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I want to assign people to cover shifts considering a set of constraints and preferences. Here's the problem definition:

Daily shifts must be covered by workers, who are divided in three groups:

  • Trainees (Nt = 2)
  • Regulars (Nr = 3)
  • Experts (Ne = 7)

The organization constraints are:

  • All shifts must be covered
  • A Trainee must always cover a shift with an Expert
  • Workers covering Friday must also cover Sunday
  • Nobody can cover two shifts in a row
  • Nobody works more than M shifts every D days

The process should maximize the following output:

  • Workers get shifts assigned based on their preference
  • Experts work as less as possible, only covering unassigned gaps
  • Number of shifts are evenly distributed

My initial idea is to write a branch and bound algorithm to generate all possible combinations allowed by the constraints. I would then score each solution for each worker based on their preferences, and run a marriage algorithm to get the best combination.

The main problem is that the number of combinations is too (damn) high. Without constraints, there would be 6**30 (~1e24) possible arrangements. I know most of these will be bound, but currently I'm generating at a pace of ~1e8/minute.

I'm wondering if there are any heuristics I could apply to improve the process. Any ideas here? How does this whole thing sound?

Thanks!

Sample code to generate combinations:

def get_combinations(population, base, spots):
    if len(base) == spots:
        yield base
        return

    for candidate in population:
        combo = base + (candidate, )

        if is_viable(combo, population, spots):
            yield from get_combinations(population, combo, spots)

population = {'t1', 't2', 'n1', 'n2', 'n3'}
get_combinations(population, tuple(), 30)
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  • $\begingroup$ Rather than implementing branch-and-bound yourself, I suggest you formulate this as an integer linear programming instance (or a SAT instance) and try applying an off-the-shelf ILP/SAT solver to see if they can find a solution for you. They implement multiple techniques and heuristics that are probably beyond what anyone on person could reasonably be expected to implement on their own. $\endgroup$
    – D.W. ♦
    Feb 17, 2018 at 19:17
  • $\begingroup$ Thanks @D.W. that's exactly what I've been investigating, I'll try to solve a simpler version following this approach! For what is worth, looks like some of the real-world constraints of this problem makes finding an optimal solution impossible. $\endgroup$
    – jminuscula
    Feb 19, 2018 at 12:59

1 Answer 1

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Turns out this problem is pretty hard to solve and is still under active research. This paper (2004) describes the state of the art.

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