A quantitative model for disruption mitigation in a supply chain

Paul, SK; Sarker, R; Essam, D

A quantitative model for disruption mitigation in a supply chain

Paul, SK

Sarker, R Essam, D

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Publication Type:: Journal Article
Citation:: European Journal of Operational Research, 2017, 257 (3), pp. 881 - 895
Issue Date:: 2017-03-16

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Field	Value	Language
dc.contributor.author	Paul, SK https://orcid.org/0000-0001-9523-179X	en_US
dc.contributor.author	Sarker, R	en_US
dc.contributor.author	Essam, D	en_US
dc.date.available	2020-05-25T19:11:38Z
dc.date.issued	2017-03-16	en_US
dc.identifier.citation	European Journal of Operational Research, 2017, 257 (3), pp. 881 - 895	en_US
dc.identifier.issn	0377-2217	en_US
dc.identifier.uri	http://hdl.handle.net/10453/110794
dc.description.abstract	© 2016 Elsevier B.V. In this paper, a three-stage supply chain network, with multiple manufacturing plants, distribution centers and retailers, is considered. For this supply chain system we develop three different approaches, (i) an ideal plan for an infinite planning horizon and an updated plan if there are any changes in the data, (ii) a predictive mitigation planning approach for managing predictive demand changes, which can be predicted in advance by using an appropriate tool, and (iii) a reactive mitigation plan, on a real-time basis, for managing sudden production disruptions, which cannot be predicted in advance. In predictive mitigation planning, we develop a fuzzy inference system (FIS) tool to predict the changes in future demand over the base forecast and the supply chain plan is revised accordingly well in advance. In reactive mitigation planning, we formulate a quantitative model for revising production and distribution plans, over a finite future planning period, while minimizing the total supply chain cost. We also consider a series of sudden disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions and which consequently require plans to be revised after the occurrence of each disruption on a real-time basis. An efficient heuristic, capable of dealing with sudden production disruptions on a real-time basis, is developed. We compare the heuristic results with those obtained from the LINGO optimization software for a good number of randomly generated test problems. Also, some numerical examples are presented to explain both the usefulness and advantages of the proposed approaches.	en_US
dc.relation.ispartof	European Journal of Operational Research	en_US
dc.relation.isbasedon	10.1016/j.ejor.2016.08.035	en_US
dc.subject.classification	Operations Research	en_US
dc.title	A quantitative model for disruption mitigation in a supply chain	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	257	en_US
utslib.for	150309 Logistics and Supply Chain Management	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 Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Management Discipline
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	257	en_US

Abstract:

© 2016 Elsevier B.V. In this paper, a three-stage supply chain network, with multiple manufacturing plants, distribution centers and retailers, is considered. For this supply chain system we develop three different approaches, (i) an ideal plan for an infinite planning horizon and an updated plan if there are any changes in the data, (ii) a predictive mitigation planning approach for managing predictive demand changes, which can be predicted in advance by using an appropriate tool, and (iii) a reactive mitigation plan, on a real-time basis, for managing sudden production disruptions, which cannot be predicted in advance. In predictive mitigation planning, we develop a fuzzy inference system (FIS) tool to predict the changes in future demand over the base forecast and the supply chain plan is revised accordingly well in advance. In reactive mitigation planning, we formulate a quantitative model for revising production and distribution plans, over a finite future planning period, while minimizing the total supply chain cost. We also consider a series of sudden disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions and which consequently require plans to be revised after the occurrence of each disruption on a real-time basis. An efficient heuristic, capable of dealing with sudden production disruptions on a real-time basis, is developed. We compare the heuristic results with those obtained from the LINGO optimization software for a good number of randomly generated test problems. Also, some numerical examples are presented to explain both the usefulness and advantages of the proposed approaches.

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http://hdl.handle.net/10453/110794