0
+  - matplotlib-base=3.1.3=py37h64f37c6_0
+  - mistune=0.8.4=py37he774522_0
+  - mkl=2019.5=281
+  - mkl-service=2.3.0=py37hb782905_0
+  - mkl_fft=1.0.15=py37h14836fe_0
+  - mkl_random=1.1.0=py37h675688f_0
+  - msys2-conda-epoch=20160418=1
+  - nb_conda=2.2.1=py37_0
+  - nb_conda_kernels=2.2.2=py37_0
+  - nbconvert=5.6.1=py37_0
+  - nbformat=5.0.4=py_0
+  - networkx=2.4=py_0
+  - nose=1.3.7=py37hc8dfbb8_1004
+  - nose2=0.9.2=py_0
+  - notebook=6.0.3=py37_0
+  - numpy=1.18.1=py37h93ca92e_0
+  - numpy-base=1.18.1=py37hc3f5095_1
   - openssl=1.1.1g=he774522_0
-  - pandas=1.0.3=py38h47e9c7a_0
-  - pip=20.0.2=py38_3
+  - pandas=1.0.1=py37h47e9c7a_0
+  - pandoc=2.2.3.2=0
+  - pandocfilters=1.4.2=py37_1
+  - parso=0.6.1=py_0
+  - patsy=0.5.1=py37_0
+  - pickleshare=0.7.5=py37_0
+  - pip=20.0.2=py37_1
+  - plotly=4.5.4=py_0
+  - plotly_express=0.4.1=py_0
   - ply=3.11=py_1
-  - pyomo=5.6.9=py38h32f6830_2
-  - pyparsing=2.4.7=pyh9f0ad1d_0
-  - pyqt=5.12.3=py38h7ae7562_3
-  - python=3.8.3=he1778fa_0
+  - prometheus_client=0.7.1=py_0
+  - prompt_toolkit=3.0.3=py_0
+  - pygments=2.5.2=py_0
+  - pyodbc=4.0.30=py37ha925a31_0
+  - pyomo=5.6.8=py37_0
+  - pyparsing=2.4.6=py_0
+  - pyqt=5.9.2=py37ha878b3d_0
+  - pyrsistent=0.15.7=py37he774522_0
+  - python=3.7.6=h60c2a47_2
   - python-dateutil=2.8.1=py_0
-  - python_abi=3.8=1_cp38
-  - pytz=2020.1=py_0
-  - pyutilib=5.8.0=pyh9f0ad1d_1
-  - qt=5.12.5=h7ef1ec2_0
-  - setuptools=46.4.0=py38_0
-  - six=1.14.0=py38_0
-  - sqlite=3.31.1=h2a8f88b_1
-  - tornado=6.0.4=py38hfa6e2cd_0
+  - python_abi=3.7=1_cp37m
+  - pytz=2019.3=py_0
+  - pyutilib=5.7.3=py37_4
+  - pywin32=227=py37he774522_1
+  - pywinpty=0.5.7=py37_0
+  - pyzmq=18.1.1=py37ha925a31_0
+  - qt=5.9.7=vc14h73c81de_0
+  - qtconsole=4.7.2=py_0
+  - qtpy=1.9.0=py_0
+  - retrying=1.3.3=py37_2
+  - scikit-learn=0.22.1=py37h6288b17_0
+  - scipy=1.4.1=py37h9439919_0
+  - seaborn=0.10.0=py_0
+  - send2trash=1.5.0=py37_0
+  - setuptools=46.0.0=py37_0
+  - sip=4.19.13=py37ha925a31_0
+  - sqlite=3.31.1=he774522_0
+  - statsmodels=0.11.0=py37he774522_0
+  - terminado=0.8.3=py37_0
+  - testpath=0.4.4=py_0
+  - tornado=6.0.3=py37he774522_3
+  - traitlets=4.3.3=py37_0
   - vc=14.1=h0510ff6_4
-  - vs2015_runtime=14.16.27012=hf0eaf9b_2
-  - wheel=0.34.2=py38_0
-  - wincertstore=0.2=py38_0
-  - zlib=1.2.11=h62dcd97_4
+  - vs2015_runtime=14.16.27012=hf0eaf9b_1
+  - waitress=1.4.3=py_0
+  - wcwidth=0.1.8=py_0
+  - webencodings=0.5.1=py37_1
+  - werkzeug=0.16.1=py_0
+  - wheel=0.34.2=py37_0
+  - widgetsnbextension=3.5.1=py37_0
+  - wincertstore=0.2=py37_0
+  - winpty=0.4.3=4
+  - xlrd=1.2.0=py37_0
+  - zeromq=4.3.1=h33f27b4_3
+  - zipp=2.2.0=py_0
+  - zlib=1.2.11=vc14h1cdd9ab_1
   - pip:
-    - pyqt5-sip==4.19.18
-    - pyqtchart==5.12
-    - pyqtwebengine==5.12.1
+    - cbcpy==2.10.3a2
+    - certifi==2017.7.27.1
+    - cplex==12.9.0.0
+    - docloud==1.0.257
+    - docplex==2.9.133
+    - func-timeout==4.3.5
+    - requests==2.18.4
+    - six==1.10.0
+    - urllib3==1.22
+prefix: C:\Users\200234360\AppData\Local\Continuum\anaconda3\envs\collider_env_v1
+

requirements.txt

@@ -1,3 +0,0 @@
-pandas
-pyomo
-matplotlib