A lexicographical dynamic flow model for relief operations

Tirado, Gregorio; Martín-Campo, F. Javier; Vitoriano, Begoña; Ortuño, M.

doi:10.1080/18756891.2014.853930

Cited by 15 publications

(15 citation statements)

References 20 publications

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“…Bozorgi-Amiri et al [20] propose a robust stochastic compromise programming model for disaster relief logistics, minimizing the sum of the expected value and the variance of the total cost and minimizing the sum of the maximum unsatisfied demands. Tirado et al [21] consider four attributes (amount of aid distributed, time of the operation, equity and cost) in a dynamic flow model that is solved via lexicographical goal programming. Huang et al [22] develop a quadratic multi-objective programming model to face an allocation and distribution problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The data for the Haiti test case come from various documents available in January 2010: a logistic map provided by the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) [44], a map reporting the satellite-identified IDP concentrations, road and bridge obstacles in central Port-au-Prince, Haiti, created by the United Nations Institute for Training and Research (UNITAR), and a map focused only on the Port-au-Prince road conditions created by the World Food Programme (WFP) Emergency Preparedness and Response [45]. In Figure 1 a map of the region is presented, showing the network nodes (depots labeled 1-3, demanding nodes labeled by 10,12,13,16,17,18,20,21,22, and the rest for intermediate nodes) and all available links. Links are shown in different color depending on their reliability and different thickness depending on their quality (determining the maximum speed of the lorries travelling through them).…”

Section: Description Of the Test Casesmentioning

confidence: 99%

“…• If the current node is a node i 0 different from the source, a candidate element is a node i linked to i 0 through an arc k = (i 0 , i) with positive residual capacity. The effective pheromone of each candidate node, ζ = i, is calculated as stated in (21). It depends positively on the amount of aid transported through arc k in the previously obtained solutions and negatively on the amount of aid transported through arc k in the current solution.…”

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confidence: 99%

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A New Ant Colony-Based Methodology for Disaster Relief

2020

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Humanitarian logistics in response to large scale disasters entails decisions that must be taken urgently and under high uncertainty. In addition, the scarcity of available resources sometimes causes the involved organizations to suffer assaults while transporting the humanitarian aid. This paper addresses the last mile distribution problem that arises in such an insecure environment, in which vehicles are often forced to travel together forming convoys for security reasons. We develop an elaborated methodology based on Ant Colony Optimization that is applied to two case studies built from real disasters, namely the 2010 Haiti earthquake and the 2005 Niger famine. There are very few works in the literature dealing with problems in this context, and that is the research gap this paper tries to fill. Furthermore, the consideration of multiple criteria such as cost, time, equity, reliability, security or priority, is also an important contribution to the literature, in addition to the use of specialized ants and effective pheromones that are novel elements of the algorithm which could be exported to other similar problems. Computational results illustrate the efficiency of the new methodology, confirming it could be a good basis for a decision support tool for real operations.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Description Of the Test Casesmentioning

confidence: 99%

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confidence: 99%

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A New Ant Colony-Based Methodology for Disaster Relief

2020

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“…Bozorgi-Amiri et al (2013) proposed a multi-objective robust stochastic programming in order to optimize humanitarian relief operations in both the preparedness (e.g., pre-positioning) and response phases simultaneously (and distribution). Tirado et al (2014) proposed the lexicographical dynamic flow model to solve the problem of relief item distribution in a humanitarian logistic supply chain. They tried to involve the criteria that were related to cost, time, and equity to obtain a better response in disasters.…”

Section: Literature Reviewmentioning

confidence: 99%

Pre-positioning and dynamic operations planning in pre- and post-disaster phases with lateral transhipment under uncertainty and disruption

et al. 2019

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Humanitarian organizations preposition relief items in pre-disaster and distribute them to the affected areas in post-disaster. Improper planning of emergency operations causes more deaths in post-disaster. In this paper, the problem of relief item pre-positioning and multi-period distribution planning is addressed considering lateral transhipment among distribution centres to improve the efficiency of the humanitarian relief chain. The proposed model not only improves cost efficiency of the relief chain, but also enhances the equity and fairness. Therefore, a multi-objective two-stage stochastic programming, which involves imprecise parameters, is developed to address the problem. Moreover, TH method is utilized to solve the proposed multi-objective programming and efficient possibilistic programming is adopted to deal with the imprecise input parameters. Applicability of the proposed model and the effectiveness of the solutions are examined through a numerical analysis. Finally, sensitivity analyses are conducted on key input parameters to extract managerial insights.

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“…Each one of them comprises important logistic operations that must be planned in the most effective and efficient way. Research on these phases may focus on specific types of disasters, such as hurricanes, typhoons and cyclones [4], earthquakes [5], floods [6] or wildfires [7]; or it can address specific problems along the cycle, such as location [6], emergency mitigation [5,8], prepositioning of aid distribution centers [9], transportation and last mile distribution [10][11][12] or evacuation problems [13,14].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Supported Evacuation for Disaster Relief through Lexicographic Goal Programming

et al. 2020

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Disasters have been striking human-beings from the beginning of history and their management is a global concern of the international community. Minimizing the impact and consequences of these disasters, both natural and human-made, involves many decision and logistic processes that should be optimized. A crucial logistic problem is the evacuation of the affected population, and the focus of this paper is the planning of supported evacuation of vulnerable people to safe places when necessary. A lexicographic goal programming model for supported evacuation is proposed, whose main novelties are the classification of potential evacuees according to their health condition, so that they can be treated accordingly; the introduction of dynamism regarding the arrival of potential evacuees to the pickup points, according to their own susceptibility about the disaster and the joint consideration of objectives such us number of evacuated people, operation time and cost, among which no trade-off is possible. The performance of the proposed model is evaluated through a realistic case study regarding the earthquake and tsunami that hit Palu (Indonesia) in September 2018.

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A lexicographical dynamic flow model for relief operations

Cited by 15 publications

References 20 publications

A New Ant Colony-Based Methodology for Disaster Relief

A New Ant Colony-Based Methodology for Disaster Relief

Pre-positioning and dynamic operations planning in pre- and post-disaster phases with lateral transhipment under uncertainty and disruption

Supported Evacuation for Disaster Relief through Lexicographic Goal Programming

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