pyworkforce 0.4.0

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 or-tools and custom modules

Features:

pyworkforce currently includes:

Queue Systems:

• queing.ErlangC: Find the number of positions required to attend incoming traffic to a constant rate, infinite queue length and no dropout.

Shifts:

It finds the number of resources to schedule in a shift, based in the number of required positions per time interval (found for example using queing.ErlangC), maximum capacity restrictions and static shifts coverage.

• shifts.MinAbsDifference: This module finds the "optimal" assignation by minimizing the total absolute differences between required resources per interval, against the scheduled resources found by the solver.
• shifts.MinRequiredResources: This module finds the "optimal" assignation by minimizing the total weighted amount of scheduled resources (optionally weighted by shift cost), it ensures that in all intervals, there are never less resources shifted that the ones required per period.

Usage:

Install pyworkforce

It's advised to install pyworkforce using a virtual env, inside the env use:

pip install pyworkforce

If you are having troubles with or-tools installation, check the or-tools guide

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

Queue systems:

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)

Output:

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

If you want to run different scenarios at the same time, you can use the MultiErlangC, for example, trying different service levels:

from pyworkforce.queuing import MultiErlangC

param_grid = {"transactions": [100], "aht": [3], "interval": [30], "asa": [20 / 60], "shrinkage": [0.3]}
multi_erlang = MultiErlangC(param_grid=param_grid, n_jobs=-1)

required_positions_scenarios = {"service_level": [0.8, 0.85, 0.9], "max_occupancy": [0.8]}

positions_requirements = multi_erlang.required_positions(required_positions_scenarios)
print("positions_requirements: ", positions_requirements)

Output:

>> positions_requirements:   [
                                {
                                    "raw_positions": 13,
                                    "positions": 19,
                                    "service_level": 0.7955947884177831,
                                    "occupancy": 0.7692307692307693,
                                    "waiting_probability": 0.285270453036493
                                },
                                {
                                    "raw_positions": 14,
                                    "positions": 20,
                                    "service_level": 0.8883500191794669,
                                    "occupancy": 0.7142857142857143,
                                    "waiting_probability": 0.1741319335950498
                                },
                                {
                                    "raw_positions": 15,
                                    "positions": 22,
                                    "service_level": 0.9414528428690223,
                                    "occupancy": 0.6666666666666666,
                                    "waiting_probability": 0.10204236700798798
                                }
                            ]

Shifts

Example:

from pyworkforce.shifts import MinAbsDifference, MinRequiredResources

# Rows are the days, each entry of a row, is number of positions required at 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,an hour of the day (24), 1 if the shift covers that hour, 0 otherwise
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]}


# Method One
difference_scheduler = MinAbsDifference(num_days=2,
                                        periods=24,
                                        shifts_coverage=shifts_coverage,
                                        required_resources=required_resources,
                                        max_period_concurrency=27,
                                        max_shift_concurrency=25)

difference_solution = difference_scheduler.solve()

# Method Two

requirements_scheduler = MinRequiredResources(num_days=2,
                                              periods=24,
                                              shifts_coverage=shifts_coverage,
                                              required_resources=required_resources,
                                              max_period_concurrency=27,
                                              max_shift_concurrency=25)

requirements_solution = requirements_scheduler.solve()

print("difference_solution :", difference_solution)

print("requirements_solution :", requirements_solution)

Output:

>> difference_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}]
                          }

>> requirements_solution: {'status': 'OPTIMAL', 
                           'cost': 113.0, 
                           'resources_shifts': [{'day': 0, 'shift': 'Morning', 'resources': 15}, 
                                                {'day': 0, 'shift': 'Afternoon', 'resources': 13}, 
                                                {'day': 0, 'shift': 'Night', 'resources': 19}, 
                                                {'day': 0, 'shift': 'Mixed', 'resources': 3}, 
                                                {'day': 1, 'shift': 'Morning', 'resources': 20}, 
                                                {'day': 1, 'shift': 'Afternoon', 'resources': 20}, 
                                                {'day': 1, 'shift': 'Night', 'resources': 23}, 
                                                {'day': 1, 'shift': 'Mixed', 'resources': 0}]}