pyworkforce 0.2.1

Common tools for workforce management, schedule and optimization problems

pyworkforce

Common tools for workforce management, schedule and optimization problems built on top of packages like google's ortools and custom modules

Usage:

For complete list and details of examples go to the examples folder

Queue systems:

It can be used for solving the required number of positions to manage a number of transactions, under some systems pre-defined parameters and goals.

Example:

from pyworkforce.queuing import ErlangC

erlang = ErlangC(transactions=100, asa=20/60, aht=3, interval=30, shrinkage=0.3)

positions_requirements = erlang.required_positions(service_level=0.8, max_occupancy=0.85)
print("positions_requirements: ", positions_requirements)


>> positions_requirements:  {'raw_positions': 14, 
                             'positions': 20, 
                             'service_level': 0.8883500191794669, 
                             'occupancy': 0.7142857142857143, 
                             'waiting_probability': 0.1741319335950498}

Shifts Design

Find the optimal number of persons to assign to a pre-defined list of shifts, under a requirement of persons per period of day and capacity restrictions

Example:

from pyworkforce.shifts import MinAbsDifference

# Rows are the days, each entry of a row, is an hour of the day (24). 
required_resources = [
    [9, 11, 17, 9, 7, 12, 5, 11, 8, 9, 18, 17, 8, 12, 16, 8, 7, 12, 11, 10, 13, 19, 16, 7],
    [13, 13, 12, 15, 18, 20, 13, 16, 17, 8, 13, 11, 6, 19, 11, 20, 19, 17, 10, 13, 14, 23, 16, 8]
]

# Each entry of a shift, is an hour of the day (24)
shifts_coverage = {"Morning": [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   "Afternoon": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
                   "Night": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
                   "Mixed": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]}


scheduler = MinAbsDifference(num_days=2,
                             periods=24,
                             shifts_coverage=shifts_coverage,
                             required_resources=required_resources,
                             max_period_concurrency=25,
                             max_shift_concurrency=20)

solution = scheduler.solve()
print("solution :", solution)

>> solution: {'status': 'OPTIMAL', 
              'cost': 157.0, 
              'resources_shifts': [{'day': 0, 'shift': 'Morning', 'resources': 8},
                                   {'day': 0, 'shift': 'Afternoon', 'resources': 11},
                                   {'day': 0, 'shift': 'Night', 'resources': 9}, 
                                   {'day': 0, 'shift': 'Mixed', 'resources': 1}, 
                                   {'day': 1, 'shift': 'Morning', 'resources': 13}, 
                                   {'day': 1, 'shift': 'Afternoon', 'resources': 17}, 
                                   {'day': 1, 'shift': 'Night', 'resources': 13}, 
                                   {'day': 1, 'shift': 'Mixed', 'resources': 0}]
              }