fix(model): Fixed bug in MonitoredResource where activities scheduled to occur at end of run length didn't happen, by making them happen after env.run()

Author: amyheather

notebooks/analysis.ipynb

@@ -336,7 +336,7 @@
        "      <td>2.065396</td>\n",
        "      <td>9.622971</td>\n",
        "      <td>0.643006</td>\n",
-       "      <td>0.642751</td>\n",
+       "      <td>0.643006</td>\n",
        "      <td>0.554903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -347,7 +347,7 @@
        "      <td>1.396755</td>\n",
        "      <td>10.186235</td>\n",
        "      <td>0.645640</td>\n",
-       "      <td>0.646154</td>\n",
+       "      <td>0.647690</td>\n",
        "      <td>0.355707</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -358,7 +358,7 @@
        "      <td>3.484284</td>\n",
        "      <td>10.733822</td>\n",
        "      <td>0.660790</td>\n",
-       "      <td>0.666590</td>\n",
+       "      <td>0.668319</td>\n",
        "      <td>0.858219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -369,7 +369,7 @@
        "      <td>2.050522</td>\n",
        "      <td>10.511447</td>\n",
        "      <td>0.634541</td>\n",
-       "      <td>0.638077</td>\n",
+       "      <td>0.638407</td>\n",
        "      <td>0.502188</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -380,7 +380,7 @@
        "      <td>1.836706</td>\n",
        "      <td>9.742127</td>\n",
        "      <td>0.597097</td>\n",
-       "      <td>0.599347</td>\n",
+       "      <td>0.601461</td>\n",
        "      <td>0.451830</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -396,11 +396,11 @@
        "4           4         0       369           1.836706              9.742127   \n",
        "\n",
        "   mean_nurse_utilisation  mean_nurse_utilisation_tw  mean_nurse_q_length  \n",
-       "0                0.643006                   0.642751             0.554903  \n",
-       "1                0.645640                   0.646154             0.355707  \n",
-       "2                0.660790                   0.666590             0.858219  \n",
-       "3                0.634541                   0.638077             0.502188  \n",
-       "4                0.597097                   0.599347             0.451830  "
+       "0                0.643006                   0.643006             0.554903  \n",
+       "1                0.645640                   0.647690             0.355707  \n",
+       "2                0.660790                   0.668319             0.858219  \n",
+       "3                0.634541                   0.638407             0.502188  \n",
+       "4                0.597097                   0.601461             0.451830  "
       ]
      },
      "metadata": {},
@@ -635,7 +635,7 @@
        "      <td>2.166733</td>\n",
        "      <td>10.159320</td>\n",
        "      <td>0.636215</td>\n",
-       "      <td>0.638584</td>\n",
+       "      <td>0.639776</td>\n",
        "      <td>0.544569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -644,7 +644,7 @@
        "      <td>0.784458</td>\n",
        "      <td>0.478668</td>\n",
        "      <td>0.023832</td>\n",
-       "      <td>0.024489</td>\n",
+       "      <td>0.024278</td>\n",
        "      <td>0.190098</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -653,7 +653,7 @@
        "      <td>1.192699</td>\n",
        "      <td>9.564976</td>\n",
        "      <td>0.606623</td>\n",
-       "      <td>0.608177</td>\n",
+       "      <td>0.609631</td>\n",
        "      <td>0.308532</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -662,7 +662,7 @@
        "      <td>3.140766</td>\n",
        "      <td>10.753665</td>\n",
        "      <td>0.665806</td>\n",
-       "      <td>0.668991</td>\n",
+       "      <td>0.669921</td>\n",
        "      <td>0.780607</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -677,10 +677,10 @@
        "upper_95_ci  396.871659           3.140766             10.753665   \n",
        "\n",
        "             mean_nurse_utilisation  mean_nurse_utilisation_tw  \\\n",
-       "mean                       0.636215                   0.638584   \n",
-       "std_dev                    0.023832                   0.024489   \n",
-       "lower_95_ci                0.606623                   0.608177   \n",
-       "upper_95_ci                0.665806                   0.668991   \n",
+       "mean                       0.636215                   0.639776   \n",
+       "std_dev                    0.023832                   0.024278   \n",
+       "lower_95_ci                0.606623                   0.609631   \n",
+       "upper_95_ci                0.665806                   0.669921   \n",
        "\n",
        "             mean_nurse_q_length  \n",
        "mean                    0.544569  \n",

notebooks/generate_exp_results.ipynb

@@ -1,5 +1,5 @@
 arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length
-10776.741935483871,0.499036905566402,9.978456743390534,0.49767035119563796,0.49778467447803937,0.12458802034117737
-115.80327218806197,0.06739346957542013,0.11513823236530764,0.007524465631261531,0.007540597007787788,0.01720568342578357
-10734.264952313013,0.47431678135150124,9.936223698796068,0.4949103549196282,0.4950187611653494,0.11827692470880834
-10819.21891865473,0.5237570297813028,10.020689787985,0.5004303474716477,0.5005505877907294,0.1308991159735464
+10776.741935483871,0.499036905566402,9.978456743390534,0.49767035119563796,0.497804034286856,0.12459850290556283
+115.80327218806197,0.06739346957542013,0.11513823236530764,0.007524465631261531,0.0075405891452702024,0.017212719807337778
+10734.264952313013,0.47431678135150124,9.936223698796068,0.4949103549196282,0.4950381238581607,0.11828482630760263
+10819.21891865473,0.5237570297813028,10.020689787985,0.5004303474716477,0.5005699447155513,0.13091217950352302

outputs/example_overall.csv

@@ -1,32 +1,32 @@
 run_number,scenario,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length
-0,0,10972,0.504541081338615,9.84226781662332,0.4996386466177992,0.4997094613610211,0.12814409130665008
-1,0,10784,0.514150649003393,10.060480983450425,0.5019905433327594,0.502064256550978,0.1283472360845507
-2,0,10854,0.5232349226016817,9.925024519746302,0.4981297032213408,0.49823133874707154,0.1324071941666391
-3,0,10831,0.4791488631810612,9.9370571543943,0.49822049332297624,0.4982638436545642,0.12013104947023319
-4,0,10720,0.46145745726579823,10.015904147971671,0.4968703372055205,0.4971000995404679,0.11450981346966105
-5,0,10772,0.3882646868128185,9.884995942861282,0.492904233058074,0.49304721268899043,0.09681451866545558
-6,0,10831,0.4669381081669874,10.041799654744745,0.503356251584998,0.5033169859948308,0.11706959836936667
-7,0,10781,0.625888360901447,10.086979128063648,0.5034489381047406,0.5037349298112143,0.15619681525181714
+0,0,10972,0.504541081338615,9.84226781662332,0.4996386466177992,0.4997404525127849,0.12814409130665008
+1,0,10784,0.514150649003393,10.060480983450425,0.5019905433327594,0.502069010098797,0.1283472360845507
+2,0,10854,0.5232349226016817,9.925024519746302,0.4981297032213408,0.4982604514269931,0.1326692082859329
+3,0,10831,0.4791488631810612,9.9370571543943,0.49822049332297624,0.4982689502894135,0.12013104947023319
+4,0,10720,0.46145745726579823,10.015904147971671,0.4968703372055205,0.4971055007588973,0.11450981346966105
+5,0,10772,0.3882646868128185,9.884995942861282,0.492904233058074,0.4930559758823053,0.09681451866545558
+6,0,10831,0.4669381081669874,10.041799654744745,0.503356251584998,0.5034005361957391,0.11706959836936667
+7,0,10781,0.625888360901447,10.086979128063648,0.5034489381047406,0.5037422085180931,0.15619681525181714
 8,0,10772,0.4684971326393017,10.202270228377186,0.508790994907774,0.5089130015618383,0.11682062761089254
-9,0,10705,0.5634349862001105,10.09260227901333,0.49997178325917613,0.5002042408148075,0.1399966447950717
-10,0,10927,0.562585969190057,10.083553615426284,0.5098373896503802,0.5099831780480953,0.14230039086434612
-11,0,10688,0.39781182403365434,9.893334037740962,0.4895233535105571,0.4896102199835747,0.09842159202017818
-12,0,11092,0.44530362984867583,9.810905063859671,0.5034276977164731,0.5034560301359091,0.11436285729213683
-13,0,10640,0.4397605749351992,9.933879963761742,0.48925457544062534,0.4892243098930909,0.10831140086366944
-14,0,10904,0.6107822771635987,10.167683587926042,0.513130373051619,0.5135275707547177,0.1541659710692565
-15,0,10849,0.47670998312351787,9.975581276412813,0.5009629889699926,0.5010211453918623,0.11971820849321864
-16,0,10719,0.428336230071242,9.809804967118032,0.4866947365248423,0.48682230223169853,0.10628092708642692
-17,0,10713,0.505574486075843,9.989154604862323,0.4954087210793937,0.49570388412668587,0.1253754506789469
-18,0,10568,0.3615821082986753,9.800182515281657,0.4793715775153063,0.4795651069171014,0.08845369723380557
-19,0,10707,0.4191589720392036,9.9193906381964,0.4915291568180775,0.49170197747337024,0.10388738688943873
-20,0,10845,0.6120142008002236,9.813774247048583,0.49261944375555833,0.492666922309052,0.15364106499255614
-21,0,10726,0.5173212645251098,9.858186468091487,0.48940978932654106,0.4895469034983069,0.1284441639651928
-22,0,10618,0.5316566599495316,9.969891296475984,0.4892835610032433,0.48936990129569896,0.13067431517000294
-23,0,10914,0.5270546197660896,9.925291785145637,0.5013818263178943,0.501432376992062,0.1331544935214607
-24,0,10660,0.5029697553047842,9.96454608027881,0.49160360886911897,0.4916211282340966,0.12411244424881943
-25,0,10685,0.6270646900660448,10.236338445072258,0.5060501169430693,0.5060869246801079,0.155101882934466
+9,0,10705,0.5634349862001105,10.09260227901333,0.49997178325917613,0.5002278075946729,0.1399966447950717
+10,0,10927,0.562585969190057,10.083553615426284,0.5098373896503802,0.5099889270180503,0.14230039086434612
+11,0,10688,0.39781182403365434,9.893334037740962,0.4895233535105571,0.48962710728052056,0.09842159202017818
+12,0,11092,0.44530362984867583,9.810905063859671,0.5034276977164731,0.5034590643983867,0.11437196007956935
+13,0,10640,0.4397605749351992,9.933879963761742,0.48925457544062534,0.4892677288083271,0.10831140086366944
+14,0,10904,0.6107822771635987,10.167683587926042,0.513130373051619,0.5135294499218972,0.1541659710692565
+15,0,10849,0.47670998312351787,9.975581276412813,0.5009629889699926,0.5010455334069686,0.11971820849321864
+16,0,10719,0.428336230071242,9.809804967118032,0.4866947365248423,0.4868274499224406,0.10628092708642692
+17,0,10713,0.505574486075843,9.989154604862323,0.4954087210793937,0.4957697619501451,0.1253754506789469
+18,0,10568,0.3615821082986753,9.800182515281657,0.4793715775153063,0.4795652084277097,0.08845369723380557
+19,0,10707,0.4191589720392036,9.9193906381964,0.4915291568180775,0.4917635127186296,0.10388738688943873
+20,0,10845,0.6120142008002236,9.813774247048583,0.49261944375555833,0.49267201493881163,0.15364106499255614
+21,0,10726,0.5173212645251098,9.858186468091487,0.48940978932654106,0.4895612849605359,0.1284441639651928
+22,0,10618,0.5316566599495316,9.969891296475984,0.4892835610032433,0.4893719186906082,0.13067431517000294
+23,0,10914,0.5270546197660896,9.925291785145637,0.5013818263178943,0.501433800156172,0.1331544935214607
+24,0,10660,0.5029697553047842,9.96454608027881,0.49160360886911897,0.49162164437257694,0.12411244424881943
+25,0,10685,0.6270646900660448,10.236338445072258,0.5060501169430693,0.5061407672693311,0.15515572552368928
 26,0,10806,0.5061794260886525,10.039653295082593,0.5022615440123254,0.5023039700989318,0.126615159220231
-27,0,10748,0.4925537687951073,10.050997027592315,0.4999546777219452,0.5000883223037053,0.12254555340300495
-28,0,10589,0.4577189216542841,10.044180668559221,0.4923573694687436,0.49234245205738814,0.11219411253234293
-29,0,10863,0.5422405738480466,10.034383892923174,0.5046064549261675,0.504719890581045,0.13635091096554006
-30,0,10796,0.5102078888697092,9.922063713004379,0.49578999982774674,0.4959450210869338,0.12767905794112022
+27,0,10748,0.4925537687951073,10.050997027592315,0.4999546777219452,0.5001183558252156,0.12254555340300495
+28,0,10589,0.4577189216542841,10.044180668559221,0.4923573694687436,0.4923672645089264,0.11219411253234293
+29,0,10863,0.5422405738480466,10.034383892923174,0.5046064549261675,0.5047331518468727,0.13635091096554006
+30,0,10796,0.5102078888697092,9.922063713004379,0.49578999982774674,0.49597325153194355,0.12767905794112022

outputs/example_run.csv

@@ -200,11 +200,11 @@ class MonitoredResource(simpy.Resource):
     Attributes:
         time_last_event (float):
             Time of last resource request or release.
-        queue_time (float):
+        area_n_in_queue (float):
             Total time that patients have spent queueing for the resource
             (i.e. sum of the times each patient spent waiting). Used to
             calculate the average queue length.
-        resource_busy_time (float):
+        area_resource_busy (float):
             Total time that resources have been in use during the simulation
             (i.e. sum of the times each individual resource was busy). Used
             to calculated utilisation.
@@ -230,8 +230,8 @@ def init_results(self):
         Resets monitoring attributes to initial values.
         """
         self.time_last_event = self._env.now
-        self.queue_time = 0.0
-        self.resource_busy_time = 0.0
+        self.area_n_in_queue = 0.0
+        self.area_resource_busy = 0.0
 
     def request(self, *args, **kwargs):
         """
@@ -277,7 +277,7 @@ def update_time_weighted_stats(self):
 
         Between every request or release of the resource, it calculates the
         relevant statistics - e.g.:
-        - Total waiting times (number of requests in queue * time)
+        - Total queue time (number of requests in queue * time)
         - Total resource use (number of resources in use * time)
         These are summed to return the totals from across the whole simulation.
         In Runner.run_single(), these are then used to calculate utilisation.
@@ -296,28 +296,9 @@ def update_time_weighted_stats(self):
 
         # Update the statistics
         # len(self.queue) is the number of requests queued
-        self.queue_time += len(self.queue) * time_since_last_event
+        self.area_n_in_queue += len(self.queue) * time_since_last_event
         # self.count is the number of resources in use
-        self.resource_busy_time += self.count * time_since_last_event
-
-    def end_of_run_cleanup(self, run_length):
-        """
-        Cleans up and finalises statistics at the end of the simulation run.
-
-        If the simulation ends while resources are still in use or requests are
-        still in the queue, the time between the last recorded event and the
-        simulation end will not have been accounted for. This method fills
-        in that gap, avoiding underestimation of resource usage and queue
-        sizes.
-
-        Arguments:
-            run_length (float):
-                Duration of the simulation run.
-        """
-        # Delay clean up until the entire simulation has completed
-        yield self._env.timeout(run_length)
-        # Update the time-weighted statistics
-        self.update_time_weighted_stats()
+        self.area_resource_busy += self.count * time_since_last_event
 
 
 class Exponential:
@@ -354,7 +335,7 @@ def sample(self, size=None):
 
         Returns:
             float or numpy.ndarray:
-                A single sample if size is None, or an array of samples if 
+                A single sample if size is None, or an array of samples if
                 size is specified.
         """
         return self.rand.exponential(self.mean, size=size)
@@ -576,16 +557,16 @@ def warm_up_complete(self):
         # Delay process until warm-up period has completed
         yield self.env.timeout(self.param.warm_up_period)
 
+        # Reset results collection variables
+        self.init_results_variables()
+
         # If there was a warm-up period, log that this time has passed so we
         # can distinguish between patients before and after warm-up in logs
         if self.param.warm_up_period > 0:
             self.param.logger.log('─' * 10)
             self.param.logger.log(f'{self.env.now:.2f}: Warm up complete.')
             self.param.logger.log('─' * 10)
 
-        # Reset results collection variables
-        self.init_results_variables()
-
     def run(self):
         """
         Runs the simulation for the specified duration.
@@ -604,13 +585,21 @@ def run(self):
         self.env.process(
             self.interval_audit(interval=self.param.audit_interval))
 
-        # Schedule process which will clean up monitoring of resources at the
-        # end of the run
-        self.env.process(self.nurse.end_of_run_cleanup(run_length))
-
         # Run the simulation
         self.env.run(until=run_length)
 
+        # If the simulation ends while resources are still in use or requests
+        # are still in the queue, the time between the last recorded event and
+        # the simulation end will not have been accounted for. Hence, we call 
+        # update_time_weighted_stats() to run for last event --> end.
+        self.nurse.update_time_weighted_stats()
+
+        # Error handling - if there was no data collection period, the
+        # simulation ends before it has a chance to reset the results,
+        # so we do so manually
+        if self.param.data_collection_period == 0:
+            self.init_results_variables()
+
         # Convert list of patient objects into a list that just contains the
         # attributes of each of those patients as dictionaries
         self.results_list = [x.__dict__ for x in self.patients]
@@ -674,7 +663,6 @@ def run_single(self, run):
 
         # Create dictionary recording the run results
         # Currently has two alternative methods of measuring utilisation
-        # TODO: Is mean_q_time_nurse and mean_nurse_q_length the same?
         run_results = {
             'run_number': run,
             'scenario': self.param.scenario_name,
@@ -684,10 +672,10 @@ def run_single(self, run):
             'mean_nurse_utilisation': (model.nurse_time_used /
                                        (self.param.number_of_nurses *
                                         self.param.data_collection_period)),
-            'mean_nurse_utilisation_tw': (model.nurse.resource_busy_time /
+            'mean_nurse_utilisation_tw': (model.nurse.area_resource_busy /
                                           (self.param.number_of_nurses *
                                            self.param.data_collection_period)),
-            'mean_nurse_q_length': (model.nurse.queue_time /
+            'mean_nurse_q_length': (model.nurse.area_n_in_queue /
                                     self.param.data_collection_period)
         }
 

simulation/model.py

@@ -1,5 +1,5 @@
 ,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length
-mean,378.0,2.1667325673589564,10.159320371752361,0.6362147508884318,0.6385838493112572,0.5445692746656905
-std_dev,15.198684153570664,0.7844583147114635,0.4786676748781607,0.02383218108751965,0.024489056062460362,0.19009766156834382
-lower_95_ci,359.12834106644124,1.192698919890132,9.564975952752244,0.606623189633386,0.6081766700485065,0.3085318521535542
-upper_95_ci,396.87165893355876,3.1407662148277806,10.75366479075248,0.6658063121434776,0.6689910285740079,0.7806066971778267
+mean,378.0,2.1667325673589564,10.159320371752361,0.6362147508884318,0.6397764054245421,0.5445692746656905
+std_dev,15.198684153570664,0.7844583147114635,0.4786676748781607,0.02383218108751965,0.024277980144559095,0.19009766156834382
+lower_95_ci,359.12834106644124,1.192698919890132,9.564975952752244,0.606623189633386,0.6096313115299372,0.3085318521535542
+upper_95_ci,396.87165893355876,3.1407662148277806,10.75366479075248,0.6658063121434776,0.6699214993191469,0.7806066971778267