Has anyone used the google OR tools in python to solve the workforce scheduling problem. Can you please let me know
- Advantages and Disadvantages
- Any issues faced during usage and implementation
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In addition to the answer from @Mehdi... I've recently started to work with OR-tools and find it very nice for prototyping. The Python interface allowed me to produce a first version of my model within one day. The times to obtain a first solution seem good - it performed very favorable in the MiniZinc Challenge 2018.
The main struggles/disadvantages that I've run into so far are the very limited support for floating point numbers. Compared to MiniZinc for example (which I also used) there is no possibility to have cumulative constraints with floats. Also, some constraints seem to be lacking from the catalog/ that MiniZinc offers. For example the
AddMaxEquality function allows only variables and no expressions to be used, so you'll have to add additional variables.
I also found that the documentation could use some improvements - for example the solver parameters were hidden in the source code and there was no dedicated place where they were listed (or I looked in the wrong directions).
I used OR-tools for TSP and VRP. These are my observations:
1- It provides a good quality solution in reasonable time. However, it is not the optimal solution and in some cases you can find much better solutions easily.
2- The implantation in Python is straightforward.
3- It is not flexible. You can not add many extensions to the problem, just basic assumptions and constraints.
4- You will have some option such as giving the initial solution, the algorithm settings.
If you want a good solution fast and you are sure you will not expand the problem later, then go for it.
Workforce scheduling describes many different problems.
A popular concert is shift scheduling. This example has gained a lot of traction in the past. It shows how to implement useful constraints on the problem that contains fixed daily shifts.