A Hybrid Capacitated Multi-facility Location Model for Pre-positioning Warehouses and Inventories in Regions at Risk in Mexico

Barojas-Payán, Erika; Sánchez‐Partida, Diana; Caballero‐Morales, Santiago‐Omar; Martínez‐Flores, José‐Luis

doi:10.1007/978-3-030-67295-9_9

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In the location-allocation research area of medical support vehicles (ambulances), Zaffar and colleagues [7] found that the main objective of most state-of-the-art work is related to finding the best distribution of ambulances to maximize coverage and minimize response times. Mohri and Haghshenas [8] analyzed the problem using the Ambulance Location Problem (ALP) as an extension of the Set Coverage Problem introduced by Toregas [9]; Kavhe and Mesgari combined the Maximum Coverage Location Problem (MCLP) and metaheuristics [10]; Hashtarkhani et al proposed the use of MCLP and combinatorial optimization techniques [11]; while Barojas-Payán et al [12] addressed the problem using the p-median problem.…”

Section: Related Workmentioning

confidence: 99%

“…The value of 9 min is a standard recommended by [47] and is used in the models described in [7,48]. However, this is an ideal case, so they propose the range [9][10][11][12][13] min. Our assumption is that there is at least one ambulance on standby in each location, which is dispatched the moment a victim is confirmed, and another unit is immediately requested from the corresponding base, even if no other victim is confirmed.…”

Section: Parameter Selectionmentioning

confidence: 99%

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Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Medina-Perez,

Guzmán,

Saldana-Perez

et al. 2024

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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

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