I am searching for algorithm and jsprit or optaplanner project seems to be in order to resolve my problem. We only work with Java.
I have take a look at jsprit bicycle example, an entry point, and now I need to solve a problem arround patient transportation.
A stretcher can carry a bed, a wheelchair or a valid patient. He can carry a wheelchair and a valid at the same time if they go to the same area and come from a same area.
for long distance, a bed require 2 stretchers
a bed mover (a kind of electric truck fixed to the bed) replace 1
stretcher
bed movers are most efficient for long distance but may be use for
short too
a bed mover only helps for bed (nor for wheelchair nor valid patient)
when finish, the bed mover stays in place or returns empty to a park
area (maybe 1..n places)
General rules :
the total time of transportation per agent must be as equals as
possible as each others (equity)
transports are time windowed and delay must be as short as possible
(depending on priorities)
new transport requests comes every time
it's better to use bed movers than 2 stretchers
Can you give me how to start to resolve this kind of problem. Do you think it's possible?
Regards
Related
I am trying to work with Optaplanner in order to solve the following simple exmample :
https://www.gams.com/products/gams/gams-language/#the-gams-language-at-a-glance/
simple inventory allocation exemple
I find it very difficult to do so since I couldn't find a similar cas on optaplanner
Can anyone help ? some directions, ressources
thanks in advance
It looks like the Facility Location Problem in OptaPlanner Quickstarts, but a consumer can pull from 2 or more suppliers at the same time.
Because the number of units to ship from supplier A to consumer X is an integer (*), you would need to use continuous value ranges and custom moves like in the investment example. Not simple (unlike other use cases in OptaPlanner), but doable.
(*) These business requirements are maybe unrealistic: in the real world, a half-full truck is almost as expensive as a full truck.
I have a problem to solve. I need to visit 7962 places with a vehicle. The vehicle travels with 10km/h and each time I visit one place I stay there for 1 minute. I want to divide those 7962 places into subsets that take will take up to 8 hours. So lets say 200 places take 8 hours I visit them and come back the next day to visit another maybe 250 places(the 200 places subsets will require more distance travelled). For the distance I only care for Euclidean Distances no need to take into account the distance through the road network.
A map of the 7962 places
What I have done so far is use the k means clustering algorithm to get good enough subsets and then the Lin Kernighan heuristic (Program Concorde) to find the distance. And then compute times. But my results go from 4 hours to 12 hours. Any idea to make it better? Or a code that does this whole task all together. Propose anything but I am not a programmer I just use Python some times.
Set of coordinates :
http://www.filedropper.com/wholesetofcoordinates
Coordinates subsets(40 clusters produces with the k means algorithm):
http://www.filedropper.com/kmeans40clusters
I am trying to solve variant of a multi-path traveling salesman with an incomplete graph.
EDIT: changed the description (twice) based on feedback from #daniel_junglas.
In more words:
Only 1 salesperson
The salesperson can only visit every city exactly once
The salesperson can drive in various modes of transport (e.g. train, car, boat). Every mode of transport changes the time it takes to travel between cities
The salesperson can change the mode of transport in between cities (at a cost), but not in a city. The change can be seen as yet another edge between the two cities with a particular weight associated
not every city can be visited by every mode of transport (e.g. only a boat can reach city D)
the graph is not complete, so not all cities are connected, but one or more Hamiltonian path exists
Based on the example:
4 cities (1-4), each node having a car parking lot (C), train station (T), harbor for boats (B).
Starting and ending in 1C
Every city has to be visited one
No link from a train station to a harbour to the parking lot, only able to change outside of the cities (for instance 1C to 2T).
every link has a weight associated to it, based on distance, speed of transport mode and time penalty for changing transport mode
Example paths:
1C -> 2T -> 3T -> 4T -> 1C
1C -> 2C -> 3T -> 4T -> 1C
I was planning to solve this with concorde/cplex.
I have tried solving it with pyconcorde. For this, I encoded every parallel edge to the same node as a new node (A, A', A''), but I can't find a restriction to say only one A-node should be visited. I see many a-symetric TSP and multi-TSP solutions, but none fitting my requirements.
My questions: How should I approach this problem (tutorial link, embedding proposal, etc) to find the shortest route that visits all cities exactly once? Which tool would help me?
P.S. I am aware of various single-algorithm solutions, both probabilistic and exact. However, I am searching for a tool that combines various techniques, such as cplex, in the hope of better results for my specific data.
P.S.2. I am aware this question might be broad. I am open to any remarks in order to improve my questions
Gurobi has a good example of this with an interactive map and example code, see here. They are solving the same problem essentially that you describe, you just need to provide your own input locations and connections.
I am not sure you can create a graph that represents your model and on which you can solve a vanilla TSP: If you have multiple edges between nodes then we already agreed that you can remove any but the cheapest one. If you instead duplicate nodes then you have the problem that you no longer want to visit all nodes but only exactly one node from a set of duplicates. This is not addressed by TSP solvers.
However, you said you want to solve concorde/cplex. How about dropping the concorde part? You could take a MIP formulation for TSP. That should be easy to extend to include your additional constraints. For example, you could go back to multiple edges and add conditions like "if you enter city A by car then you have to leave by car or pay an extra N". You can then feed this to a general MIP solver like CPLEX.
At a high level, Concorde is a very efficient implementation of column generation (branch and price and cut) for the TSP.
You can surely develop a very efficient implementation of column generation for your problem, where the routes would satisfy your constraints.
Marco Luebbecke has a very nice general-purpose tutorial. The TSP book is the reference work on the subject.
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.
I've gotten a request to show a person's local time based on their phone number. I know our local GMT offset, so I can handle USA phones via a database table we have that links US zip_code to GMT offset (e.g. -5). But I've no clue how to convert non-US phone numbers or country names (these people obviously don't have a zip code).
If you care, my employer college wants to solicit our alumni for donations and do it during reasonable hours.
Sorry to all that I didn't clearly state that I was considering HOME phone numbers. So roaming isn't an issue. I'm looking for some reference table or Oracle application I can source this info from.
Florida has two time zones, but many countries only have one. You need this table: http://en.wikipedia.org/wiki/List_of_country_calling_codes . Parse the country code out of the phone number by looking for the 1 and the area code for NANPA countries (those countries using the same 1+area code allocation as the USA), 7 for Russia or Kazakhstan. If that doesn't match check to see whether the number starts with one of the 2 digit calling prefixes, and then the 3-digit ones.
Remember that the first few digits of the number may be the international dialing prefix, and are not properly part of the telephone number.
For countries that span more than one time zone, see if you can get allocation information from their national telecom regulator. For the USA and other NANPA countries, check out http://www.nanpa.com/ .
Of course your results will be far from perfect, but hopefully you will wake fewer customers from their night's sleep.
Local time is one thing but, if you have worldwide customers, there are also local habits to take into account.
People typically go to bed earlier in Norway than in Spain (and they are in the same time zone).
You might be able to get the phone company to feed you location data (this info should exist for land lines and must exist for cells) but expect to pay.
Some nations are easy, since they are in a single time-zone. Look at Europe and add millions of people by just using the internation dialing code. +47 for Norway etc.
Phone-number allocations are usually done by a national telecom authority, so you could probably get the information for free.
As you allready know this would only take into account default-timezone, since they might be anywhere on the planet at the time. Also number-allocation might not distingish at all between timezones, so the approach is buggy but potentially usefull to provide default settings.
Regards
Look in the phone book. Ours has quite a few pages mapping area codes onto countries/provinces/states. Then you have to map geographical locations onto time zones, but that is pretty straightforward.
Impossible. If I drive about 400 miles east (west coast of the US) then I'll break your algorithm by having a XXX number in a YYY timezone.
Now if this is a cell phone app, it does seem possible with something called NITZ.
I think Danie, Bortzmeyer, and others are over thinking the problem. It's not to maximize the calling window, it's to find an acceptable time.
Let's take the US and consider only the 4 major timezones. Say we define acceptable as from 10AM - 7PM. I doubt even the Norwegian Bachelor farmers go to be before 7PM.
So if you know that the phone is in the US, don't make any call before 1PM. That way if they are in NYC or LA, it's still after 10AM. And no calls after 7PM. Who cares if it's Florida main or its hour later panhandle? Dallas or El Paso, also same state but different time zones. For US, filter for AK and HI. The only seriously difficult country is Russia -- lots-o-timezones.