Two-stage stochastic programming in disaster management: A literature survey

Graß, Emilia; Fischer, Kathrin

doi:10.1016/j.sorms.2016.11.002

Cited by 84 publications

(60 citation statements)

References 50 publications

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“…Hence, the model is a two-stage model for each humanitarian organization. We recognize that there have been multiple two-stage stochastic optimization models proposed for various phases of disaster management (see, e.g., [16,21,35,57]). In a disaster management two-stage stochastic programming framework, pre-disaster decisions such as prepositioning of supplies are referred to as first-stage decisions and decisions regarding shipment of disaster relief items to the demand nodes, such as flows on network links, are second-stage decisions.…”

Section: Optimization Under Uncertainty and Disaster Reliefmentioning

confidence: 99%

“…First-stage decisions should be made prior to realizing the uncertain factors, taking into account all possible scenarios of the incident. When the event happens, and unknown factors are revealed, second-stage decisions are modified and carried out [21].…”

Section: Optimization Under Uncertainty and Disaster Reliefmentioning

confidence: 99%

“…Indeed, since 2010 and, specifically, following the work by [57], the number of publications on two-stage stochastic programming in disaster management has increased significantly (see [21]). Rawls and Turnquist [57] presented a model that assists decision-makers in determining the relief item quantities and the location of facilities for pre-positioning in terms of preparedness for a possible hurricane.…”

Section: Optimization Under Uncertainty and Disaster Reliefmentioning

confidence: 99%

“…In two-stage stochastic programming, a common way to deal with uncertainty is to use the scenario approach; a scenario includes information about the location, time, and extent of the damage caused by the disaster [21]. Snyder [63] states that using the scenario approach makes the model more tractable and allows the parameters to be statistically dependent; dependency plays an important role in bringing the model closer to reality.…”

Section: The Stochastic Disaster Relief Game Theory Network Modelmentioning

confidence: 99%

“…Moreover, VI (20) has the same structural form as (21) except that it is defined over the simpler feasible set K 1 .…”

Section: Theorem 1 (Variational Inequality Formulation Of Stochastic mentioning

confidence: 99%

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A Stochastic Disaster Relief Game Theory Network Model

Nagurney

Salarpour

Dong

et al. 2020

SN Oper. Res. Forum

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In this paper, we construct a novel game theory model for multiple humanitarian organizations engaged in disaster relief. Each organization is faced with a two-stage stochastic optimization problem associated with the purchase and storage of relief items pre-disaster, subject to a budget constraint, and, if need be, additional purchases and shipments post the disaster. The model integrates logistical and financial components, in that the humanitarian organizations compete for financial donations, as well as freight service provision, and each seeks to maximize its expected utility. The expected utility function of each humanitarian organization depends on its strategies and on those of the other organizations, and their feasible sets do, as well, since the organizations are subject to common lower and upper bound demand constraints. The governing concept is that of a stochastic generalized Nash equilibrium. We provide alternative variational inequality formulations of the model and propose an algorithmic scheme, which at each iteration yields closed form expressions for the product purchase/storage/shipment variables and the associated constraint Lagrange multipliers. Numerical examples illustrate the modeling and computational framework.

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Section: Optimization Under Uncertainty and Disaster Reliefmentioning

confidence: 99%

Section: Optimization Under Uncertainty and Disaster Reliefmentioning

confidence: 99%

Section: Optimization Under Uncertainty and Disaster Reliefmentioning

confidence: 99%

Section: The Stochastic Disaster Relief Game Theory Network Modelmentioning

confidence: 99%

“…Moreover, VI (20) has the same structural form as (21) except that it is defined over the simpler feasible set K 1 .…”

Section: Theorem 1 (Variational Inequality Formulation Of Stochastic mentioning

confidence: 99%

See 3 more Smart Citations

A Stochastic Disaster Relief Game Theory Network Model

Nagurney

Salarpour

Dong

et al. 2020

SN Oper. Res. Forum

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In search of network resilience: An optimization‐based view

et al. 2020

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Fifty years of research in Networks coincides with 50 years of advances in resilience theory and applications. The purpose of this review is to identify how these two technical communities influenced each other in the past and can bolster each other in the future. Advances in resilience theory show that there are at least four ways networks demonstrate resilience: robustness, rebound, extensibility, and adaptability. Research published in Networks and by the broader network optimization community has focused primarily on technical methods for robustness and rebound. We review this literature to organize seminal problems and papers on the ability of networks to manage increasing stressors and return to normal activities after a stressful event. In contrast, the Networks community has made less progress addressing issues for network extensibility and adaptability. Extensibility refers to the ability to stretch current operations to surprising situations and adaptability refers to the ability to sustain operations into the future. We discuss ways to harness existing network optimization methods to study these forms of resilience and outline their limitations. We conclude by providing a research agenda that ensures the Networks community remains central to future advances in resilience while being pragmatic about the limitations of network optimization for achieving this task.

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