Develop Reference

Limit the number of ranges worked in a day

I'm writing code to automatically generate shifts for my workers.
There are several constraints I've already been able to create:
Each shift in a day is assigned to a single worker
Each worker can't work more than 8h per day
Each worker should work [#minShifts, #maxShifts] hours per week
Each worker can't work on different rooms at the same moment
Each worker can't work on more than 2 different rooms in a day
So far so good; now I'm facing another constraint I can't really think how to code: each worker can't work on more than 2 different ranges in a day.
What I mean is I don't want a person to work (e.g.) 08.30-10.30, 12.30-13.30 and 16.30-18.30, but limit assigned ranges at two.
Given that I have shifts[numWorkers, numDays, numShifts], how can I arrange this constraint?

If you stay with these variables for each block of two hours [8AM shift, 9AM shift],
create a boolvar that is true is the first shift is ON and the second is OFF
end_of_shift = model.NewBoolVar('')
model.AddBoolOr([first_shift.Not(), second_shift, end_of_shift])
model.AddImplication(end_of_shift, first_shift)
model.AddImplication(end_of_shift, second_shift.Not())
# then for each day and each worker:
model.Add(sum(end_of_shifts) <= 2)

EWS exchange rooms view

I need to make an application which shows the current exchange meeting rooms and for each room whether the hour is free or busy. The user can give a daterange of max 5 days to see the result.
I have made a construction but it 2 slow to use as it takes up to 3 seconds to get all the information from only 3 meeting rooms (while in the future it will be more around 20).
This is how I work:
Authenticate through AutodiscoverUrl function: service.AutodiscoverUrl(email, password).
After been given a startdate and enddate with 5 days in it, I first get all the available meetingrooms with service.GetRooms("room#roomlist.com")
I iterate through the found meetingrooms and use the function service.GetUserAvailability(room,...) to get the calenderevents.
Then I have a class which tells me the hours of the day and I check the found calenderevents of the room to see whether an hour is busy or not.
Now I have my collection of rooms with calenderevents and the indication whether an hour is busy or not.
But is there another, faster way? As said this takes up to 2/3 seconds for only 3 rooms in a daterange of 5 days.

Are you calling the GetUserAvailability request for each room, as you iterate through, or batching the users together? The availability call can return info on multiple users (100 is the hard limit I recall). It's likely one big call will be more efficient than multiple single calls.

Generate closest teams based on employee schedules C#

I am given a csv of employee schedules with columns:
employee ID, first last name, sunday schedule, monday schedule, ... , saturday schedule
1 week schedule for each employee. I've attached a screenshot of a portion of the csv file. The total file has around 300 rows.
I need to generate teams of 15 based on the employees' schedules (locations don't matter) so that the employees on each team have the closest schedules to each other. Pseudocode of what I have tried:
parse csv file into array of schedules (my own struct definition)
match employees who have the same exact schedule into teams (creates ~5 full sized teams, 20 - 25 half filled teams, leaves ~50 schedules who don't match with anyone)
for i = 1 to 14, for each member of teams of size i, find the team with the closest schedule (as a whole) and add the member to that team. Once a team reaches size 15, mark them as "done".
This worked somewhat but definitely did not give me the best teams. My question is does anyone know a better way to do this? Pseudocode or just a general idea will help, thanks.
EDIT: Here is an example of the formula of comparison
The comparison is based on half hour blocks of difference between the agents schedules. Agent 25 has a score of 16 because he has a difference of 8 half hours with Agent 23 and 24. The team's total score is 32 based on everyone's scores added together.
Not all agents work 8 hour days, and many have different days off, which have the greatest effect on their "closeness" score. Also, a few agents have a different schedule on a certain day than their normal schedule. For example, one agent might work 7am - 3pm on mondays but work 8am - 4pm on tuesday - friday.

Unless you find a method that gets you an exact best answer, I would add a hill-climbing phase at the end that repeatedly checks to see if swapping any pair of agents between teams would improve things, and swaps them if this is the case, only stopping when it has rechecked every pair of agents and there are no more improvements to be made.
I would do this for two reasons:
1) Such hill-climbing finds reasonably good solutions surprisingly often.
2) People are good at finding improvements like this. If you produce a computer-generated schedule and people can find simple improvements (perhaps because they notice they are often scheduled at the same time as somebody from another team) then you're going to look silly.
Thinking about (2) another way to find local improvements would be to look for cases where a small number of people from different teams are scheduled at the same time and see if you can swap them all onto the same team.

Can't say for sure about the schedules, but in string algorithms you can find an edit distance calculation. The idea is to define number of operations you need to perform to get one string from another. For example, distance between kitten and sitting is 3, 2 for substitutions and 1 for deletion. I think that you can define a metric between two employees' schedule in similar way.
Now, after you have a distance function, you may start a clusterization. The k-means algorithm may be a good start for you, but it's main disadvantage is that the number of groups is fixed initially. But I think that you can easily adjust general logic for your needs. After that, you may try some additional ways to cluster your data, but you really should start with your distance function, and then simply optimize it on your employee records.

Approach to solve delimited scheduling

I'm facing a problem and Im having problems to decide/figure-out an approach to solve it. The problem is the following:
Given N phone calls to be made, schedule in a way that the maximum of them be made.
Know Info:
Number of phone calls pending
Number callers (people who will talk on the phone)
Type of phone call (Reminder, billing, negotiation, etc...)
Estimate duration of phone call type (reminder:1min, billing:3min, negotiation:15min, etc...)
Number of phone calls pending
Ideal date for a given call
"Minimum" date of the a given call (can't happen before...)
"Maximum" date of the a given call (can't happen after...)
A day only have 8 hours
Rules:
Phone calls cannot be made before the "Minimum" or after the "Maximum" date
Reminder call placed award 1 point, reminder call missed -2 points
Billing call placed award 6 points, billing call missed -9 points
Negotiation call placed award 20 points, Negotiation call missed -25 points
A phone calls to John must be placed by the first person to ever call him. Notice that it does not HAVE TO, but, that call will earn extra points if you do...
I know a little about A.I. and I can recognize this a problem that fits the class, but i just dont know which approach to take... should i use neural networks? Graph search?
PS: this is not a academic question. This a real world problem that im facing.
PS2: Pointing system is still being created... the points here sampled are not the real ones...
PS3: The resulting algol can be executed several times (batch job style) or it can be resolved online depending on the performance...
PS4: My contract states that I will charge the client based on: (amount of calls I place) + (ratio * the duration of the call), but theres a clause about quality of service, and only placing reminders calls is not good for me, because even when reminded, people still forget to attend their appointments... which reduces the "quality" of the service I provide... i dont know yet the exact numbers

This does not seem like a problem for AI.
If it were me I would create a set of rules, ordered by priority. Then start filling in the caller's schedule.
Mabey one of the rules is to assign the shortest duration call types first (to satisfy the "maximum number of calls made" criteria).
This is sounding more and more like a knapsack problem, where you would substitute in call duration and call points for weight and price.

This is just a very basic answer, but you could try to "brute force" an optimum solution:
Use the Combinatorics library (it's in NuGet too) to generate every permutation of calls for a given person to make in a given time period (looking one week into the future, for instance).
For each permutation, group the calls into 8-hour chunks by estimated duration, and assign a date to them.
Iterate through the chunks - if you get to a call too early, discard that permutation. Otherwise add or subtract points based on whether the call was made before the end date. Store the total score as the score for that permutation.
Choose the permutation with the highest score.

How to start some Method every 20 days (Windows Phone)

In my Windows Phone Applicarion I need to implement logic to start some Method every 20 days, how can I implement this?
public void Method()
{
//some logic
}
Update
For example I can fix the first start of some method, then every start of application I will fix the current date time and them calculate the differens, and if the difference between the last and first sstart of some method will be more than 30 days I will start Method(). So how can I calculate the difference (days)?

Not easily!
The best fit for this is to use a Scheduled Agent - but this runs ever 30 minutes approximately - so you would need to keep track of the last run time and act accordingly.
The other issue with this is that if the app that is associated with the scheduled task is not run at least ever 14 days the task is disabled.
HTH,
Rupert.

So you want to know if it's 20 (or more?) days since you last performed an action in your app.
Let's assume that you save the date to calculate from as a DateTime called savedDate. (I'm assuming you can put this in and retrieve from IsolatedStorage without issue.)
Then you can just do a simple test against the current date:
if (saveddate.Date.AddDays(20) <= DateTime.UtcNow.Date)
{
// Do your every 20 days action here
// and then probably reset savedDate to the current date?
}

What about a push notification. You let the server take care of this. When the time is ready it sends a notification to the phone which does things accordingly.
Edit:
To find the difference you can do something like this
TimeSpan span = endTime.Subtract(startTime);
if(span.Days >= 20)
Method();

You could store the last time the method was ran in Isolated Storage, and then use the Scheduled Agent to run every 30min:
if (DateTime.Now.Subtract(LastTimeRanFromIsolatedStorage) > TimeSpan.FromDays(20))
{
Method();
}