Quantitative modeling in disaster management: A literature review

Baxter, Arden; Lagerman, H. E. Wilborn; Keskinocak, Pınar

doi:10.1147/jrd.2019.2960356

Cited by 14 publications

(8 citation statements)

References 91 publications

(166 reference statements)

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“…On the other hand, the process of repairing and restoring access to the road network affected by a disruptive event is temporary (Baxter et al, 2020;Çelik, 2016;Kim et al, 2018;Maya Duque et al, 2016;Yan et al, 2012), and whose decisions are affected by the resource constraints required for cleaning and rehabilitating the affected networks to serve the affected population. Temporary network repair activities are characteristic of the response phase (Maya Duque et al, 2016;Nurre et al, 2012).…”

Section: Types Of Results and Knowledge Generatedmentioning

confidence: 99%

“…On the other hand, the process of repairing and restoring access to the road network affected by a disruptive event is temporary (Baxter et al. , 2020; Çelik, 2016; Kim et al.…”

Section: Resultsmentioning

confidence: 99%

“…In the case of network flow problems, the vehicle fleet is designated by integer variables and the trajectory of each vehicle cannot be easily monitored in the distribution network; however, their computational complexity can be minimized (Edrissi et al, 2015). These problems have been mainly represented using integer and mixed integer linear programming modeling (Lin et al, 2011;Edrissi et al, 2015;Ozdamar et al, 2018;Tavana et al, 2018;Tan et al, 2019;Shin et al, 2019;Hezam and Nayeem, 2020;Baxter et al, 2020;Coco et al, 2020).…”

Section: Characteristics Of the Models Usedmentioning

confidence: 99%

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Humanitarian aid distribution logistics with accessibility constraints: a systematic literature review

Trejos

Meisel²,

Jaimes³

2022

JHLSCM

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PurposeThe purpose of this paper is to review the relevant literature in order to identify trends and suggest some possible directions for future research in the framework of humanitarian aid distribution logistics with accessibility constraints.Design/methodology/approachThe authors developed a systematic literature review to study the state of the art on distribution logistics considering accessibility constraints. The electronic databases used were Web of science, Scopus, Science Direct, Jstor, Emerald, EBSCO, Scielo and Redalyc. As a result, 49 articles were reviewed in detail.FindingsThis study identified some gaps, as well as some research opportunities. The main conclusions are the need for further studies on the interrelationships and hierarchies of multiple actors, explore intermodality, transshipment options and redistribution relief goods to avoid severe shortages in some nodes and excess inventory in others, studies of the vulnerability of transport networks, correlational analysis of road failures and other future lines.Research limitations/implicationsThe bibliography is limited to peer-reviewed academic journals due to their academic relevance, accessibility and ease of searching. Most of the studies included in the review were conducted in high-income countries, which may limit the generalizability of the results to low-income countries. However, the authors focused on databases covering important journals on humanitarian logistics.Originality/valueThis paper contextualises and synthesises research into humanitarian aid distribution logistics with accessibility constrains, highlights key themes and suggests areas for further research.

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Section: Types Of Results and Knowledge Generatedmentioning

confidence: 99%

“…On the other hand, the process of repairing and restoring access to the road network affected by a disruptive event is temporary (Baxter et al. , 2020; Çelik, 2016; Kim et al.…”

Section: Resultsmentioning

confidence: 99%

Section: Characteristics Of the Models Usedmentioning

confidence: 99%

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Humanitarian aid distribution logistics with accessibility constraints: a systematic literature review

Trejos

Meisel²,

Jaimes³

2022

JHLSCM

View full text Add to dashboard Cite

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“…Relief distribution and resource allocation during disasters have been extensively studied and reviewed (e.g., Anaya-Arenas et al., 2014; Baxter et al., 2020). The majority of this research develops optimization models for problems like facility and asset location, flow assignment and scheduling, vehicle routing, and fleet management (e.g., Salmerón and Apte, 2010; Doan and Shaw, 2019; Pedraza-Martinez et al., 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Fuel Distribution

Rana,

Russell,

Boutilier

et al. 2024

Production and Operations Management

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Fuel is an essential commodity in society with an amplified role during disasters. Disasters often cause a spike in demand for fuel and impose physical and operational constraints on fuel distribution. Accordingly, public and private sector stakeholders seek disaster preparedness and response interventions to ensure an adequate supply of fuel for emergency activities. We develop models to quantify downstream operational flow capacity, i.e., the volume of fuel that can be distributed from bulk storage terminals to retail gas stations via tanker trucks. We do this for both steady state conditions and non-steady state conditions during disasters. The operational flow capacity measures can serve as inputs to optimal relief distribution and resource allocation problems, as well as enable a cost-benefit assessment of interventions aimed at increasing system capacity. We identify the best interventions for each type of bulk storage terminal structure and determine how to prioritize bulk storage terminals for each type of intervention when resources are limited. As the downstream fuel distribution system from bulk storage terminals to retail gas stations is similar across regions, our methodology and insights are generalizable not only within the US but also in countries with comparable distribution systems. To demonstrate impact on practice, we present a case study drawn from the application of our methodology to support US federal disaster preparedness. More generally, we contribute to system level understanding of multi-server tandem cyclic queues with time-limited customers found in relief distribution systems, which is often overlooked in the disaster management literature.

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“…Problems similar to mRmD have been studied in emergency response and disaster management (Baxter et al (2020)), machine and project scheduling, robotic task allocation, and vehicle routing problems. Some of the theoretical notions of our work also align with interval graphs (Gilmore and Hoffman (1964); Olariu (1991); Yannakakis and Gavril (1987); Mertzios (2008); Carlisle and Lloyd (1995)) where a demand can be represented as an interval with its start time and service duration.…”

Section: Literature Reviewmentioning

confidence: 99%

Heterogeneous Multi-Resource Allocation with Subset Demand Requests

Baxter,

Keskinocak,

Singh

2022

Preprint

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We consider the problem of allocating multiple heterogeneous resources geographically and over time to meet demands that require some subset of the available resource types simultaneously at a specified time, location, and duration. The objective is to maximize the total reward accrued from meeting (a subset of) demands.We model this problem as an integer program, show that it is NP-hard, and analyze the complexity of various special cases. We introduce approximation algorithms and an extension to our problem that considers travel costs. Finally, we test the performance of the integer programming model in an extensive computational study.

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Quantitative modeling in disaster management: A literature review

Cited by 14 publications

References 91 publications

Humanitarian aid distribution logistics with accessibility constraints: a systematic literature review

Humanitarian aid distribution logistics with accessibility constraints: a systematic literature review

Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Fuel Distribution

Heterogeneous Multi-Resource Allocation with Subset Demand Requests

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