Reliable Facility Location Design Under Uncertain Correlated Disruptions

Lu, Mengshi; Shen, Zuo‐Jun Max

doi:10.2139/ssrn.2336836

Cited by 13 publications

(29 citation statements)

References 35 publications

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“…First, we define three commonly-used representations of generally correlated disruption profiles (i.e., with scenario probabilities, marginal probabilities and conditional probabilities) and derive transformations between them. These transformations unify different representations that have appeared in past studies (Liu et al, 2009;Lu et al, 2015;Li and Ouyang, 2010) and theoretically prove their equivalence. Second, we provide detailed formulas to transform these probabilistic correlation profiles into an adapted supporting station structure.…”

Section: Introductionsupporting

confidence: 58%

Decomposition of general facility disruption correlations via augmentation of virtual supporting stations

Xie

Ouyang

2015

Transportation Research Part B: Methodological

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Section: Introductionsupporting

confidence: 58%

Decomposition of general facility disruption correlations via augmentation of virtual supporting stations

Xie

Ouyang

2015

Transportation Research Part B: Methodological

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“…Second, Theorem 4 extends the framework studied by Lu et al. () in the context of facility location, showing how the DRO approach can be applied when sources of disruption risks can be partitioned into groups.…”

Section: Hybrid Heuristic Modelmentioning

confidence: 64%

“…Lu et al. () apply DRO in the context of facility location, where the disruption risks among the set of candidate facility locations are correlated. Due to the supermodularity of their objective function, they can obtain an explicit form of the worst‐case joint distribution for their problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Sourcing from Suppliers under Ambiguously Correlated Major Disruption Risks

Zhao

Freeman

2019

Production and Operations Management

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A s the severity and frequency of supply chain disruptions increases due to globalization and outsourcing, companies are faced with the challenge of managing correlated disruption risks. However, correctly estimating supplier correlations is difficult and relying on incorrect estimates can be costly. Motivated by these challenges, we consider models for managing disruption risks when suppliers are ambiguously correlated. Our models are based on profit-oriented correlation structures that only require accurate estimates of the marginal disruption probabilities for available suppliers. Analysis of the models, along with extensive numerical studies, show that they offer high-quality solutions and provide several important insights regarding supplier selection, appropriate budgets for efforts to better understand the prevailing correlation structure, and identifying subsets of the available suppliers that should be considered for strategic alliances. In addition to these benefits, we discuss a progressive, profit-oriented approach for risk management that is based on these models and is easily implemented in situations with minimal in formation regarding supplier correlations.

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“…Step (5): Update the value of Γ i via the equation (38). Then, come back to Step (2) and figure out the Lagrangian problem with new Lagrangian coefficients.…”

Section: Step (2): Figure Out the Lagrangian Problem With The Lagrangmentioning

confidence: 99%

“…Shishebori and Yousefi-Babadi [37] studied a reliable and robust health care station location ND problem, which simultaneously takes an investment budget constraint, system disruptions, and uncertain parameters into account. Lu et al [38] formulated a reliable facility location design in which allows disruptions at different locations to be correlated with an uncertain joint distribution. They applied distributionally robust optimization to minimize the expected cost under the worst-case distribution with predetermined marginal disruption probabilities.…”

mentioning

confidence: 99%

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

2017

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Abstract:This study considers a multi-objective combined budget constrained facility location/network design problem (FL/NDP) in which the system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure the network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including, transshipment costs, facility location (FL) costs, fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider of several system uncertainty, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient Sub-gradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. At the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering of practical factors (e.g., several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and subsequently more efficient utilization of the established network.

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Reliable Facility Location Design Under Uncertain Correlated Disruptions

Cited by 13 publications

References 35 publications

Decomposition of general facility disruption correlations via augmentation of virtual supporting stations

Decomposition of general facility disruption correlations via augmentation of virtual supporting stations

Robust Sourcing from Suppliers under Ambiguously Correlated Major Disruption Risks

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

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