Optimization models for disaster response operations: a literature review

Kamyabniya, Afshin; Sauré, Antoine; Salman, F. Sibel; Bénichou, Noureddine; Patrick, Jonathan

doi:10.1007/s00291-024-00750-6

Cited by 2 publications

(3 citation statements)

References 151 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is useful for robust decision-making in high-stakes environments [13]. Previously, only linear or convex versions of these programs weresolvable [8,9,17,42], but now convergence to the optimal solution is possible for non-convex and non-smooth versions as well using our developed model-free method, which we have empirically validated as well in Section 6. In addition, our method can also handle constraints with state variables in them which cannot be handled by existing methods like risk-averse stochastic dual dynamic programming [57].…”

Section: Methodical Contribution Of This Researchmentioning

confidence: 94%

“…Despite the need for accurate and robust decision-making, no model in the disaster management literature has yet combined constrained optimization, risk aversion, and deprivation costs. This is due to the computational intractability issue which is solved by this research [42].…”

Section: Research Contributionsmentioning

confidence: 99%

“…∀ ∈ while proving Theorem 2 from Theorem 1 (on page 15) when we moved from (42) to (43). However, if we start from the statement of Theorem 2, we need to prove the time invariance of Lagrangian multipliers \ , ,…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

Ahmad,

Ramzan,

Omair

et al. 2024

Mathematics

View full text Add to dashboard Cite

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.

show abstract

Section: Methodical Contribution Of This Researchmentioning

confidence: 94%

Section: Research Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

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

Ahmad,

Ramzan,

Omair

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

Location and capacity allocation for emergency contact points in large-scale power outages

Truden,

Wankmüller,

Zehetner

et al. 2024

Cent Eur J Oper Res

View full text Add to dashboard Cite

Nowadays, industry and individuals alike are highly dependent on a reliable power supply. A large-scale power outage, commonly known as a “blackout” is caused by natural disasters, cyber attacks, technical failure, or human errors, and can lead to a variety of severe consequences. The far-reaching dynamics of blackouts can even result in the collapse of critical public service infrastructure reliant on electricity (e.g., communication, water supply, medical services, public safety). Particularly, the loss of information and communication infrastructure essential to reporting medical emergencies, and the collapse of the drinking water supply are two critical stressors for the population to cope with. One attempt to tackle this situation is to install temporary emergency contact points (ECPs) into existing infrastructure. These can be approached by the population to communicate with medical personnel and to receive drinking water. Different types of professional personnel, which is a limited resource, are required to run such ECPs. Our study introduces this tactical decision problem. We formulate it as an integer linear program for the optimal spatial allocation of ECPs, such that multiple types of human resources that are required for operating such locations can be efficiently assigned. A comprehensive numerical study, based on data of the City of Vienna, demonstrates how to reduce the walking distance of inhabitants while increasing the efficiency of resource allocation. Matrix pruning based on an enforced limit of the walking distances together with a decomposition approach is utilized to solve the considered instances.

show abstract

Optimization models for disaster response operations: a literature review

Cited by 2 publications

References 151 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

Location and capacity allocation for emergency contact points in large-scale power outages

Contact Info

Product

Resources

About