I am developing a logistics simulation in the factory by Anylogic. It's a pick up and delivery problem, where the AGVs need to pick up the parcel and deliver to the target location. All the AGVs are traveling following paths. The paths have different speed limits.
My goal is to reduce the time of traffic jam or waiting time for jobs to be picked up.
I have the leading time, job delivered time - job generated time.
But I from here, I want to identify the time of traffic jam or waiting time.
Is there a way to calculate the time from one spot to the other considering different speed limit of paths without waiting time or traffic jam? So that I could subtract this from leading time.
Let me know if I need to clarify something.
There is no build-in way to do this, you have to do it yourself. I have 3 ideas:
You compute this mathematically in the model yourself, i.e. write a function that computes the length of the total path and you have the ideal speed already, voila
You run a separate experiment and turn off all speed limits and other traffic: record the time in that ideal case and use that to compare
Similarly, you could do this in the same experiment during a warmup period: drive a fake transporter along the path and compute the perfect durations
Related
We’re generating the data that we might get from a shop floor to run, test, and validate our machine learning models. We first have here a discrete event simulation model for our manufacturing system. Each production order is seen as an agent, which then goes through different processes with a queue (waiting time) and delays (firstly production time, secondly logistics time).
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But sometimes we have one process, for example, printing (code 5A, after the second Select5Output), with three different machines, which do not have a particular capacity. It’s time when we divide our order into parts and send them to those machines (very randomly, subjectively).
The data we take is from flowchart_process_states_log in Database.
The data we take is from flowchart_process_states_log in Database.
My questions here are:
How can we define the number of products in each order? Ex. we’re printing card, for one order it may be 10k, for another 8k or 33k. Can we define it as agent’s parameter? Then how can we vary them (stochastically, no exact number needed).
How can we split those 10k cards into three different machines? And then how to get back an complete agent with 10k? The Agent ID should remain the same as we trace and analyse them in ML model. Is it reasonable to see an order as an agent?
How can we multiply the number of our agent after a process? Ex. After cutting 10k pieces we have 20k.
We have the distribution for delay ex. triangle distribution. But we want some disturbances, when it suddenly takes 2 days for that delay instead of 3-4 hours as normal. How to do it?
Thank you in advance for your effort. Every help is highly appreciated, because we're here and learning together. Thank you !!
My question is:
Is there a simple an proper way to create arrivals for multiple deliverylocations with the same poisson rate without using a source block for every delivery location?
So an example to make it perhaps a bit more understandable:
I've 30 delivery location nodes (node1, node2, node3, node4 etc). For all those 30 delivery location there should be something transported to those nodes with the same poission arrival rate for simplicity say 1 and they all have different intermediate points where they have to pass (so say delivery location node1, first need to go to intermediate point2 and than to location node 1, see figure for example of database values).
Now ofcourse I can create 30 source blocks with this arrival rate and the intermediate points as parameters of the agent created in that source, but this is kind of time intensive, so is there a quick solution to model this quickly?
Since it happens randomly, arrivals according to database can not be used, since there is not a specified time of arrival it just happens randomly based on a poisson rate.
I used MATLAB to simulate the cascading failure of two interdependent networks/Layers (I generated the two layers based on small world - watts strogatz algorithm).
My code works fine but it is not time dependent.
I want to have time steps, for example, the initial attack on one node happens at t1 then after some time the next vulnerable nodes get failed at different time t2 and so on for the other failure events. My code emulates only telecommunication nodes ( all events happen instantaneously), I want it to work for other logistic networks, say social networks for example, where the timestamp for every event might be in minutes or hours. Your thoughts and ideas would appreciated.
Note: I can provide my code if this helps.
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.
Hi all I am a new SUMO user. I am having simulation iteratively with DUAROUTER and SUMO. The simulation consist of 20000 trips in Singapore network and it's very slow, took one hour and more to complete one simulation.
Anyone knows any way to speed up the process? I need to do 50 iterations. 1 hour per iteration is too slow.
My commands are as follows:
duarouter --net-file sg_left_v1.net.xml --trip-files trips20000_merged.trips.xml --output-file 0.20000.route.xml --ignore-errors true --no-warnings true --repair true
sumo -c simulation_sg_20000.sumocfg --tripinfo-output 0.20000.trip.output.xml --no-warnings true --tripinfo-output.write-unfinished true --vehroute-output 0.20000.individual.output.xml --link-output 0.20000.link-state.output.xml
The number X in X.20000.something.xml is increased on each iteration by my python code.
Thank you all in advance.
There are different things you can do to speed up the process by analyzing the bottlenecks. I would do the following:
Check whether the traffic flow is smooth. If there are big jams piling up the simulation slows down.
Do the vehicles depart at the times you expect them too. Even is there no visible jam, the backlog slows the simulation down. A good indicator is that vehicles which have an intended departure time near the end of the simulation, take much longer to depart (it's also in the tripinfo).
Recheck whether you need all outputs. To get a feeling whether it helps disable them one by one and have a look at the running time.
3a. Extend SUMO to aggregate your data. It is open source after all, so if the outputs are the bottleneck, aggregate inside the simulation.
Think about parallel execution. Maybe you do not need to start the iterations one after another?
Make the scenario smaller.
To accelerate the simulation, you will need to pass a parameter to Sumo called step-length
which a ratio of sumoTime / realWorldtime.
sumo your-other-args-here --step-length 1
It should enable you the get the wanted result