Empirical analysis of volunteer convergence following the 2011 tornado disaster in Tuscaloosa, Alabama

Lodree, Emmett J.; Davis, Lauren B.

doi:10.1007/s11069-016-2477-8

Cited by 32 publications

(38 citation statements)

References 20 publications

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“…Further properties of the optimal control policy are derived in section 3, followed by a numerical study in section 4. In the numerical study, we use the insights from the structural results from our MDP model to propose and analyze a class of practical admission policies under realistic conditions based on the real-world study of Lodree and Davis (2016). Concluding remarks are given in section 5.…”

Section: Controllermentioning

confidence: 99%

“…Consequently, the studies by Falasca and Zobel (2012) and Lassiter et al (2015), both of which concern VLA from a DOM perspective, are applicable to affiliated volunteers, but are not appropriate for managing the convergence of unaffiliated volunteers. As described in Lodree and Davis (2016), random numbers of unaffiliated volunteers arrive at or near affected areas at random times, and they participate in relief efforts for random amounts of time. Thus, uncertainty in volunteer arrival and abandonment times must be taken into account when making labor assignment decisions within the context of volunteer convergence.…”

Section: Disaster Operations Management and Volunteer Schedulingmentioning

confidence: 99%

“…The following observations from the Lodree and Davis () case study reveal several characteristics of real‐world volunteer convergence that we incorporate into our model and explore through numerical experiments and simulation: “…there is some evidence… that the exponential distribution can be used to model volunteer inter‐arrival times” (see p. 1129 in Lodree and Davis ); “the exponential distribution is not appropriate for modeling volunteer participation [abandonment] time” (see p. 1126 in Lodree and Davis ). The authors provide insights for when alternative distributions may be used for model volunteer inter‐departure times.…”

Section: Numerical Studymentioning

confidence: 99%

“…Volunteer arrival process —For some of the cases analyzed in Lodree and Davis (), the average time between individual volunteer arrivals (batch size = 1) was approximately 35 minutes, and the average time between group volunteer arrivals (batch size > 1) was about 45 minutes (see Figure 7 in Lodree and Davis ()). As such, we assume in Study 1 that volunteer inter‐arrival times are exponential with rate

ν = 2 \cdot \frac{60}{35} \approx 3.42

volunteers per hour.…”

Section: Numerical Studymentioning

confidence: 99%

See 3 more Smart Citations

Optimal Control of Parallel Queues for Managing Volunteer Convergence

Zayas‐Cabán

Lodree

Kaufman

2020

Production and Operations Management

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V olunteer convergence refers to the influx of volunteers to affected areas after large-scale disasters. There are not only many benefits to volunteer convergence, but it also creates significant logistical challenges that can impede relief efforts. This study examines polices for admitting volunteers into organized relief operations, and for assigning admitted volunteers to relief tasks. We represent this problem as a queueing system where, in addition to customer arrivals and departures, random server arrivals and abandonments are also present. Then, using a Markov decision process framework, we analyze server admission and assignment policies that seek to minimize relief tasks holding costs as well as volunteer holding and rejection costs. We show that the classic cl rule, a server allocation policy that determines where to put servers based on relief tasks holding costs and processing requirements, is optimal under both collaborative and noncollaborative service regimes and when batch server arrivals are allowed. Additionally, we find that the optimal server admission policy is a complex state-dependent policy. As a result, we propose a class of admission heuristics that depend on the number of workers in the system and the remaining system workload. In a numerical study, we show that our heuristic policies perform well with respect to long-run average costs, waiting times, number of volunteers in the system, and number of volunteers idling in the system over a range of parameter values and distributions that are based on real data from a case study. As such, they promise volunteer coordinators an effective and simple way to manage disaster volunteers.

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Section: Controllermentioning

confidence: 99%

Section: Disaster Operations Management and Volunteer Schedulingmentioning

confidence: 99%

Section: Numerical Studymentioning

confidence: 99%

ν = 2 \cdot \frac{60}{35} \approx 3.42

volunteers per hour.…”

Section: Numerical Studymentioning

confidence: 99%

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Optimal Control of Parallel Queues for Managing Volunteer Convergence

Zayas‐Cabán

Lodree

Kaufman

2020

Production and Operations Management

Self Cite

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“…Creating a balance between immediate needs of response and long term needs for coordination with all partners involved presents additional challenge especially managing proliferation of volunteers which might create a disaster within a disaster [24].…”

Section: Usability Of Eo Crisis Data and Role Of Mobile Technologymentioning

confidence: 99%

Emerging Trends in Crisis Management: Usability, Earth Observation and Disaster Management

Leonard¹

2018

Crisis Management - Theory and Practice

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Availability of Earth observation (EO) data and dissemination has been revolutionized with technological advancement in computation and use of mobile tools such as phones, tablets and laptops. This is further expanded with availability of affordable data/internet connections which no longer require Ethernet cables to get internet connection. The result is explosion of spatial data on various blogs and personal profiles such as Tweeter, Facebook, Instagram and other internet feeds. This has created explosion of information with a lot of integrity issues on data quality, lack of data source and lack of responsibility and authenticity. The problem is further compounded with advancement in search engines and data harvesting applications which collects similar archived information and makes them available to the user on the internet search engines. There should be a clear link detailing the nature of EO and the kind of relevant information that can be derived from them.

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Modeling Disaster Operations Management Problems with System Dynamics

Delgado-Alvarez

Morales

2018

Decision-Making in Humanitarian Operations

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Empirical analysis of volunteer convergence following the 2011 tornado disaster in Tuscaloosa, Alabama

Cited by 32 publications

References 20 publications

Optimal Control of Parallel Queues for Managing Volunteer Convergence

Optimal Control of Parallel Queues for Managing Volunteer Convergence

Emerging Trends in Crisis Management: Usability, Earth Observation and Disaster Management

Modeling Disaster Operations Management Problems with System Dynamics

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