Introduction

pyworkforce is a Python package with tools for workforce management problems such as queue staffing, shift scheduling, rostering and operations-research optimization. It is especially well suited to contact / call centers, but the same techniques apply to hospitals, retail, logistics, network capacity planning and any operation that has to match a variable demand with a finite number of resources.

The package is organized around three planning steps that usually happen in sequence:

1. Queuing — how many resources do I need?

Given an incoming demand (for example, calls arriving at a call center), how many agents or channels are required to hit a service target?

• ErlangC — the classic M/M/c queue: Poisson arrivals, exponential handling times, an infinite queue and no abandonment.
• ErlangA — the M/M/c+M queue, which additionally models customers who abandon the queue if they wait too long. This is closer to reality for most contact centers.
• ErlangB — the M/M/c/c loss queue: no waiting room, blocked calls are shed. Use this for trunk / SIP channel sizing.
• MultiErlangC / MultiErlangA / MultiErlangB — evaluate many scenarios at once from a parameter grid, with a scikit-learn-like interface.

2. Staffing — what is the optimal skill mix?

When your team handles multiple contact types (languages, queues, skill groups), you need to decide how many agents of each skill profile to hire so that every skill's coverage requirement is met at the lowest possible cost.

• MultiSkillStaffing — solves the integer programme that minimises total weighted headcount given per-skill requirements. Flexible (multi-skilled) agents count towards every skill they hold, so the solver automatically trades off dedicated vs. flexible hiring.

3. Scheduling — how many people per shift?

Once you know the resources required per period of the day, decide how many people to place on each predefined shift.

• MinAbsDifference — minimize the absolute difference between required and scheduled resources.
• MinRequiredResources — minimize the (optionally weighted) number of scheduled resources while covering every period.

The shift coverage builders help you create the shifts_coverage definitions these solvers expect without writing 0/1 arrays by hand.

4. Rostering — who works when?

Assign named resources to days and shifts while respecting rules such as banned shifts, rest days, minimum working hours, non-sequential shifts and personal preferences.

• MinHoursRoster — build a resource-level roster that covers the shift requirements with the minimum scheduled hours.

5. Break scheduling — when do breaks happen?

Once the roster is set, assign a start time to every break for every agent slot while ensuring that simultaneous breaks never drop coverage below the required minimum.

• BreakScheduler — CP-SAT solver that places breaks inside shift windows, prevents overlaps, and respects per-period coverage constraints.

Design principles

pyworkforce aims for a scikit-learn level of friendliness:

• consistent constructors and clear, actionable error messages;
• get_params() and readable repr() on every estimator;
• the last result is stored on the estimator (solution_) after solving;
• deterministic, well-tested numerical methods.

Ready? Head to Installation and the Quick Start, or jump straight into the end-to-end tutorial.