Stochastic facility location problem in a competitive situation: A game theory model for emergency financial services

Abensur, Eder Oliveira; Paes, Alexandre da Silva; Yamada, Erick Reyann Kasai; Ruggieri, Vito Giovanni; Aquino, Walquiria Alves de

doi:10.1080/23311916.2020.1837411

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…Still, one clear limitation of their approach is the assumption that the flight distance of a drone follows an exponential probability distribution. Abensur et al [50] proposed the combined use of game theory and simulations to analyze an FLP under an uncertainty scenario. Another singular aspect of this work is the application context, which is not the traditional logistics one, but one related to financial services.…”

Section: Related Work On Stochastic Facility Location Problemsmentioning

confidence: 99%

A Forward–Backward Simheuristic for the Stochastic Capacitated Dispersion Problem

Gomez,

Martínez-Gavara,

Panadero

et al. 2024

Mathematics

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In an effort to balance the distribution of services across a given territory, dispersion and diversity models typically aim to maximize the minimum distance between any pair of facilities. Specifically, in the capacitated dispersion problem (CDP), each facility has an associated capacity or level of service, and the objective is to select a set of facilities so that the minimum distance between any pair of them (dispersion) is maximized, while ensuring a user-defined level of service. This problem can be formulated as a linear integer model, where the sum of the capacities of the selected facilities must match or exceed the total demand in the network. Real-life applications often necessitate considering the levels of uncertainty affecting the capacity of the nodes. Failure to account for this uncertainty could lead to low-quality or infeasible solutions in practical scenarios. However, research addressing the stochastic version of the CDP is scarce. This paper introduces two models for the CDP with stochastic capacities, incorporating soft constraints and penalty costs for violating the total capacity constraint. The first model includes a probabilistic constraint to ensure the required level of service with a certain probability, while the second model introduces a soft constraint with penalty costs for violations. To solve both variants of the model, a forward–backward simheuristic algorithm is proposed. Our approach combines a metaheuristic algorithm with Monte Carlo simulation, enabling the efficient handling of the random behavior of node capacities and obtaining reliable solutions regardless of their probability distribution.

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Section: Related Work On Stochastic Facility Location Problemsmentioning

confidence: 99%

A Forward–Backward Simheuristic for the Stochastic Capacitated Dispersion Problem

Gomez,

Martínez-Gavara,

Panadero

et al. 2024

Mathematics

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“…The tool has two modules that work together. One of the modules solves the dispatch problem using an allocation heuristic, known as the auction algorithm of Bertsekas [102]. The location module proposes modeling based on a Voronoi graph approach.…”

Section: Modeling Issuesmentioning

confidence: 99%

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

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In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

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A location and capacity-allocation game model for two competing online delivery platforms seeking new customers

Ashraf,

Bardhan

2024

Electron Commer Res

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Stochastic facility location problem in a competitive situation: A game theory model for emergency financial services

Cited by 3 publications

References 45 publications

A Forward–Backward Simheuristic for the Stochastic Capacitated Dispersion Problem

A Forward–Backward Simheuristic for the Stochastic Capacitated Dispersion Problem

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

A location and capacity-allocation game model for two competing online delivery platforms seeking new customers

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