Develop Reference

Employee Scheduling Constraints Issue

I am currently trying to develop an employee scheduling tool in order to reduce the daily workload. I am using pyomo for setting up the model but unfortunately stuck on one of the constraint setting.
Here is the simplified background:
4 shifts are available for assignation - RDO (Regular Day Off), M (Morning), D (Day) and N (Night). All of them are 8-hrs shift
Every employee will get 1 RDO per week and constant RDO is preferred (say staff A better to have Monday as day off constantly but this can be violate)
Same working shift (M / D / N) is preferred for every staff week by week (the constraint that I stuck on)
a. Example 1 (RDO at Monday): The shift of Tuesday to Sunday should be / have better to be the same
b. Example 2 (RDO at Thursday): The shift of Mon to Wed should be same as the last working day of prior week, while the shift of Fri to Sun this week also need to be same but not limit to be which shift
Since the RDO day (Mon - Sun) is different among employees, the constraint of point 3 also require to be changed people by people conditionally (say if RDO == "Mon" then do A ; else if RDO == "Tue" then do B), I have no idea how can it be reflected on the constraint as IF / ELSE statement cant really work on solver.
Appreciate if you can give me some hints or direction. Thanks very much!

The constraints you are trying to create could be moderately complicated, and are very dependent on how you set up the problem, how many time periods you look at in the model, etc. etc. and are probably beyond the scope of 1 answer. Are you taking an LP course in school? If so, you might want to bounce your framework off of your instructor for ideas.
That aside, you might want to tackle the ROD by assigning each person a cost table based on their preferences and then putting in a small penalty in the objective based on their "costs" to influence the solver to give them their "pick" -- assumes the "picks" are relatively well distributed and not everybody wants Friday off, etc.
You could probably do the same with the shifts, essentially making a parameter that is indexed by [employee, shift] with "costs" and using that in the obj in a creative way. This would be the easiest solution... others get into counting variables, big-M, etc.

You could in this case use a switch case. Use the ROD as input and the cases are the days of the week. In the cases you can than to the rest of the planing.
Here is a good reverence:
https://pythongeeks.org/switch-in-python/

Trying to work out how to produce a synthetic data set using python or javascript in a repeatable way

I have a reasonably technical background and have done a fair bit of node development, but I’m a bit of a novice when it comes to statistics and a complete novice with python, so any advice on a synthetic data generation experiment I’m trying my hand at would be very welcome :)
I’ve set myself the problem of generating some realistic(ish) sales data for a bricks and mortar store (old school, I know).
I’ve got a smallish real-world transactional dataset (~500k rows) from the internet that I was planning on analysing with a tool of some sort, to provide the input to a PRNG.
Hopefully if I explain my thinking across a couple of broad problem domains, someone(s?!) can help me:
PROBLEM 1
I think I should be able to use the real data I have to either:
a) generate a probability distribution curve or
b) identify an ‘out of the box’ distribution that’s the closest match to the actual data
I’m assuming there’s a tool or library in Python or Node that will do one or both of those things if fed the data and, further, give me the right values to plug in to a PRNG to produce a series of data points that not are not only distributed like the original's, but also within the same sort of ranges.
I suspect b) would be less expensive computationally and, also, better supported by tools - my need for absolute ‘realness’ here isn’t that high - it’s only an experiment :)
Which leads me to…
QUESTION 1: What tools could I use to do do the analysis and generate the data points? As I said, my maths is ok, but my statistics isn't great (and the docs for the tools I’ve seen are a little dense and, to me at least, somewhat impenetrable), so some guidance on using the tool would also be welcome :)
And then there’s my next, I think more fundamental, problem, which I’m not even sure how to approach…
PROBLEM 2
While I think the approach above will work well for generating timestamps for each row, I’m going round in circles a little bit on how to model what the transaction is actually for.
I’d like each transaction to be relatable to a specific product from a list of products.
Now the products don’t need to be ‘real’ (I reckon I can just use something like Faker to generate random words for the brand, product name etc), but ideally the distribution of what is being purchased should be a bit real-ey (if that’s a word).
My first thought was just to do the same analysis for price as I’m doing for timestamp and then ‘make up’ a product for each price that’s generated, but I discarded that for a couple of reasons: It might be consistent ‘within’ a produced dataset, but not ‘across’ data sets. And I imagine on largish sets would double count quite a bit.
So my next thought was I would create some sort of lookup table with a set of pre-defined products that persists across generation jobs, but Im struggling with two aspects of that:
I’d need to generate the list itself. I would imagine I could filter the original dataset to unique products (it has stock codes) and then use the spread of unit costs in that list to do the same thing as I would have done with the timestamp (i.e. generate a set of products that have a similar spread of unit cost to the original data and then Faker the rest of the data).
QUESTION 2: Is that a sensible approach? Is there something smarter I could do?
When generating the transactions, I would also need some way to work out what product to select. I thought maybe I could generate some sort of bucketed histogram to work out what the frequency of purchases was within a range of costs (say $0-1, 1-2$ etc). I could then use that frequency to define the probability that a given transaction's cost would fall within one those ranges, and then randomly select a product whose cost falls within that range...
QUESTION 3: Again, is that a sensible approach? Is there a way I could do that lookup with a reasonably easy to understand tool (or at least one that’s documented in plain English :))
This is all quite high level I know, but any help anyone could give me would be greatly appreciated as I’ve hit a wall with this.
Thanks in advance :)
The synthesised dataset would simply have timestamp, product_id and item_cost columns.
The source dataset looks like this:
InvoiceNo,StockCode,Description,Quantity,InvoiceDate,UnitPrice,CustomerID,Country
536365,85123A,WHITE HANGING HEART T-LIGHT HOLDER,6,12/1/2010 8:26,2.55,17850,United Kingdom
536365,71053,WHITE METAL LANTERN,6,12/1/2010 8:26,3.39,17850,United Kingdom
536365,84406B,CREAM CUPID HEARTS COAT HANGER,8,12/1/2010 8:26,2.75,17850,United Kingdom
536365,84029G,KNITTED UNION FLAG HOT WATER BOTTLE,6,12/1/2010 8:26,3.39,17850,United Kingdom
536365,84029E,RED WOOLLY HOTTIE WHITE HEART.,6,12/1/2010 8:26,3.39,17850,United Kingdom
536365,22752,SET 7 BABUSHKA NESTING BOXES,2,12/1/2010 8:26,7.65,17850,United Kingdom
536365,21730,GLASS STAR FROSTED T-LIGHT HOLDER,6,12/1/2010 8:26,4.25,17850,United Kingdom
536366,22633,HAND WARMER UNION JACK,6,12/1/2010 8:28,1.85,17850,United Kingdom

Is there a library or suggested tactic for shift planning with hours and breaks?

I’m trying to think through a sort of extra credit project- optimizing our schedule.
Givens:
“demand” numbers that go down to the 1/2 hour. These tell us the ideal number of people we’d have on at any given time;
8 hour shift, plus an hour lunch break > 2 hours from the start and end of the shift (9 hours from start to finish);
Breaks: 2x 30 minute breaks in the middle of the shift;
For simplicity, can assume an employee would have the same schedule every day.
Desired result:
Dictionary or data frame with the best-case distribution of start times, breaks, lunches across an input number of employees such that the difference between staffed and demanded labor is minimized.
I have pretty basic python, so my first guess was to just come up with all of the possible shift permutations (points at which one could take breaks or lunches), and then ask python to select x (x=number of employees available) at random a lot of times, and then tell me which one best allocates the labor. That seems a bit cumbersome and silly, but my limitations are such that I can’t see beyond such a solution.
I have tried to look for libraries or tools that help with this, but the question here- how to distribute start times and breaks within a shift- doesn’t seem to be widely discussed. I’m open to hearing that this is several years off for me, but...
Appreciate anyone’s guidance!

Understanding SLOCCount output

I recently run the SLOCCount tool because I needed to estimate the number of lines in a large project.
This is what it showed:
Totals grouped by language (dominant language first):
python: 7826 (100.00%)
Total Physical Source Lines of Code (SLOC) = 7,826
Development Effort Estimate, Person-Years (Person-Months) = 1.73 (20.82)
(Basic COCOMO model, Person-Months = 2.4 * (KSLOC**1.05))
Schedule Estimate, Years (Months) = 0.66 (7.92)
(Basic COCOMO model, Months = 2.5 * (person-months**0.38))
Estimated Average Number of Developers (Effort/Schedule) = 2.63
Total Estimated Cost to Develop = $ 234,346
(average salary = $56,286/year, overhead = 2.40).
I'm not entirely sure how it comes up with all those estimates but one in particular threw me off, the Development Effort Estimate. I read about the COCOMO model but I'm still a bit lost.
What is the meaning of this estimate in simple words?

The Development Effort Estimate is a measure of how much time it might have taken to create the 7.8k lines of Python code.
If you believe in divisible man-months of effort, it would have taken one person about 21 months to produce (might be about right), or two people about 11 months (a bit optimistic), or three people about 7 months (quite optimistic). In practice, it doesn't scale linearly like that — and some tasks are indivisible. Putting 9 women to work to produce a baby in 1 month doesn't work, even though it takes 1 woman 9 months to produce a baby.
Is $56k really the average salary for a programmer these days?

COCOMO calculates how long it takes the average developer in a large company to create this software.
It's a very rough estimate, but there are parameters (called drivers) that you can tweak to make it more accurate to your case.
Some tools like ProjectCodeMeter can auto-detect these parameters and make the calculation for you.

Data modeling/forecasting in Python?

How do I go about this problem I've thought up? I don't even know if it's possible in Python, but anyway. Basically, I want to give Python some data to look for patterns in the data and then display the most likely result. I thought pocket money would make a good example:
A gets $7 a week, a dollar a day, he spends $5 at the weekend and his balance on Monday is $2 + $1 ($7 a week 1 a day). This continues for three weeks. What will A get on the forth week?
Balance in account:
week1 = 1,2,3,4,5,6,7
week2 = 3,4,5,6,7,8,9
week3 = 5,6,7,8,9,10,11
week4 = ??????????
Now, instead of basic math I was wondering if it was possible to create a model, one that looks for patterns and then creates the data using the existing data and the patterns. So the script should be able to see that A gets $7 a week and he loses $5 a week. Is this possible?
Is the model possible to be flexible as in if I give it other data of the same nature, will it be able to find patterns?
(I'm using Python 3.2.)

What you're describing is classified as a regression problem. Armed with this term, you should be able to find lots of information online and in books.
To point you in the right direction, I'd suggest investigating "linear regression" and perhaps moving on to a more complex regression model like the "random forests regressor".
The "scikit-learn" Python package has a number of different regression models and the documentation is excellent.