A recovery planning model for online business operations under the COVID-19 outbreak

Paul, SK; Moktadir, MA; Sallam, K; Choi, T-M; Chakrabortty, RK

A recovery planning model for online business operations under the COVID-19 outbreak

Paul, SK Moktadir, MA Sallam, K Choi, T-M Chakrabortty, RK

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Publisher:: Taylor and Francis
Publication Type:: Journal Article
Citation:: International Journal of Production Research, 2023, 61, (8), pp. 2613-2635
Issue Date:: 2023-04

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Field	Value	Language
dc.contributor.author	Paul, SK
dc.contributor.author	Moktadir, MA
dc.contributor.author	Sallam, K
dc.contributor.author	Choi, T-M
dc.contributor.author	Chakrabortty, RK
dc.date.accessioned	2024-02-12T02:43:35Z
dc.date.available	2024-02-12T02:43:35Z
dc.date.issued	2023-04
dc.identifier.citation	International Journal of Production Research, 2023, 61, (8), pp. 2613-2635
dc.identifier.issn	0020-7543
dc.identifier.issn	1366-588X
dc.identifier.uri	http://hdl.handle.net/10453/175575
dc.description.abstract	This study analytically develops a new recovery planning optimisation model for managing the impacts of the recent COVID-19 outbreak for online business operations. Firstly, a mathematical model for the ideal plan is designed and then extended to generate a recovery plan in a finite planning horizon that maximises total profit. Recovery plans are generated considering two scenarios, namely the dynamic and uncertain situations. For the dynamic situation, a realistic system with time-dependent and dynamic demand, supply, and warehouse capacity for investigating the impacts of the COVID-19 outbreak is developed using several measures, such as collaborating with emergency suppliers, increasing warehouse capacity, and considering back-orders and lost sales to form recovery strategies. For the uncertain situation, demand, supply, and warehouse capacities are considered as uncertain variables. Further, an innovative solution approach using an adapted differential evolution technique, which is capable of (i) generating long-term recovery plans and (ii) solving both small- and large-scale problems, is developed. The results are illustrated using numerical analyses and simulation experiments. A sensitivity analysis is also conducted. In practice, the proposed optimisation model will assist the decision-makers of online business operations facing the COVID-19 outbreak to decide the optimal recovery plans.
dc.language	en
dc.publisher	Taylor and Francis
dc.relation.ispartof	International Journal of Production Research
dc.relation.isbasedon	10.1080/00207543.2021.1976431
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Operations Research
dc.title	A recovery planning model for online business operations under the COVID-19 outbreak
dc.type	Journal Article
utslib.citation.volume	61
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Business
pubs.organisational-group	University of Technology Sydney/Faculty of Business/Management Discipline
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2024-02-12T02:43:34Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	61
utslib.citation.issue	8

Abstract:

This study analytically develops a new recovery planning optimisation model for managing the impacts of the recent COVID-19 outbreak for online business operations. Firstly, a mathematical model for the ideal plan is designed and then extended to generate a recovery plan in a finite planning horizon that maximises total profit. Recovery plans are generated considering two scenarios, namely the dynamic and uncertain situations. For the dynamic situation, a realistic system with time-dependent and dynamic demand, supply, and warehouse capacity for investigating the impacts of the COVID-19 outbreak is developed using several measures, such as collaborating with emergency suppliers, increasing warehouse capacity, and considering back-orders and lost sales to form recovery strategies. For the uncertain situation, demand, supply, and warehouse capacities are considered as uncertain variables. Further, an innovative solution approach using an adapted differential evolution technique, which is capable of (i) generating long-term recovery plans and (ii) solving both small- and large-scale problems, is developed. The results are illustrated using numerical analyses and simulation experiments. A sensitivity analysis is also conducted. In practice, the proposed optimisation model will assist the decision-makers of online business operations facing the COVID-19 outbreak to decide the optimal recovery plans.

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http://hdl.handle.net/10453/175575