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

•A joint facility location and inventory model is developed.•Partial-disruption risk is considered.•A modified particle swarm optimization is proposed to solve the model.•The impact of the customer's decision to accept or reject backorder has analyzed.•The use of substitute products as a risk m...

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Vydáno v:Computers & operations research Ročník 117; s. 104884 - 26
Hlavní autoři: Saha, Apurba Kumar, Paul, Ananna, Azeem, Abdullahil, Paul, Sanjoy Kumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.05.2020
Pergamon Press Inc
Témata:
Algorithms
Backorders
Cost analysis
Customers
Customers’ preferences
Decision analysis
Disruption
Disruption risk
Distribution centers
Heuristic methods
Industrial plants
Integer programming
Location–inventory modeling
Logit models
Manufacturing
Modified particle swarm optimization
Operations research
Particle swarm optimization
Risk
Risk mitigation
Substitute product
Substitutes
Supply chains
Tabu search
Disruption risk
Modified particle swarm optimization
Substitute product
Location–inventory modeling
Customers’ preferences
Risk mitigation
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:•A joint facility location and inventory model is developed.•Partial-disruption risk is considered.•A modified particle swarm optimization is proposed to solve the model.•The impact of the customer's decision to accept or reject backorder has analyzed.•The use of substitute products as a risk management strategy has analyzed. 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2020.104884