Abstract

Personnel scheduling problems have attracted research interests for several decades. They have been considerably changed over time, accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems including optimisation, meta-heuristics, artificial intelligence, decision-support, and also hybrids of these approaches. However, this still does not imply that this research has a large impact on practice and that state-of-the art models and algorithms are widely in use in organisations. One can find a reasonably large number of software packages that aim to assist in personnel scheduling. A classification of this software based on its purpose will be proposed, accompanied with a discussion about the level of support that this software offers to schedulers. A general conclusion is that the available software, with some exceptions, does not benefit from the wealth of developed models and methods. The remaining of the paper will provide insights into some characteristics of real-world scheduling problems that, in the author’s opinion, have not been given a due attention in the personnel scheduling research community yet and which could contribute to the enhancement of the implementation of research results in practice. Concluding remarks are that in order to bridge the gap that still exists between research into personnel scheduling and practice, we need to engage more with schedulers in practice and also with software developers; one may say we need to get wet if we want to learn how to swim.

Highlights

  • Personnel scheduling can be defined as the process of assignment of personnel to shifts to cover the demand for resources that varies over time (Ernst et al 2004b)

  • Personnel scheduling problems have attracted research interests for several decades. They have been considerably changed over time, accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems including optimisation, meta-heuristics, artificial intelligence, decisionsupport, and hybrids of these approaches

  • Personnel scheduling problems have been considerably changed since accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems, which can be classified in the following groups: optimisation, meta-heuristics, artificial intelligence, decision-support, and hybrids of these approaches

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Summary

Introduction

Personnel scheduling can be defined as the process of assignment of personnel to shifts to cover the demand for resources that varies over time (Ernst et al 2004b). Common features include import/export of data describing employees, access to the software through mobile devices, access to the software through cloud servers, which means that the software can be accessed from any computer with an internet connection, posting schedules online so that employees can view them on their computers or mobile devices, sending text and/or e-mails to employees reminding them of their shifts, etc This gives a great opportunity to employ computer science and software development knowledge to advance personnel scheduling.

Personnel scheduling-problem definition and methods
Software packages for personnel scheduling
Software for roster administration
Software for workforce management
Software for automated rostering
Towards bridging the gap between personnel scheduling research and practice
Research directions
Well-being of personnel
Integration of planning and scheduling
Integration of personnel scheduling with other related problems
What-if analysis
Re-scheduling
Modelling of particular features of real-world personnel scheduling
Modelling of uncertainty
Maintenance of the software
Findings
Concluding remarks
Full Text
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