Prescriptive analytics for a multi-shift staffing problem.
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| Title: | Prescriptive analytics for a multi-shift staffing problem. |
|---|---|
| Authors: | Notz, Pascal M.1 (AUTHOR) pascal.notz@uni-wuerzburg.de, Wolf, Peter K.1 (AUTHOR), Pibernik, Richard1,2 (AUTHOR) |
| Source: | European Journal of Operational Research. Mar2023, Vol. 305 Issue 2, p887-901. 15p. |
| Subjects: | Queuing theory, Decision support systems |
| Abstract: | • This paper proposes novel data-driven approaches to a multi-shift staffing problem. • Fluid and stationary approximations enable application of prescriptive analytics. • Case study conducted using real historical demand and feature data. • Weighted Sample Average Approximation approach achieves best results. • Our approach bridges the worlds of queuing theory and prescriptive analytics. Motivated by the work with an industry partner, this paper proposes and examines novel data-driven approaches to solve a certain type of capacity-sizing problem, which we term the multi-shift staffing problem (MSSP). In our MSSP, a company has to staff multiple shifts for each workday in the presence of uncertain arrival rates that vary throughout the day and patient "customers" that do not abandon the queue while waiting for a service, but who must be served by some pre-defined time. Drawing on established methods in both capacity management and prescriptive analytics, we propose to use fluid and stationary approximations of the demand arrival process to apply tailored prescriptive analytics approaches to determine staffing levels for multiple interrelated shifts. The prescriptive analytics approaches rely on machine learning techniques that incorporate a detailed representation of the non-stationary structure of arrivals and leverage extensive auxiliary data. In particular, we adapt established prescriptive analytics approaches—weighted sample average approximation and kernelized empirical risk minimization—and propose a new optimization prediction approach to solving the multi-shift staffing problem. Using a case study that is based on extensive data from our project partner, the maintenance service provider, we demonstrate the applicability of these approaches, highlight their benefits over traditional "estimate then optimize" approaches, and shed light on their structural properties and performance drivers. [ABSTRACT FROM AUTHOR] |
| Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 159978683 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prescriptive analytics for a multi-shift staffing problem. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Notz%2C+Pascal+M%2E%22">Notz, Pascal M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pascal.notz@uni-wuerzburg.de</i><br /><searchLink fieldCode="AR" term="%22Wolf%2C+Peter+K%2E%22">Wolf, Peter K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pibernik%2C+Richard%22">Pibernik, Richard</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Mar2023, Vol. 305 Issue 2, p887-901. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Queuing+theory%22">Queuing theory</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • This paper proposes novel data-driven approaches to a multi-shift staffing problem. • Fluid and stationary approximations enable application of prescriptive analytics. • Case study conducted using real historical demand and feature data. • Weighted Sample Average Approximation approach achieves best results. • Our approach bridges the worlds of queuing theory and prescriptive analytics. Motivated by the work with an industry partner, this paper proposes and examines novel data-driven approaches to solve a certain type of capacity-sizing problem, which we term the multi-shift staffing problem (MSSP). In our MSSP, a company has to staff multiple shifts for each workday in the presence of uncertain arrival rates that vary throughout the day and patient "customers" that do not abandon the queue while waiting for a service, but who must be served by some pre-defined time. Drawing on established methods in both capacity management and prescriptive analytics, we propose to use fluid and stationary approximations of the demand arrival process to apply tailored prescriptive analytics approaches to determine staffing levels for multiple interrelated shifts. The prescriptive analytics approaches rely on machine learning techniques that incorporate a detailed representation of the non-stationary structure of arrivals and leverage extensive auxiliary data. In particular, we adapt established prescriptive analytics approaches—weighted sample average approximation and kernelized empirical risk minimization—and propose a new optimization prediction approach to solving the multi-shift staffing problem. Using a case study that is based on extensive data from our project partner, the maintenance service provider, we demonstrate the applicability of these approaches, highlight their benefits over traditional "estimate then optimize" approaches, and shed light on their structural properties and performance drivers. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ejor.2022.06.011 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 887 Subjects: – SubjectFull: Queuing theory Type: general – SubjectFull: Decision support systems Type: general Titles: – TitleFull: Prescriptive analytics for a multi-shift staffing problem. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Notz, Pascal M. – PersonEntity: Name: NameFull: Wolf, Peter K. – PersonEntity: Name: NameFull: Pibernik, Richard IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 03772217 Numbering: – Type: volume Value: 305 – Type: issue Value: 2 Titles: – TitleFull: European Journal of Operational Research Type: main |
| ResultId | 1 |
