5 ms·

Sorry to hear this. Any lessons learned? I use WhenIWork for this area, which suits our needs. Not sure what would make me switch, probably not a text messag

by tarr11 10y ago

Sorry to hear this. Any lessons learned? I use WhenIWork for this area, which suits our needs. Not sure what would make me switch, probably not a text messaging. Most of my employees have smartphones, so a mobile app is fine. Biggest problem for me (as a business owner) is managing schedules to a budget, and making sure we are being as efficient as possible with our staffing spend. Most tools I've seen have a pretty weak budgeting feature. (You just put your budget in and it tells you how you did)

madenine 10y ago
I know some companies are using ML to do this internally - predicting staffing needs, optimizing costs, etc. It strikes me as a very individualized problem; might be hard to generalize - or the markets you could generalize too aren't that big/don't need a solution all that much.
- philip1209 10y ago
  ML is more statistical - good for forecasting. We were doing optimization mathematics with hard constraints for the scheduling. One of the issues that we encountered was that a generalized algorithm was tough. Different companies had different requirements for an algorithm, and it started to feel like a consulting company. For instance, if a big box retailer would rather be slightly underscheduled at peak time rather than adding additional employees (and being over-scheduled near the peak time). A high-end boutique retailer (like a diamond shop) might be the opposite - each sales brings in so much money that they overstaff at non-peak hours so that they can service customers during peak hours.
  - xapata 10y ago
    Sounds like a machine learning problem. The dependent variable is profit, not budget.
    - princeb 10y ago
      scheduling is a classic mgmt. science problem.
      - xapata 10y ago
        But once you get into the modeling of how many customers come in at what hour and how likely they are to purchase how much conditioned on the staff present, it's a learning problem.
        philip1209 10y ago
        It's a pipeline. Check out this [1] paper about the Taco Bell scheduling system. It's forecasting (stats, but would probably be called ML if paper wasn't in 1998) -> queuing theory (markov chains, sometimes formula-based, sometimes simulated) -> scheduling (normally mixed integer linear programming) [1] http://pubsonline.informs.org/doi/abs/10.1287/inte.28.1.75 http://pubsonline.informs.org/doi/abs/10.1287/inte.28.1.75
        xapata 10y ago
        Eh, what the heck, with enough layers (and enough data) a neural network will eventually create a neuron for each of those tasks. Just let it train for a few years.
        xapata 10y ago
        BTW, that was a joke.
    - botexpert 10y ago
      Not really machine learning. https://en.wikipedia.org/wiki/Nurse_scheduling_problem https://en.wikipedia.org/wiki/Nurse_scheduling_problem Above is a nice example. There has been some nice contests with the above problem and the ILP solvers work extremely fast and great and solve them to optimality. Although Staffjoy constraints might have been more general. Either way you could easily attack any custom problem with an ILP solver. It depends how long it would take to get a feasible solution and then how long to minimize the costs or fit the budget. In order to speed up the solver you might use ML but that would require previous data. Probably the only way to speed up the solver is to learn it through reinforcement learning on a batch of data. Takes time and time and time. Not to mention that your ILP solver has to be equipped to merge with any ML machinery you are using.
  - madenine 10y ago
    As a consultant, sounds like my bread and butter (specialized problem, no generalized tools). For a product company, sounds like a nightmare - no one's paying you by the hour to meet their unique requirements. Thanks for sharing your story!

by tarr11 10y ago

madenine 10y ago

I know some companies are using ML to do this internally - predicting staffing needs, optimizing costs, etc. It strikes me as a very individualized problem; might be hard to generalize - or the markets you could generalize too aren't that big/don't need a solution all that much.

philip1209 10y ago
ML is more statistical - good for forecasting. We were doing optimization mathematics with hard constraints for the scheduling. One of the issues that we encountered was that a generalized algorithm was tough. Different companies had different requirements for an algorithm, and it started to feel like a consulting company. For instance, if a big box retailer would rather be slightly underscheduled at peak time rather than adding additional employees (and being over-scheduled near the peak time). A high-end boutique retailer (like a diamond shop) might be the opposite - each sales brings in so much money that they overstaff at non-peak hours so that they can service customers during peak hours.
- xapata 10y ago
  Sounds like a machine learning problem. The dependent variable is profit, not budget.
  - princeb 10y ago
    scheduling is a classic mgmt. science problem.
    - xapata 10y ago
      But once you get into the modeling of how many customers come in at what hour and how likely they are to purchase how much conditioned on the staff present, it's a learning problem.
      - philip1209 10y ago
        It's a pipeline. Check out this [1] paper about the Taco Bell scheduling system. It's forecasting (stats, but would probably be called ML if paper wasn't in 1998) -> queuing theory (markov chains, sometimes formula-based, sometimes simulated) -> scheduling (normally mixed integer linear programming) [1] http://pubsonline.informs.org/doi/abs/10.1287/inte.28.1.75 http://pubsonline.informs.org/doi/abs/10.1287/inte.28.1.75
        xapata 10y ago
        Eh, what the heck, with enough layers (and enough data) a neural network will eventually create a neuron for each of those tasks. Just let it train for a few years.
        xapata 10y ago
        BTW, that was a joke.
  - botexpert 10y ago
    Not really machine learning. https://en.wikipedia.org/wiki/Nurse_scheduling_problem https://en.wikipedia.org/wiki/Nurse_scheduling_problem Above is a nice example. There has been some nice contests with the above problem and the ILP solvers work extremely fast and great and solve them to optimality. Although Staffjoy constraints might have been more general. Either way you could easily attack any custom problem with an ILP solver. It depends how long it would take to get a feasible solution and then how long to minimize the costs or fit the budget. In order to speed up the solver you might use ML but that would require previous data. Probably the only way to speed up the solver is to learn it through reinforcement learning on a batch of data. Takes time and time and time. Not to mention that your ILP solver has to be equipped to merge with any ML machinery you are using.
- madenine 10y ago
  As a consultant, sounds like my bread and butter (specialized problem, no generalized tools). For a product company, sounds like a nightmare - no one's paying you by the hour to meet their unique requirements. Thanks for sharing your story!