Model is duplicating output of 1 time instead of the 3 required - anylogic

In the model, there are 2 types of agents (FollowUp and NewConsult) that I would like to collect time for upon entering a seize block and upon seizing the resource in order to understand the time waiting in that block while running a parameters variation experiment. Each patient type has their respective variable of timeEnterClerk and the time is added to their respective waitClerkTime statistic. As seen in the output, each agent type is being given a single time, regardless of its the morning (time < 150), afternoon (time > 150), or entire day. I am confused as this is not the case when outputting the average for all agent types as seen in waitClerkTime.add(time() - agent.timeEnterClerk );, waitClerkTimeMorn.add(time() - agent.timeEnterClerk );, and waitClerkTimeAft.add(time() - agent.timeEnterClerk );.
// On Enter
agent.timeEnterClerk = time();
if (agent instanceof FollowUp)
((FollowUp)agent).timeEnterClerkFollowUp = time();
if (agent instanceof NewConsult)
((NewConsult)agent).timeEnterClerkNewConsult = time();
// On Seize
waitClerkTime.add(time() - agent.timeEnterClerk );
if (agent instanceof FollowUp)
waitClerkTimeFollowUp.add(time() - ((FollowUp)agent).timeEnterClerkFollowUp );
if (agent instanceof NewConsult)
waitClerkTimeNewConsult.add(time() - ((NewConsult)agent).timeEnterClerkNewConsult );
if (time() < 150)
waitClerkTimeMorn.add(time() - agent.timeEnterClerk );
if (agent instanceof FollowUp)
waitClerkTimeMornFollowUp.add(time() - ((FollowUp)agent).timeEnterClerkFollowUp );
if (agent instanceof NewConsult)
waitClerkTimeMornNewConsult.add(time() - ((NewConsult)agent).timeEnterClerkNewConsult);
if (time() > 150)
waitClerkTimeAft.add(time() - agent.timeEnterClerk );
if (agent instanceof FollowUp)
waitClerkTimeAftFollowUp.add(time() - ((FollowUp)agent).timeEnterClerkFollowUp );
if (agent instanceof NewConsult)
waitClerkTimeAftNewConsult.add(time() - ((NewConsult)agent).timeEnterClerkNewConsult);
Parameters variation in After Simulation Run:
KneeClinicDataExport.setCellValue(root.waitClerkTime.mean(), 1, row + 1, 1);
KneeClinicDataExport.setCellValue(root.waitClerkTimeMorn.mean(), 1, row + 1, 2);
KneeClinicDataExport.setCellValue(root.waitClerkTimeAft.mean(), 1, row + 1, 3);
KneeClinicDataExport.setCellValue(root.waitClerkTimeFollowUp.mean(), 1, row + 1, 4);
KneeClinicDataExport.setCellValue(root.waitClerkTimeMornFollowUp.mean(), 1, row + 1, 5);
KneeClinicDataExport.setCellValue(root.waitClerkTimeAftFollowUp.mean(), 1, row + 1, 6);
KneeClinicDataExport.setCellValue(root.waitClerkTimeNewConsult.mean(), 1, row + 1, 7);
KneeClinicDataExport.setCellValue(root.waitClerkTimeMornNewConsult.mean(), 1, row + 1, 8);
KneeClinicDataExport.setCellValue(root.waitClerkTimeAftNewConsult.mean(), 1, row + 1, 9);
KneeClinicDataExport.setCellValue(root.waitClerkTimeRevision.mean(), 1, row + 1, 10);
KneeClinicDataExport.setCellValue(root.waitClerkTimeMornRevision.mean(), 1, row + 1, 11);
KneeClinicDataExport.setCellValue(root.waitClerkTimeAftRevision.mean(), 1, row + 1, 12);
row++;

the reason you have this problem is that you are not using brackets for your if statements
In the following case, both statements are executed when the condition is true
if(conditions){
statementA;
statementB;
}
In the following example, only statementA is executed when the condition is true, but statementB is executed always
if(conditions)
statementA;
statementB;

Related

For flexible jobshop problem, getting infeasible solution

wanted to discuss one issue i am getting while using the google ortools.
Problem: status of the solution is coming INFEASIBLE. but solution is feasible as per my understandings.
below is the sample.py used
import collections
from ortools.sat.python import cp_model
class SolutionPrinter(cp_model.CpSolverSolutionCallback):
"""Print intermediate solutions."""
def __init__(self):
cp_model.CpSolverSolutionCallback.__init__(self)
self.__solution_count = 0
def on_solution_callback(self):
"""Called at each new solution."""
print('Solution %i, time = %f s, objective = %i' %
(self.__solution_count, self.WallTime(), self.ObjectiveValue()))
self.__solution_count += 1
def flexible_jobshop():
"""Solve a small flexible jobshop problem."""
# Data part.
jobs = [
[ # Job 0 (machine_id, processingtime, starttime,endtime)
[(0, 65, 0, 72), (1, 65, 0, 72), (2, 65, 0, 72)], # task 0 with 3 alternatives
[(3, 170, 0, 240), (4, 170, 0, 240), (5, 170, 0, 240)] # task 1 with 3 alternatives
],
[ # Job 1
[(0, 25, 0, 120), (1, 25, 0, 120), (2, 25, 0, 120)],
[(3, 168, 0, 288), (4, 168, 0, 288), (5, 168, 0, 288)]
],
[ # Job 2
[(0, 34, 0, 480), (1, 34, 0, 480), (2, 34, 0, 480)],
[(3, 32, 0, 504), (4, 32, 0, 504), (5, 32, 0, 504)]
],
[ # Job 3
[(0, 39, 0, 600), (1, 39, 0, 600), (2, 39, 0, 600)],
[(3, 93, 0, 696), (4, 93, 0, 696), (5, 93, 0, 696)]
]
]
# from data import Data
# jobs = Data().p3
# print(jobs)
num_jobs = len(jobs)
all_jobs = range(num_jobs)
num_machines = 3
all_machines = range(num_machines)
# Model the flexible jobshop problem.
model = cp_model.CpModel()
horizon = 0
for job in jobs:
for task in job:
max_task_duration = 0
for alternative in task:
max_task_duration = max(max_task_duration, alternative[1])
horizon += max_task_duration
print('Horizon = %i' % horizon)
# Global storage of variables.
intervals_per_resources = collections.defaultdict(list)
starts = {} # indexed by (job_id, task_id).
presences = {} # indexed by (job_id, task_id, alt_id).
job_ends = []
# Scan the jobs and create the relevant variables and intervals.
for job_id in all_jobs:
job = jobs[job_id]
num_tasks = len(job)
previous_end = None
for task_id in range(num_tasks):
task = job[task_id]
min_duration = task[0][1]
max_duration = task[0][1]
num_alternatives = len(task)
all_alternatives = range(num_alternatives)
for alt_id in range(1, num_alternatives):
alt_duration = task[alt_id][1]
min_duration = min(min_duration, alt_duration)
max_duration = max(max_duration, alt_duration)
# Create main interval for the task.
suffix_name = '_j%i_t%i' % (job_id, task_id)
# start = model.NewIntVar(task[0][2], horizon, 'start' + suffix_name)
start = model.NewIntVar(0, horizon, 'start' + suffix_name)
duration = model.NewIntVar(min_duration, max_duration,
'duration' + suffix_name)
end = model.NewIntVar(0, horizon, 'end' + suffix_name)
# end = model.NewIntVar(task[0][3], horizon, 'end' + suffix_name)
interval = model.NewIntervalVar(start, duration, end,
'interval' + suffix_name)
# Store the start for the solution.
starts[(job_id, task_id)] = start
# Add precedence with previous task in the same job.
if previous_end is not None:
model.Add(start >= previous_end)
previous_end = end
# Create alternative intervals.
if num_alternatives > 1:
l_presences = []
for alt_id in all_alternatives:
alt_suffix = '_j%i_t%i_a%i' % (job_id, task_id, alt_id)
l_presence = model.NewBoolVar('presence' + alt_suffix)
l_start = model.NewIntVar(0, horizon, 'start' + alt_suffix)
l_duration = task[alt_id][0]
l_end = model.NewIntVar(0, horizon, 'end' + alt_suffix)
l_interval = model.NewOptionalIntervalVar(
l_start, l_duration, l_end, l_presence,
'interval' + alt_suffix)
l_presences.append(l_presence)
# Link the master variables with the local ones.
model.Add(start == l_start).OnlyEnforceIf(l_presence)
model.Add(duration == l_duration).OnlyEnforceIf(l_presence)
model.Add(end == l_end).OnlyEnforceIf(l_presence)
# Add the local interval to the right machine.
intervals_per_resources[task[alt_id][1]].append(l_interval)
# Store the presences for the solution.
presences[(job_id, task_id, alt_id)] = l_presence
# Select exactly one presence variable.
model.AddExactlyOne(l_presences)
else:
intervals_per_resources[task[0][1]].append(interval)
presences[(job_id, task_id, 0)] = model.NewConstant(1)
job_ends.append(previous_end)
# Create machines constraints.
for machine_id in all_machines:
intervals = intervals_per_resources[machine_id]
if len(intervals) > 1:
model.AddNoOverlap(intervals)
# Makespan objective
makespan = model.NewIntVar(0, horizon, 'makespan')
model.AddMaxEquality(makespan, job_ends)
model.Minimize(makespan)
# Solve model.
solver = cp_model.CpSolver()
solution_printer = SolutionPrinter()
status = solver.Solve(model, solution_printer)
# Print final solution.
for job_id in all_jobs:
print('Job %i:' % job_id)
for task_id in range(len(jobs[job_id])):
start_value = solver.Value(starts[(job_id, task_id)])
machine = -1
duration = -1
selected = -1
for alt_id in range(len(jobs[job_id][task_id])):
if solver.Value(presences[(job_id, task_id, alt_id)]):
duration = jobs[job_id][task_id][alt_id][0]
machine = jobs[job_id][task_id][alt_id][1]
selected = alt_id
print(
' task_%i_%i starts at %i (alt %i, machine %i, duration %i)' %
(job_id, task_id, start_value, selected, machine, duration))
print('Solve status: %s' % solver.StatusName(status))
print('Optimal objective value: %i' % solver.ObjectiveValue())
print('Statistics')
print(' - conflicts : %i' % solver.NumConflicts())
print(' - branches : %i' % solver.NumBranches())
print(' - wall time : %f s' % solver.WallTime())
flexible_jobshop()
for this data set, it is giving infeasible solution.
Not sure what i m doing wrong here.
please guide.

How to pick an element from matrix (list of list in python) based on decision variables (one for row, and one for column) | OR-Tools, Python

I am new to constraint programming and OR-Tools. A brief about the problem. There are 8 positions, for each position I need to decide which move of type A (move_A) and which move of type B (move_B) should be selected such that the value achieved from the combination of the 2 moves (at each position) is maximized. (This is only a part of the bigger problem though). And I want to use AddElement approach to do the sub setting.
Please see the below attempt
from ortools.sat.python import cp_model
model = cp_model.CpModel()
# value achieved from combination of different moves of type A
# (moves_A (rows)) and different moves of type B (moves_B (columns))
# for e.g. 2nd move of type A and 3rd move of type B will give value = 2
value = [
[ -1, 5, 3, 2, 2],
[ 2, 4, 2, -1, 1],
[ 4, 4, 0, -1, 2],
[ 5, 1, -1, 2, 2],
[ 0, 0, 0, 0, 0],
[ 2, 1, 1, 2, 0]
]
# 6 moves of type A
num_moves_A = len(value)
# 5 moves of type B
num_moves_B = len(value[0])
num_positions = 8
type_move_A_position = [model.NewIntVar(0, num_moves_A - 1, f"move_A[{i}]") for i in range(num_positions)]
type_move_B_position = [model.NewIntVar(0, num_moves_B - 1, f"move_B[{i}]") for i in range(num_positions)]
value_position = [model.NewIntVar(0, 10, f"value_position[{i}]") for i in range(num_positions)]
# I am getting an error when I run the below
objective_terms = []
for i in range(num_positions):
model.AddElement(type_move_B_position[i], value[type_move_A_position[i]], value_position[i])
objective_terms.append(value_position[i])
The error is as follows:
Traceback (most recent call last):
File "<ipython-input-65-3696379ce410>", line 3, in <module>
model.AddElement(type_move_B_position[i], value[type_move_A_position[i]], value_position[i])
TypeError: list indices must be integers or slices, not IntVar
In MiniZinc the below code would have worked
var int: obj = sum(i in 1..num_positions ) (value [type_move_A_position[i], type_move_B_position[i]])
I know in OR-Tools we will have to create some intermediary variables to store results first, so the above approach of minizinc will not work. But I am struggling to do so.
I can always create a 2 matrix of binary binary variables one for num_moves_A * num_positions and the other for num_moves_B * num_positions, add re;evant constraints and achieve the purpose
But I want to learn how to do the same thing via AddElement constraint
Any help on how to re-write the AddElement snippet is highly appreciated. Thanks.
AddElement is 1D only.
The way it is translated from minizinc to CP-SAT is to create an intermediate variable p == index1 * max(index2) + index2 and use it in an element constraint with a flattened matrix.
Following Laurent's suggestion (using AddElement constraint):
from ortools.sat.python import cp_model
model = cp_model.CpModel()
# value achieved from combination of different moves of type A
# (moves_A (rows)) and different moves of type B (moves_B (columns))
# for e.g. 2 move of type A and 3 move of type B will give value = 2
value = [
[-1, 5, 3, 2, 2],
[2, 4, 2, -1, 1],
[4, 4, 0, -1, 2],
[5, 1, -1, 2, 2],
[0, 0, 0, 0, 0],
[2, 1, 1, 2, 0],
]
min_value = min([min(i) for i in value])
max_value = max([max(i) for i in value])
# 6 moves of type A
num_moves_A = len(value)
# 5 moves of type B
num_moves_B = len(value[0])
# number of positions
num_positions = 5
# flattened matrix of values
value_flat = [value[i][j] for i in range(num_moves_A) for j in range(num_moves_B)]
# flattened indices
flatten_indices = [
index1 * len(value[0]) + index2
for index1 in range(len(value))
for index2 in range(len(value[0]))
]
type_move_A_position = [
model.NewIntVar(0, num_moves_A - 1, f"move_A[{i}]") for i in range(num_positions)
]
model.AddAllDifferent(type_move_A_position)
type_move_B_position = [
model.NewIntVar(0, num_moves_B - 1, f"move_B[{i}]") for i in range(num_positions)
]
model.AddAllDifferent(type_move_B_position)
# below intermediate decision variable is created which
# will store index corresponding to the selected move of type A and
# move of type B for each position
# this will act as index in the AddElement constraint
flatten_index_num = [
model.NewIntVar(0, len(flatten_indices), f"flatten_index_num[{i}]")
for i in range(num_positions)
]
# another intermediate decision variable is created which
# will store value corresponding to the selected move of type A and
# move of type B for each position
# this will act as the target in the AddElement constraint
value_position_index_num = [
model.NewIntVar(min_value, max_value, f"value_position_index_num[{i}]")
for i in range(num_positions)
]
objective_terms = []
for i in range(num_positions):
model.Add(
flatten_index_num[i]
== (type_move_A_position[i] * len(value[0])) + type_move_B_position[i]
)
model.AddElement(flatten_index_num[i], value_flat, value_position_index_num[i])
objective_terms.append(value_position_index_num[i])
model.Maximize(sum(objective_terms))
# Solve
solver = cp_model.CpSolver()
status = solver.Solve(model)
solver.ObjectiveValue()
for i in range(num_positions):
print(
str(i)
+ "--"
+ str(solver.Value(type_move_A_position[i]))
+ "--"
+ str(solver.Value(type_move_B_position[i]))
+ "--"
+ str(solver.Value(value_position_index_num[i]))
)
The below version uses AddAllowedAssignments constraint to achieve the same purpose (per Laurent's alternate approach) :
from ortools.sat.python import cp_model
model = cp_model.CpModel()
# value achieved from combination of different moves of type A
# (moves_A (rows)) and different moves of type B (moves_B (columns))
# for e.g. 2 move of type A and 3 move of type B will give value = 2
value = [
[-1, 5, 3, 2, 2],
[2, 4, 2, -1, 1],
[4, 4, 0, -1, 2],
[5, 1, -1, 2, 2],
[0, 0, 0, 0, 0],
[2, 1, 1, 2, 0],
]
min_value = min([min(i) for i in value])
max_value = max([max(i) for i in value])
# 6 moves of type A
num_moves_A = len(value)
# 5 moves of type B
num_moves_B = len(value[0])
# number of positions
num_positions = 5
type_move_A_position = [
model.NewIntVar(0, num_moves_A - 1, f"move_A[{i}]") for i in range(num_positions)
]
model.AddAllDifferent(type_move_A_position)
type_move_B_position = [
model.NewIntVar(0, num_moves_B - 1, f"move_B[{i}]") for i in range(num_positions)
]
model.AddAllDifferent(type_move_B_position)
value_position = [
model.NewIntVar(min_value, max_value, f"value_position[{i}]")
for i in range(num_positions)
]
tuples_list = []
for i in range(num_moves_A):
for j in range(num_moves_B):
tuples_list.append((i, j, value[i][j]))
for i in range(num_positions):
model.AddAllowedAssignments(
[type_move_A_position[i], type_move_B_position[i], value_position[i]],
tuples_list,
)
model.Maximize(sum(value_position))
# Solve
solver = cp_model.CpSolver()
status = solver.Solve(model)
solver.ObjectiveValue()
for i in range(num_positions):
print(
str(i)
+ "--"
+ str(solver.Value(type_move_A_position[i]))
+ "--"
+ str(solver.Value(type_move_B_position[i]))
+ "--"
+ str(solver.Value(value_position[i]))
)

Repeat stream when done

hello I am using a stream on my app to get some data. at the end of data the stream stop, what i want is to start again every time stream is done
final stream = Stream.periodic(kDuration, (count) => count)
.take(kLocations.length);
stream.listen((value) => newLocationUpdate(value)
I was searching for hours and didnt find a good solution
Calling .take(kLocations.length) will cause the stream to close once that number of elements have been emitted. For example:
final stream = Stream.periodic(kDuration, (count) => count).take(3);
stream.listen(print); // prints 0, 1, 2, then stops
If instead, you want this to repeat (i.e. emit 0, 1, 2, 0, 1, 2, etc), you can use the modulus operator (%):
final stream = Stream.periodic(kDuration, (count) => count % 3);
stream.listen(print); // prints 0, 1, 2, 0, 1, 2, 0, 1, 2, ...

ThinkinSphinx query not working with sphinx_select with four conditions

I'm trying to use ThinkingSphinx to return records that have a start date within a range OR an end date within the same range, basically any record that starts or ends within this range.
To do this, I am using a computed attribute and sphinx_select as per the documentation in combination with what this post suggests for date ranges, as follows (assume there are two records, record_a starts outside the range, but ends within the range and record_b starts and ends within the range):
with_display = "*, IF(start_at >= #{range_start.to_i}, 1, 0) + " +
"IF(start_at <= #{range_end.to_i}, 1, 0) + " +
"IF(end_at >= #{range_start.to_i}, 10, 0) + " +
"IF(end_at <= #{range_end.to_i}, 10, 0) AS display"
{
sphinx_select: with_display,
with: {'display' => [2, 20, 22]},
}
=> [record_b]
However, if I only use the start_at conditions, I get one record, and if I use only the end_at conditions, it returns both records.
with_display = "*, IF(start_at >= #{range_start.to_i}, 1, 0) + " +
"IF(start_at <= #{range_end.to_i}, 1, 0) AS display"
=> [record_b]
with_display = "*, IF(end_at >= #{range_start.to_i}, 10, 0) + " +
"IF(end_at <= #{range_end.to_i}, 10, 0) AS display"
=> [record_a, record_b]
If I'm understanding this correctly, having all four conditions, should result in both record_a and record_b being returned, since record_a should have a display value of 20, while record_b should have a display value of 22.
Am I missing something?
I just realized my math was wrong, given the cases I want to handle:
record_a will have a display of 21
record_b will have a display of 22
What I needed to do was change my array to:
{
sphinx_select: with_display,
with: {'display' => [12, 21, 22]},
}
in order the handle the cases of records that end within the range (21), records that start within range (12), and records than start and end within the range (22)

Why doesn't this coffee-script print out?

So when the index is 0, I want to print it out:
a = [ 1, 2, 3 ]
for i of a
if i == 0
console.log a[i]
But there is no output.
i == 0 is never true...
i returns the index as a string, if you parse them as an integer, it would work
a = [ 1, 2, 3 ]
for i of a
if parseInt(i) == 0
console.log a[i]
It's because i will only be 1, 2 or 3, as you loop over the items in a, not the index numbers.
This works the way you described above:
a = [ 1, 2, 3 ]
for i in [0..a.length]
if i == 0
console.log a[i]
You shouldn't use of to loop over an array, you should use in. From the fine manual:
Comprehensions can also be used to iterate over the keys and values in an object. Use of to signal comprehension over the properties of an object instead of the values in an array.
yearsOld = max: 10, ida: 9, tim: 11
ages = for child, age of yearsOld
"#{child} is #{age}"
So you're trying to iterate over the properties of an array object, not its indexes.
You should use one of these for your loop:
for e, i in a
if(i == 0)
console.log(a[i])
for e, i in a
console.log(e) if(i == 0)
console.log(e) for e, i in a when i == 0
#...
Or, since you have an array and a numeric index, why not just skip the loop and get right to the point:
console.log(a[0])