Input

Filling in pre-defined shifts with employees according to contractual agreements that enforce fair and cost efficient rosters. The input requires you to make up:

• contract definitions
• open shifts
• available employees

Additionally you can customise the solver behaviour by also changing the weights and priority of the constraints

Contract

A contract definition consists of a multitude of labour restricting constraints such as the maximum number of consecutive working days, the maximum number of shifts per day, number of resting days, etc...

{
    "name": "FULL",
    "max": "PT38H",
    "min": "PT5H",
    "maxConsecutiveWorkDays": 5,
    "maxShiftsDay": 2,
    "minRestBetweenShiftsSameDay": 30,
    "maxWorkingDays": 6,
    "latestShiftStart": "10:00:00",
    "earliestShiftStart": "02:00:00"
}

Shifts

The shifts represent the demand. These shifts need to be filled in with employees.
A shift in its simplest form can be defined by a shift start time (from) and an end time (to).
Shifts are uniquely identified by a name and require a specific skill.
You can define the number of employees that need to be assigned to this shift with value.

Employees

Define the resources or employees with whom you want to fill up the shifts.

Period Rule

A shift rule for an employee that applies to a period of time.

Fairness

For a select group of employees and shifts you can require fair distribution of shifts per planning period. The planning period can be the entire planning period or a subset of it.

{
  //...
  "fairnessBuckets": 
  [
    {
      "employees": ["John", "Andrew"],
      "shifts": ["shift1", "shift2"],
      "period": {
       "from": "2020-01-01",
        "to": "2020-01-15"
      }
    }
  ]
}

Output

Simply fetch the solution in the jobs/:id/solution endpoint and receive the shift assignments.
For every shift defined by its name we will assign the best employee.

[
{
  "name": "Morning Shift 1",
  "from": "2020-06-26T08:00:00",
  "to": "2020-06-30T08:00:00",
  "employee": "John Dory",
  "skills": [
      "waiter",
      "bar"
  ]
}
]

Weights

We take into account many constraints. Let us know if there are any constraints that you think are missing.

The weights and priorities of these constraints can be set in the solve request by adding a weights object to the request where each property that we want to customise has a string with the format "<weight><priority>" where <weight> is a positive integer number and <priority> can assume one of the following values: soft, medium and hard. hard constraints are more important than medium constraints and medium constraints are more important than soft. Between violations of two constraints with the same priority, the solver will choose the one with the minimum weight. In the example below we are customising the weights for the locked and unassigned constraints.

{
   "weights":{
          "locked" : "100hard",
          "unassigned" : "5soft"
    }
}