Two-stage stochastic formulation for relief operations with multiple agencies in simultaneous disasters

Rodríguez-Espíndola, Oscar

doi:10.1007/s00291-023-00705-3

Cited by 7 publications

(3 citation statements)

References 79 publications

(128 reference statements)

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“…Based on the surrogate reward function (50) above and [47], we have designed "Algorithm 1" for the solution of "Problem 2" in the presence of only deterministic instantaneous constraints as follows.…”

Section: Developed Mechanismmentioning

confidence: 99%

“…The problem has an upper bound (constraint) on the deprivation level of the third affected area. This is because this affected area is assumed to be relatively more vulnerable to disruption than the rest [49,50]. These disruptions can be in the form of secondary disasters inside the affected areas or logistical blockages hindering relief supply, which will have devastating effects (increase in deprivation from life-sustaining resources) on the affected population.…”

Section: Numerical Examplementioning

confidence: 99%

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Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios

Ahmad,

Ramzan,

Omair

et al. 2024

Mathematics

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This paper considers a risk-averse Markov decision process (MDP) with non-risk constraints as a dynamic optimization framework to ensure robustness against unfavorable outcomes in high-stakes sequential decision-making situations such as disaster response. In this regard, strong duality is proved while making no assumptions on the problem’s convexity. This is necessary for some real-world issues, e.g., in the case of deprivation costs in the context of disaster relief, where convexity cannot be ensured. Our theoretical results imply that the problem can be exactly solved in a dual domain where it becomes convex. Based on our duality results, an augmented Lagrangian-based constraint handling mechanism is also developed for risk-averse reinforcement learning algorithms. The mechanism is proved to be theoretically convergent. Finally, we have also empirically established the convergence of the mechanism using a multi-stage disaster response relief allocation problem while using a fixed negative reward scheme as a benchmark.

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Section: Developed Mechanismmentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios

Ahmad,

Ramzan,

Omair

et al. 2024

Mathematics

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“…During 2020 alone, the average number of disasters and economic losses was higher than the average of the prior two decades (EM-DAT, 2021), threatening organisations and communities (Kovacs & Spens, 2009;Rodríguez-Espíndola, 2023). Humanitarian operations deploy a massive number of resources to different regions (John et al, 2019) in chaotic conditions with high levels of urgency (Kovacs & Spens, 2009).…”

Section: Introductionmentioning

confidence: 99%

Sustainability and intermodality in humanitarian logistics: a two-stage multi-objective programming formulation

et al. 2023

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When managing crises and disasters, decision-makers face high uncertainty levels, disrupted supply chains, and damaged infrastructure. This complicates delivering resources that are essential for the survival of the victims. Flexible and adaptable supply networks are needed to ensure a consistent flow of relief to the areas affected by disasters. Intermodality is a valuable approach when infrastructure is damaged, as it allows the use of different delivery modes to reach demand areas. Nevertheless, involving different transportation modes has an impact on the environment. Looking at the importance of helping victims and considering the environmental impact of humanitarian operations for long-term sustainability, intermodality and carbon emission reduction measures can be an interesting combination. This area, however, is currently understudied. This article introduces a two-stage stochastic formulation to fill that gap. The model addresses facility location, resource allocation, and intermodal relief distribution considering carbon emission reduction in facilities, intermodal activities, and distribution. The formulation minimises costs and the level of shortage of relief. The model is tested using a case study in Sinaloa, Mexico, to investigate the impact of intermodality and carbon emission reduction measures on costs and shortage of relief for disaster victims. The findings confirm that the model proposed allows for the diversification of transportation modes and reduces carbon emissions whilst achieving a good level of performance in both metrics. The comparison with a benchmark model without intermodality and carbon reduction measures suggests that the formulation can increase flexibility and reduce the level of CO2 emissions whilst maintaining high satisfaction rates.

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Theoretical Framework for Modeling Impact of Natural Disaster and Relief Measures

Agarwal Goel,

Roy Chowdhury,

Grover Sharma

et al. 2024

Economics of Natural Disasters

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Two-stage stochastic formulation for relief operations with multiple agencies in simultaneous disasters

Cited by 7 publications

References 79 publications

Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios

Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios

Sustainability and intermodality in humanitarian logistics: a two-stage multi-objective programming formulation

Theoretical Framework for Modeling Impact of Natural Disaster and Relief Measures

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