Automated Staff Scheduling Software

Tim Curtois

The OR Society Criminal Justice Special Interest Group 27 June 2012

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Background

The software was originally developed as part of a PhD research project on automatic scheduling for healthcare personnel

The research was published and a demo put online ASAP was approached by a software company interested in

licensing the technology The University of Nottingham formed a spin-out company

to license the software engine

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The Modelling Approach During the research we collected data and benchmark instances

from lots of different sources (e.g. industrial collaborators, other researchers)

Became clear that the problems varied significantly from one workplace to another not just in terms of problem size (e.g. staff numbers, planning horizon length, numbers and types of shifts) but also in the variety of working constraints and rules

Developed a model that would allow end-users to define custom rules and their priorities

Priorities are specified with weights/costs (number values). A penalty/cost is incurred when a rule cannot be satisfied

Objective is to minimise the sum of all penalties due to constraint violations

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Example Constraints

Employee working constraints - Min/max hours worked, min/max consecutive days on or off, shift rotations, night shifts, weekends, shift requests etc

Cover/demand constraints - Min/max required employees during shifts/time periods (possibly skill/task based)

Ensuring employees work together (or do not work together) Training/supervision, car-sharing, productivity based

constraints Similar skills, couple with children, or two employees just don't

get on!

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Problem Versions

1. Pre-defined shifts with fixed start and finish times (e.g. early shift, day shift, late shift, night shift)

2. Shift start and finish times not pre-defined, additional constraints e.g. Earliest/latest shift start and finish times Min/max shift lengths Break lengths and times (often depending on shift lengths and

start times) Tasks assigned during shifts

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Methodologies Exact based (e.g. Branch and Price)

Advantages Works very well on smaller instances Can provide optimal solutions or information on how close to optimal

the solution is

Disadvantages On larger instances can require infeasible amounts of memory and

computation times and so may not always return a solution Heuristic based (e.g. Metaheuristics)

Advantages More robust and always returns a solution regardless of instance size

and computation time

Disadvantages Outperformed by exact based methods on smaller instances No information on how close to optimal the solution is

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Demo

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(Software available at www.staffrostersolutions.com)