Mitigating partial-disruption risk: A joint facility location and inventory model considering customers’ preferences and the role of substitute products and backorder offers

Saha, AK; Paul, A; Azeem, A; Paul, SK

Mitigating partial-disruption risk: A joint facility location and inventory model considering customers’ preferences and the role of substitute products and backorder offers

Saha, AK Paul, A Azeem, A Paul, SK

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Computers & Operations Research, 2020, 117, pp. 1-26
Issue Date:: 2020-05

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Field	Value	Language
dc.contributor.author	Saha, AK
dc.contributor.author	Paul, A
dc.contributor.author	Azeem, A
dc.contributor.author	Paul, SK
dc.date.accessioned	2020-10-03T23:56:30Z
dc.date.available	2020-10-03T23:56:30Z
dc.date.issued	2020-05
dc.identifier.citation	Computers & Operations Research, 2020, 117, pp. 1-26
dc.identifier.issn	0305-0548
dc.identifier.issn	1873-765X
dc.identifier.uri	http://hdl.handle.net/10453/143062
dc.description.abstract	© 2020 This paper studies a joint facility location and inventory model from the viewpoint of partial-disruption risk—i.e., when manufacturing facilities meet the demands of third-party distribution centers with a portion of their capacity, free from any disruptions—while considering substitute products as a disruption risk mitigation strategy. We considered these third-party distribution centers as the customers of the manufacturing facilities. We used a multinomial logit model to rank-order the facilities according to customers’ preferences. Then, a non-linear integer programming model was developed which attempted to assign a sequence of facilities to each customer based on their preferences while at the same time, minimizing the total supply-chain cost. We also considered customers’ decisions for backorders while developing the model. Due to the NP-hard nature of the problem, we developed a particle swarm optimization-based metaheuristic algorithm to solve the model. The efficiency of the modified particle swarm optimization (MPSO) was illustrated through computational tests and systematic comparison with the exact method, a hybrid meta-heuristic algorithm including tabu search (TS) and variable neighborhood search (VNS) from the literature, and its modified form (Modified TS-VNS). A numerical example was used to show the applicability of the model. Finally, we gained useful insight into the role of substitute products and customers’ decisions for backorders through scenario-based analysis. We found that the total supply chain cost could increase in disruption scenarios when customers were more likely to refuse backorder offers. However, the cost-saving from producing a substitute for key products could be significant.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Computers & Operations Research
dc.relation.isbasedon	10.1016/j.cor.2020.104884
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0102 Applied Mathematics, 0103 Numerical and Computational Mathematics
dc.subject.classification	Operations Research
dc.title	Mitigating partial-disruption risk: A joint facility location and inventory model considering customers’ preferences and the role of substitute products and backorder offers
dc.type	Journal Article
utslib.citation.volume	117
utslib.for	1503 Business and Management
utslib.for	0102 Applied Mathematics
utslib.for	0103 Numerical and Computational Mathematics
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2020-10-03T23:56:26Z
pubs.publication-status	Published
pubs.volume	117

Abstract:

© 2020 This paper studies a joint facility location and inventory model from the viewpoint of partial-disruption risk—i.e., when manufacturing facilities meet the demands of third-party distribution centers with a portion of their capacity, free from any disruptions—while considering substitute products as a disruption risk mitigation strategy. We considered these third-party distribution centers as the customers of the manufacturing facilities. We used a multinomial logit model to rank-order the facilities according to customers’ preferences. Then, a non-linear integer programming model was developed which attempted to assign a sequence of facilities to each customer based on their preferences while at the same time, minimizing the total supply-chain cost. We also considered customers’ decisions for backorders while developing the model. Due to the NP-hard nature of the problem, we developed a particle swarm optimization-based metaheuristic algorithm to solve the model. The efficiency of the modified particle swarm optimization (MPSO) was illustrated through computational tests and systematic comparison with the exact method, a hybrid meta-heuristic algorithm including tabu search (TS) and variable neighborhood search (VNS) from the literature, and its modified form (Modified TS-VNS). A numerical example was used to show the applicability of the model. Finally, we gained useful insight into the role of substitute products and customers’ decisions for backorders through scenario-based analysis. We found that the total supply chain cost could increase in disruption scenarios when customers were more likely to refuse backorder offers. However, the cost-saving from producing a substitute for key products could be significant.

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http://hdl.handle.net/10453/143062