A Chance Constrained Programming Model for Reliable Emergency Vehicles Relocation Problem

Liu, Yang; Yuan, Yun; Li, Yuhui; Pang, Hao

doi:10.1016/j.sbspro.2013.08.078

Cited by 16 publications

(6 citation statements)

References 30 publications

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“…Considering better computing capabilities available nowadays, many researchers include more features in the modeling process to obtain more realistic models. In this sense, it is important to highlight the inclusion of stochasticity in demand in [67,81,86,88,89,95,[113][114][115][116][117][118][119][120][121][122][123][124][125][126][127][128]. There are different ways to address stochasticity, but one of the prominent research areas is machine learning.…”

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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Section: Modeling Issuesmentioning

confidence: 99%

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

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“…This problem is formulated as a bi-objective stochastic mixed-integer nonlinear program and approached via linearization and epsilon-constraint methods. Another well known application of the re-location (re-balancing) problem is the one that deals with emergency vehicles [17,18]. For vehicle routing problems dealing with specialized technicians, balancing issues in the workload of technicians were modeled in [19] and solved with a bi-objective optimization model [20].…”

Section: Introductionmentioning

confidence: 99%

The Unit Re-Balancing Problem

2021

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We describe the problem of re-balancing a number of units distributed over a geographic area. Each unit consists of a number of components. A value between 0 and 1 describes the current rating of each component. By a piecewise linear function, this value is converted into a nominal status assessment. The lowest of the statuses determines the efficiency of a unit, and the highest status its cost. An unbalanced unit has a gap between these two. To re-balance the units, components can be transferred. The goal is to maximize the efficiency of all units. On a secondary level, the cost for the re-balancing should be minimal. We present a mixed-integer nonlinear programming formulation for this problem, which describes the potential movement of components as a multi-commodity flow. The piecewise linear functions needed to obtain the status values are reformulated using inequalities and binary variables. This results in a mixed-integer linear program, and numerical standard solvers are able to compute proven optimal solutions for instances with up to 100 units. We present numerical solutions for a set of open test instances and a bi-criteria objective function, and discuss the trade-off between cost and efficiency.

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“…The systems used endeavored to enhance systematically the whole process of repositioning emergency ambulances and timing their journeys. Real advances in this area paralleled the advent of new technologies related to algorithms, including advanced telecommunications systems and digital mapping (Liu et al, 2013). The availability of cheap computing power and tracking systems made the repositioning decisions even more efficient.…”

Section: Introductionmentioning

confidence: 99%

A Bi-objective Covering Location Problem

Itani

Abdelaziz

Masri

2019

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Purpose Ambulance response time is an important factor in saving lives and is highly linked with the ambulance location problem. The Maximum Expected Covering Location Problem (MEXCLP), introduced by Daskin (1983), is one of the most used ambulance location models that maximize the probability of stratifying demands for emergency medical service (EMS) centers. Due to huge increase in the operational costs of EMS centers, ambulance location models must consider the cost of coverage and the opportunity to use other companies’ private ambulances to answer emergency calls. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors propose to extend the MEXCLP to a bi-objective optimization problem where the cost of satisfying emergency calls is minimized. Findings The proposed model is tested using data retrieved from the Lebanese Red Cross (LRC) in Beirut capital of Lebanon. The reported findings show significant enhancements in the results where the LRC can fully satisfy the perceived demands from all areas in Beirut within 9 min with an affordable cost. Originality/value The model is a first attempt to reduce operational costs of EMS centers while constraining the response time to satisfy emergency calls at an acceptable rate.

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A Chance Constrained Programming Model for Reliable Emergency Vehicles Relocation Problem

Cited by 16 publications

References 30 publications

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

The Unit Re-Balancing Problem

A Bi-objective Covering Location Problem

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