I need to model a waiting line in a call centre in AnyLogic. This is what I don't understand. It says:
If all of the service representatives are busy, an arriving customer is placed on hold, but ties up on the phone lines.
I am not sure what block or how to model customers waiting. Can someone help me? Thank You!
Here is my solution, although I'm absolutely sure other methods exist.
To represent an arrival rate of 15/hr, use a source block with arrivals defined by rate, with the rate set to 15 per hour.
To represent 24 phone lines and 3 service reps, use a queue block (callsWaiting) followed by a delay block (service). The queue block should have capacity = 21 and the delay block should have capacity = 3 with a delay time of exponential(0.1,0) minutes representing the exponential service time (with mean 10 min).
To represent losing calls when all of the phone lines are tied up, place a selectOutput block before the callsWaiting queue and set its condition to: callsWaiting.canEnter(). It will return false if the queue is at maximum capacity. On the false branch for that selectOutput, place a sink block for dropped calls.
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Hi I am a beginner at AnyLogic. I would appreciate if someone could help me. I have the task to create a call center model in which I have 3 customer service representatives, Customers calls arrive to the call center according to a Poisson process with rate λ = 15 per, The customer service times are exponentially distributed with a mean of 10 minutes. There are 24 phone lines and in case all lines are busy then the call is lost. What is the easiest way to limit queue space in the waiting area?
I'm quite a beginner in Anylogic, so maybe my question is moronic.
What I'm trying to do is to create a model of M/M/1 with reneging, i.e. an agent waits in queue for a (random) amount of time and then exits the queue via timeOut.
Also, I've inserted timeMeasureStart and timeMeasureEnd in order to find the mean time spent in queue for the agents which left the queue by timeOut: MM1 with reneging.
I've tried to set constant time, uniform, triangular and normal random time - the mean time (and the deviation) was as the theory predicts.
But when I tried to use exponential (and weibull), the mean time was significantly less then the mean value of the distribution.
I wonder if someone could explain to me why it happens?
I have to calculate the probability of a customer waiting more than 5 min in the queue in anylogic. I've already implemented the timemeasureend and-start block, but I have seriously no clue how to compute the probability of a customer waiting longer than 5min? What do I need to write where? Help is highly appreciated
Thanks!
There are two objects in a process modelling library: TimeMeasureStart and TimeMeasureEnd. You can put those around a queue and record the time for each entity after they exit the queue. Save that time to a Statistics object and from there your probability if waiting more that 5 mins should be (no of samples over 5 mins)/(total number of entities). Also, make sure that your model time unit is set to minute to make it easier.
I am currently working on a simple simulation that consists of 4 manufacturing workstations with different processing times and I would like to measure the WIP inside the system. The model is PennyFab2 in case anybody knows it.
So far, I have measured throughput and cycle time and I am calculating WIP using Little's law, however the results don't match he expectations. The cycle time is measured by using the time measure start and time measure end agents and the throughput by simply counting how many pieces flow through the end of the simulation.
Any ideas on how to directly measure WIP without using Little's law?
Thank you!
For little's law you count the arrivals, not the exits... but maybe it doesn't make a difference...
Otherwise.. There are so many ways
you can count the number of agents inside your system using a RestrictedAreaStart block and use the entitiesInside() function
You can just have a variable that adds +1 if something enters and -1 if something exits
No matter what, you need to add the information into a dataset or a statistics object and you get the mean of agents in your system
Little's Law defines the relationship between:
Work in Process =(WIP)
Throughput (or Flow rate)
Lead Time (or Flow Time)
This means that if you have 2 of the three you can calculate the third.
Since you have a simulation model you can record all three items explicitly and this would be my advice.
Little's Law should then be used to validate if you are recording the 3 values correctly.
You can record them as follows.
WIP = Record the average number of items in your system
Simplest way would be to count the number of items that entered the system and subtract the number of items that left the system. You simply do this calculation every time unit that makes sense for the resolution of your model (hourly, daily, weekly etc) and save the values to a DataSet or Statistics Object
Lead Time = The time a unit takes from entering the system to leaving the system
If you are using the Process Modelling Library (PML) simply use the timeMeasureStart and timeMeasureEnd Blocks, see the example model in the help file.
Throughput = the number of units out of the system per time unit
If you run the model and your average WIP is 10 units and on average a unit takes 5 days to exit the system, your throughput will be 10 units/5 days = 2 units/day
You can validate this by taking the total units that exited your system at the end of the simulation and dividing it by the number of time units your model ran
if you run a model with the above characteristics for 10 days you would expect 20 units to have exited the system.
I am trying to build a network simulation (aloha like) where n nodes decide at any instant whether they have to send or not according to an exponential distribution (exponentially distributed arrival times).
What I have done so far is: I set a master clock in a for loop which ticks and any node will start sending at this instant (tick) only if a sample I draw from a uniform [0,1] for this instant is greater than 0.99999; i.e. at any time instant a node has 0.00001 probability of sending (very close to zero as the exponential distribution requires).
Can these arrival times be considered exponentially distributed at each node and if yes with what parameter?
What you're doing is called a time-step simulation, and can be terribly inefficient. Each tick in your master clock for loop represents a delta-t increment in time, and in each tick you have a laundry list of "did this happen?" possible updates. The larger the time ticks are, the lower the resolution of your model will be. Small time ticks will give better resolution, but really bog down the execution.
To answer your direct questions, you're actually generating a geometric distribution. That will provide a discrete time approximation to the exponential distribution. The expected value of a geometric (in terms of number of ticks) is 1/p, while the expected value of an exponential with rate lambda is 1/lambda, so effectively p corresponds to the exponential's rate per whatever unit of time a tick corresponds to. For instance, with your stated value p = 0.00001, if a tick is a millisecond then you're approximating an exponential with a rate of 1 occurrence per 100 seconds, or a mean of 100 seconds between occurrences.
You'd probably do much better to adopt a discrete-event modeling viewpoint. If the time between network sends follows the exponential distribution, once a send event occurs you can schedule when the next one will occur. You maintain a priority queue of pending events, and after handling the logic of the current event you poll the priority queue to see what happens next. Pull the event notice off the queue, update the simulation clock to the time of that event, and dispatch control to a method/function corresponding to the state update logic of that event. Since nothing happens between events, you can skip over large swatches of time. That makes the discrete-event paradigm much more efficient than the time step approach unless the model state needs updating in pretty much every time step. If you want more information about how to implement such models, check out this tutorial paper.