Facility location under demand uncertainty: Response to a large-scale bio-terror attack

Murali, Pavankumar; Ordóñez, Fernando; Dessouky, Maged

doi:10.1016/j.seps.2011.09.001

Cited by 151 publications

(63 citation statements)

References 35 publications

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“…Indeed, even after a disaster has hit the zone, information about demand is hard to obtain, and a stochastic modeling approach can be useful to represent the incertitude related to the process of the impact's estimation. Recent contributions tackled this issue with stochastic models that maximized coverage (like Murali et al 2012), models reflecting post-disaster challenges as disaster overlapping , or fairness in distribution objectives . It is worth mentioning that, as we indicated before, the contributions in this section still present the classic structure of the FLP applied to emergency situations, without real insight into the context difficulties being reflected in their models.…”

Section: Location and Network Designmentioning

confidence: 99%

“…Despite their efforts, it seems that most of these contributions did not focus adequately in the specific characteristics of humanitarian logistics like the knowledge of demand, the considered objectives, the periodicity and the decision-making structure (Holguín-Veras et al, 2012). Hopefully, our knowledge and comprehension level of humanitarian challenges increases and recent articles present more sophisticated models, which better suit the specific context and needs, especially in the case of transportation problems (Berkoune et al 2012;Gu 2011;Huang et al 2012;Lee et al 2009a;Lin et al 2012;Lin et al 2011;Murali et al 2012;). Nonetheless, we think that the sudden and dramatic nature of humanitarian problems should be emphasized in future research works.…”

Section: Analysis Of the Reviewed Literaturementioning

confidence: 99%

“…Thereby, papers proposing approximated methods (e.g. Nolz et al 2010;Berkoune et al 2012;Lin et al 2012;Murali et al 2012;Wohlgemuthscha et al 2012) are becoming more and more popular than the ones, focusing in modeling aspects, where commercial software is used to solve the proposed mathematical formulation Lin et al 2011;Rawls & Turnquist 2011).…”

Section: Analysis Of the Reviewed Literaturementioning

confidence: 99%

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Relief distribution networks: a systematic review

2014

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In the last 20 years, Emergency Management has received increasing attention from the scientific community. Meanwhile, the study of relief distribution networks has become one of the most popular topics within the Emergency Management field. In fact, the number and variety of contributions devoted to the design or the management of relief distribution networks has exploded in the recent years, motivating the need for a structured and systematic analysis of the works on this specific topic. To this end, this paper presents a systematic review of contributions on relief distribution networks in response to disasters. Through a systematic and scientific methodology, it gathers and consolidates the published research works in a transparent and objective way. It pursues three goals. First, to conduct an up-to-date survey of the research in relief distribution networks focusing on the logistics aspects of the problem, which despite the number of previous reviews has been overlooked in the past. Second, to highlight the trends and the most promising challenges in the modeling and resolution approaches and, finally, to identify future research perspectives that need to be explored.

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Section: Location and Network Designmentioning

confidence: 99%

Section: Analysis Of the Reviewed Literaturementioning

confidence: 99%

Section: Analysis Of the Reviewed Literaturementioning

confidence: 99%

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Relief distribution networks: a systematic review

2014

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“…Huang et al [17] propose a variation of the p-center model based on the additional assumption that a facility could fail to meet demands that it is supposed to satisfy (e.g., if its capacity is exceeded or its functionality is reduced by damage or other factors), which is likely to happen to many facilities during large-scale emergencies. Murali et al [18] formulate a special case of the coverage model using a loss function and chance-constraints. The basic FLMs also can be extended to complex optimization models, including models based on bi-level optimization [19,20], hierarchical models [21], and models that account for dynamic environments [22].…”

Section: Shelter Site Locationmentioning

confidence: 99%

Planning Emergency Shelters for Urban Disaster Resilience: An Integrated Location-Allocation Modeling Approach

Zhao

Sun

et al. 2017

Sustainability

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Abstract:In recent years, extreme natural hazards threaten cities more than ever due to contemporary society's high vulnerability in cities. Hence, local governments need to implement risk mitigation and disaster operation management to enhance disaster resilience in cities. Transforming existing open spaces within cities into emergency shelters is an effective method of providing essential life support and an agent of recovery in the wake of disasters. Emergency shelters planning must identify suitable locations for shelters and reasonably allocate evacuees to those shelters. In this paper, we first consider both the buildings' post-disaster condition and the human choice factor that affect evacuees' decision, and propose a forecasting method to estimate the time-varying shelter demand. Then we formulate an integrated location-allocation model that is used sequentially: an emergency shelter location model to satisfy the time-varying shelter demand in a given urban area with a goal of minimizing the total setup cost of locating the shelters and an allocation model that allocates the evacuees to shelters with a goal of minimizing their total evacuation distance. We also develop an efficient algorithm to solve the model. Finally, we propose an emergency shelters planning based on a case study of Shanghai, China.

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“…In emergency response systems and healthcare facilities, it is important to develop a location plan which allows serving maximum number of people with limited resources on hand. In [30], researchers study the problem of determining the locations for delivering medicines in a large city under a possible bio-terror attack. They develop a special case of the MCLP which considers the distance-dependent coverage and demand uncertainty and apply the model to a possible anthrax attack scenario in the city of Los Angeles.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-criteria assessment of the p-median, maximal coverage and p-center location models

2017

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Original scientific paper Location problems consider locating facilities with the objective of finding their best locations. In most real world problems, it is common that a demand node is required to be covered with multiple facilities in order to ensure a backup supply. The backup supply is necessary especially for public or emergency service location problems where a covered demand may not be serviced if it's designated facility is engaged serving other demands. In this study we consider three classic location models, i.e. p-median, maximal coverage and p-center, and compare their performances with respect to seven decision criteria under Q-coverage requirement. For this purpose, we generate multiple problem instances and solve each instance with the three models for different Q-values. Our numerical results reveal the pros and cons of each model to assist decision makers in determining the most promising model with respect to each assessment criteria. Keywords: backup coverage; facility location; maximal coverage; p-center; p-median Višekriterijska procjena modela lokacije p-srednje, maksimalne pokrivenosti i p-centralnoIzvorni znanstveni članak Problemi lokacije znače lociranje objekata s ciljem pronalaženja najbolje lokacije. Kod većine stvarnih problema uobičajeno je da zahtijevani čvor zadovoljava višestruke uvjete kako bi osigurao rezervnu opskrbu. Ta je rezervna opskrba posebno potrebna kod javnih ili problema lokacije hitne službe kada se pokrivena potražnja ne može osigurati ako je njezin za to određeni objekt zauzet pružanjem usluge drugim zahtijevima. U ovom radu razmatramo tri klasična modela lokacije, t.j. p-srednje, maksimalne pokrivenosti i p-centralno, te uspoređujemo njihovo funkcioniranje u odnosu na sedam kriterija za donošenje odluke u okviru zahtjeva Q-pokrivenosti (Q-coverage). U tu svrhu generiramo slučajeve višestrukih problema i svaki slučaj rješavamo s tri modela za različite Q vrijednosti. Naši numerički rezultati otkrivaju argumente za i protiv svakog modela koji pomažu u donošenju odluke o određivanju modela koji bi najviše odgovarao u odnosu na svaki od kriterija koji se uzimaju u obzir pri donošenju procjene.

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Facility location under demand uncertainty: Response to a large-scale bio-terror attack

Cited by 151 publications

References 35 publications

Relief distribution networks: a systematic review

Relief distribution networks: a systematic review

Planning Emergency Shelters for Urban Disaster Resilience: An Integrated Location-Allocation Modeling Approach

A multi-criteria assessment of the p-median, maximal coverage and p-center location models

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