Inequity-averse shelter location for disaster preparedness

Mostajabdaveh, Mahdi; Gutjahr, Walter J.; Salman, Serdar

doi:10.1080/24725854.2018.1496372

Cited by 48 publications

(37 citation statements)

References 63 publications

(66 reference statements)

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“…In this area, we can mention the authors in [18], who propose a relocation of sites for the delivery of support in Mexico, through the problem of the p-median modeled in the LINGO software, thereby achieving the minimization of the current path at 25%. Meanwhile, in [19], the authors develop a stochastic programming model for shelter location in disaster preparedness, introducing a Genetic Algorithm (GA) for larger instances.…”

Section: Literate Reviewmentioning

confidence: 99%

Logistics Solutions in the Preparation Phase for the Appearance of Disasters

Barojas-Payán¹,

Sánchez‐Partida²,

Heredia-Roldan³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Natural disasters have caused not only economic but also human losses. These events bring with them, among other things, deficiencies in the supply of food, clothing, health and cleaning products, to name a few. This situation makes it imperative to locate facilities that can supply the needs, as mentioned earlier, to the victims in the shortest possible time. This document presents the evaluation of a logistic model of the literature whose foundations are: a) the classic p-median problem for the location of a pre-positioning warehouse; b) an extension of the (q-R) model for calculating inventories of different products, according to different types of demand, and c) the problem of multiple vehicles routing with the capacity to establish delivery routes from warehouses to the affected municipalities. This model is evaluated with 90 municipalities belonging to Veracruz, Mex. The results show that the location of the warehouse falls in the municipality of Fortín de las Flores, the inventory levels for five demands and four product kits, and product delivery routes, which are a total of 12, with which a favorable cost minimization is obtained.

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Section: Literate Reviewmentioning

confidence: 99%

Logistics Solutions in the Preparation Phase for the Appearance of Disasters

Barojas-Payán¹,

Sánchez‐Partida²,

Heredia-Roldan³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…Recently, Mostajabdaveh et al [14] studied a stochastic scenario-based problem involving the determination of a set of shelter locations in the preparedness stage for natural disasters, which takes into consideration the equity quantified by Gini’s Mean Absolute Difference (called Gini index) under uncertainty. For large-scale problems, a genetic algorithm to solve the sub-problem by mixed-integer programming was proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…when disasters happen. Topics regarding shelter or evacuation, important aspects of disaster relief, have been frequently considered by researchers, and have appeared in emergency supply planning [1,2], shelter location problems [3,4,5], emergency evacuation planning problems [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26] and shelter location and evacuation routing assignment problems [4,5,8,9,10,11,12,16,27,28].…”

Section: Introductionmentioning

confidence: 99%

A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment

Liang

Yang

Qin

et al. 2019

IJERPH

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Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia.

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“…Models exist that consider equity in facility location, but these often include equity as a constraint of the problem rather than a component of the objective [14,15,16]. There are many examples of equity metrics available to include in these models, as this research has attempted to address this issue earnestly for decades [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Model for Including Equity in the Siting of Emergency Services

Newton

Kumara

Ventura

et al. 2022

Preprint

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We propose a method to identify inequity in access to emergency services using logistic regression and present a model to address inequity by siting additional facilities, recommending both quantity and location. We classify emergencies by the median income bracket of their Zip code tabulation areas. We use logisitic regression to determine whether the probability of emergency responses classified by income bracket are within the average response, defined by time or distance, and quantify overall equity using mean absolute deviation. To address inequity, we iterate a bounded maximal covering location problem over coverage distance and additional facilities to provide a set of possible solutions. We evaluate the mean absolute deviation of each solution and identify the tradeoff of adding facilities and minimizing deviation.We use 2019 data from the United States Internal Revenue Service and San Francisco Fire Department as an example case for our model. In this example, we find the distribution of emergency rooms leave the low income population of San Francisco significantly underserviced and recommend to decision makers additional locations that reduce the average distance to an emergency room while minimizing mean absolute deviation—providing more equitable access to emergency rooms across income groups in San Francisco.

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Inequity-averse shelter location for disaster preparedness

Cited by 48 publications

References 63 publications

Logistics Solutions in the Preparation Phase for the Appearance of Disasters

Logistics Solutions in the Preparation Phase for the Appearance of Disasters

A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment

A Generalized Model for Including Equity in the Siting of Emergency Services

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