I am new to optaplanner, and right now I focus on trying to understand the project job scheduling. I trying to run this examples using the sample data from optaplanner manual like in this picture below:
I have some question about the domain classes in this example :
What is the difference of GlobalResource and LocalResource? In the examples, all the resource is GlobalResource right? Then what the use of LocalResource?
There are 3 JobType: SOURCE, STANDARD, SINK, what is the meaning each one of them? It is SOURCE mean the job should be the first to start before the others? STANDARD mean it is should be run after the predecessor job finished but not after the SINK job? SINK mean it is the last job to do after all job finished?
What is the meaning of property releaseDate and criticalPathDuration in Project class? If we related it with the picture above, what is the value for project Book1 and Book2?
What is the meaning of requirement in ResourceRequirement?
I will be really thankful if someone can help me create the xml sample data like in optaplanner distribution, cause it will help me more faster to understand this example. Thanks & Regards.
A LocalResource belongs to a specific Project, a GlobalResource is shared between the projects.
So a LocalResource only has to be worry about being used by other jobs in the same Project too, while a GlobalResource has to worry about all other tasks.
That's an implementation trick. The source and sink jobs are dummy's basically. Because a project might start with multiple jobs in parallel, a SOURCE job is put in front of it, to have a single root. Same for the end: it can end with multiple, so a SINK job is put after it, to have a single tail. This makes it easier and faster to determine makespan etc.
IIRC, releaseDate is the first date we are allowed to start the first job. For example: you have to create a book, but you 'll only get the actual final content next Monday, so the releaseDate is next Monday (you can't start any work before that date).
The criticalPathDuration is a theoretical minimum duration (if we can happily ignore resources IIRC). For example: if job A takes 5 days and job B takes 2 days and B has to be done AFTER A, then the critical path duration is 7 days. Adding job C which takes 1 day and can be done in parallel with the others, don't affect that.
ResourceRequirement is the many2many relationship between ExecutionMode and Resource. Remember that ExecutionMode belongs to a specific Job. For example: doing job A in executionMode A1 requires 1 laborers and 5 days. Doing job A in executionMode A2 requires 2 laborers and 3 days.
Related
I need to start a job every 30 minutes, but before a new job is being started I want the old but same job being terminated. This is to make sure the job always fetches the newest data file which is constantly being updated.
Right now I'm using the BlockingScheduler paired with my own condition to stop the job (stop job if processed 1k data etc.), I was wondering if APScheduler supports this "only 1 job at the same time and stop old one before new one" behavior natively
I've read the docs but I think the closest is still the default behavior which equals max_instances=1, this just prevents new jobs firing before the old job finishes, which is not what I'm looking for.
Any help is appreciated. Thanks!
After further research I came to a conclusion that this is not supported natively in APScheduler, but by inspired by
Get number of active instances for BackgroundScheduler jobs
, I modified the answer into a working way of detecting the number of current running instances of the same job, so when you have a infinite loop/long task executing, and you want the new instance to replace the old instance, you can add something like
if(scheduler._executors['default']._instances['set_an_id_you_like'] > 1):
# if multiple instances break loop/return
return
and this is what should look like when you start:
scheduler = BlockingScheduler(timezone='Asia/Taipei')
scheduler.add_job(main,'cron', minute='*/30', max_instances=3, next_run_time=datetime.now(),\
id='set_an_id_you_like')
scheduler.start()
but like the answer in the link, please refrain from doing this if someday there's a native way to do this, currently I'm using APScheduler 3.10
This method at least doesn't rely on calculating time.now() or datetime.datetime.now() in every iteration to check if the time has passed compared when the loop started. In my case since my job runs every 30 minutes, I didn't want to calculate deltatime so this is what I went for, hope this hacky method helped someone that googled for a few days to come here.
Want to sequence a set of tasks with some rules. Each task has an index (the PlanningVariable) to indicate its sequence, its range is from 1 to n. One rule needs to minimize the sum of a shadow variable of all tasks, and it's meaningful only when the sum is calculated from index 1 consecutively to a larger number, say from 1 to 5. It's useless to sum say 2,4,8. Question: Is there any way to force optaplanner to assign 1 to a task and then 2 and then 3 ... to get potential solutions? no waste in this way.
Take a look at the task assigning example in optaplanner-examples (sources are in zip download in green button on optaplanner.org). See this video.
It uses a CHAINED variable to assign tasks to a (linked) list basically. Then use a #CustomShadowVariable to calculate the index in that chain for each Task.
In a future version of optaplanner, we'll support an Employee having a List<Task> and a Task having a #IndexShadowVariable, which will be a much simplier model. But meanwhile you'll have to work with the chained var approach.
I have a report which creates a background job. The user can decide if the job should be periodic or not. Now I want to show information about the actual job. So how can I find out the JOBCOUNT of the following job (periodic) after the old one was executed?
I guess SAP would store that information only if it's needed for internal operations.
I think there's no need, so you won't find that information stored anywhere.
You might do an approximation yourself by searching the currently-scheduled job which has its creation date/time (TBTCO/TBTCS) close to the end of a previous one (TBTCO), with same characteristics (including it(s) step(s) in table TBTCP)... You may get inspired from a few programs prefixed BTCAUX (04, 13).
If you do this piece of code, don't hesitate to post it as a separate answer, that could be very helpful for future visitors.
You can use BP_JOB_SELECT FM for that, it mainly resembles SM37 selection parameters.
Set JOBSELECT_DIALOG param to N to omit GUI screen and fill in job name into JOBSEL_PARAM_IN-JOBNAME param, these are the only two mandatory parameters.
The JOBCOUNT value resides in JOBSELECT_JOBLIST table:
I am new to OctaPlanner.
I want to build a solution where I will nave number of locations to deliver items from one single location and also I want to use openmap distance data for calculating the distance.
Initially I used jsprit, but for more than 300 deliveries, it takes more than 8 minutes with 20 threads. Thats why I am trying to use Octa planner.
I want to map 1000 deliveries within 1 minute.
Does any one know any reference code or reference material which I can start using?
Thanks in advance :)
CVRPTW is a standard example, just open the examples app, vehicle routing and then import one of the belgium datasets with timewindows. The code is in the zip too.
To scale to 1k deliveries and especially beyond, you'll want to use "Nearby selection" (see reference manual), which isn't on by default but which makes a huge difference.
Good afternoon and happy Friday, folks
I’m trying to automate a placement simulation of youth into residential treatment where they will have the highest likelihood of success. Success is operationalized as “not recidivating” within 3 years of entering treatment. Equations predicting recidivism have been generated for each location, and the equations have been applied to each individual in the scenario (based on youth characteristics like risk, age, etc., LOS). Each youth has predicted success rates for every location, which throws in a wrench: youth are not qualified for all of the treatment facilities for which they have predicted success rates. Indeed, treatment locations have differing, yet overlapping qualifications.
Let’s take a made-up example. Johnny (ID # 5, below) is a 15-year-old boy with drug charges. He could have “predicted success rates” of 91% for location A, 88% for location B, 50% for location C, and 75% for location D. Johnny is most likely to be successful (i.e., not recidivate within three years of entering treatment) if he is treated at location A; unfortunately, location A only accepts youth who are 17 years old or older; therefore, Johnny would not qualify for treatment here. Alternatively, for Johnny, location B is the next best location. Let us assume that Johnny is qualified for location B, but that all of location-B beds are filled; so, we must now look to location D, as it is now Johnny’s “best available” option at 75%.
The score so far: We are matching youth to available beds in location for which they qualify and might enjoy the greatest likelihood of success. Unfortunately, each location only has a certain number of available beds, and the number of available beds different across locations. The qualifications of entry into treatment facilities differ, yet overlap (e.g., 12-17 year-olds vs 14-20 year-olds).
In order to simulate what placement decisions might look like based on success rates, I went through the scenario describe above for over 400 youth, by hand, in excel. It took me about a week. I’d like to use PROC SQL imbedded in a SAS MACRO to automate these placement scenarios with the ultimate goals of a) obtain the ability to bootstrap iterations in order to examine effect sizes across distributions, b) save time, and c) prevent further brain damage from banging my head again desk and wall in frustration whilst doing this by hand. Whilst never having had the necessity—nay—the privilege of using SQL in my typical roll as a researcher, I believe that this time has now come to pass and I’m excited about it! Honestly. I believe it has the capacity I’m looking for. Unfortunately, it is beating the devil out of me!
Here’s what I’ve got cookin’ so far: I want to create and automate the placement simulation with the clever use of merging/joining/switching/or something like that.
I have two datasets (tables). The first dataset contains all of the youth information (one row per youth; several columns with demographics, location ranks, which correspond to the predicted success rates). The order of rows in the youth dataset (was/will be randomly generated (to simulate the randomness with which youth enter the system and are subsequently place into treatment). Note that I will be “cleaning” the youth dataset prior to merging such that rank-column cells will only be populated for programs for which a respective youth qualifies. This should take the “does the youth even qualify for the program” problem out of the equation.
However, it still leaves the issue of availability left to be contended with in the scenario.
The second dataset containing the treatment facility beds, with each row corresponding to an available bed in one of the treatment location; two columns contain bed numbers and location names. Each bed (row) has only one location cell populated, but locations will populate several cells.
Thus, in descending order, I want to merge each youth row with the available bed that represents his/her best chance of success, and so the merge/join/switch/thing should take place
on youth.Rank1= distinct TF.Location,
and if youth.Rank1≠ TF.location then
merge on youth.Rank2= TF.location,
if youth.Rank2≠ TF.location then merge at
youth.Rank3 = TF.location, etc.
Put plainly: “Merge on rank1 unless rank1 location is no longer available, then merge on rank2, unless rank2 location is no longer available, and on down the line, etc., etc., until all option are exhausted and foster care (i.e., alternative services). Is the only option.
I’ve had no success getting this to work. I haven’t even been successful getting the union function to work. About the only successful thing I’ve done in SQL so far is create a view of a single dataset. It’s pretty sad. I’ve been following this guidance, but I get hung up around the “where” command:
proc sql; /Calls the SQL procedure*/;
create table x as /*Tells SAS to create a table called x*/
select /*Specifies the column(s) to be selected*/
from /*Specificies the tables(s) (data sets) to be queried*/
where /*Subjests the data based on a condition*/
group by /*Classifies the data into groups based on the specified
column(s)*/
order by /*Sorts the resulting rows observations) by the specified
column(s)*/
; quit; /*Ends the proc sql procedure*/
Frankly, I’m stuck and I could use some advice. This greenhorn in me is in way over his head.
I appreciate any help or guidance anyone might lend.
Cheers!
P
The process you describe (and to be honest I skiped to the end so I might of missed something) does not lend itself to SQL because each step could affect the results of the next one. However, you want to get the most best results for the most kids. (I think a lot of that text was to convince us how important it is to help out). You don't actually give us anything we can really use to help since you don't give any details of your data model, your data, or expected results. There really is no way to answer this question. But I don't care -- I'm going to go forward with some suggestions because it is a friday and I've never done a stream of consciousness answer to a stream of consciousness question before. I will suggest you don't formulate your solution just in sql, but instead use a higher level program and engage is a process like the one described below -- because this a DB questions I've noted the locations where the DB might be involved.
Generate a list kids (this can be in a table -- called NEEDY-KID)
Have a list of locations to assign (this can also be a table LOCATION)
Run your matching for best fit from KID to location -- at this point don't worry about assign more than one kid to a location -- there can be duplicates (put this in table called KID2LOC using a query)
Check KID2LOC for locations assigned twice -- use some method to remove the duplicate ones so each loc is only assigned once. (remove from the KID2LOC using a query)
Prune the LOCATION list to remove assigned locations (once again -- a query)
If kids exist without a location go to 3 with new pruned location list.
Done.