Shelf-space optimization models in decentralized automated dispensing cabinets

Esmaili, N; Norman, BA; Rajgopal, J

Shelf-space optimization models in decentralized automated dispensing cabinets

Esmaili, N Norman, BA Rajgopal, J

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Publication Type:: Journal Article
Citation:: Operations Research for Health Care, 2018, 19 pp. 92 - 106
Issue Date:: 2018-12-01

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Field	Value	Language
dc.contributor.author	Esmaili, N	en_US
dc.contributor.author	Norman, BA	en_US
dc.contributor.author	Rajgopal, J	en_US
dc.date.issued	2018-12-01	en_US
dc.identifier.citation	Operations Research for Health Care, 2018, 19 pp. 92 - 106	en_US
dc.identifier.issn	2211-6923	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132439
dc.description.abstract	© 2018 Elsevier Ltd We propose a mixed integer programming (MIP) model to help clinicians store medications and medical supplies optimally in space-constrained, decentralized Automated Dispensing Cabinets (ADCs) located on hospital patient floors. We also propose a second MIP model that addresses human errors associated with the selection of pharmaceuticals from floor storage, and not only selects the best set of medications for storage but also determines their optimal layout within the cabinet. To improve the computational performance of these MIP models, we investigate several valid inequalities and relaxations that allow us to solve large, real-world instances in reasonable times. These models are applicable to very general ADCs and are illustrated using real-world data from ADCs at hospitals. Our results indicate that using these models can significantly reduce the time spent by clinical staff on routine logistical functions, while making efficient use of limited space and decreasing risks associated with errors in the selection of medication.	en_US
dc.relation.ispartof	Operations Research for Health Care	en_US
dc.relation.isbasedon	10.1016/j.orhc.2018.03.005	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Shelf-space optimization models in decentralized automated dispensing cabinets	en_US
dc.type	Journal Article
utslib.citation.volume	19	en_US
utslib.for	010303 Optimisation	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	1117 Public Health and Health Services	en_US
utslib.for	1199 Other Medical and Health Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2018 Elsevier Ltd We propose a mixed integer programming (MIP) model to help clinicians store medications and medical supplies optimally in space-constrained, decentralized Automated Dispensing Cabinets (ADCs) located on hospital patient floors. We also propose a second MIP model that addresses human errors associated with the selection of pharmaceuticals from floor storage, and not only selects the best set of medications for storage but also determines their optimal layout within the cabinet. To improve the computational performance of these MIP models, we investigate several valid inequalities and relaxations that allow us to solve large, real-world instances in reasonable times. These models are applicable to very general ADCs and are illustrated using real-world data from ADCs at hospitals. Our results indicate that using these models can significantly reduce the time spent by clinical staff on routine logistical functions, while making efficient use of limited space and decreasing risks associated with errors in the selection of medication.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/132439