An approximate hypercube model for public service systems with co-located servers and multiple response

Ansari, Sardar; Yoon, Soovin; Albert, Laura A.

doi:10.1016/j.tre.2017.04.013

Cited by 16 publications

(5 citation statements)

References 33 publications

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“…On the basis of the MHN and the ERG theory, plenty of previous research related to community services has been referred to systematically classify residents’ demands. Community services in smart communities can be split into seven categories according to existing literature (i.e., smart business service [ 38 , 39 , 40 , 41 , 42 , 43 ], smart property service [ 38 , 39 , 40 , 44 , 45 , 46 , 47 , 48 ], smart emergency service [ 38 , 39 , 45 , 46 , 49 , 50 ], smart medical care service [ 38 , 45 , 51 , 52 ], smart elderly care service [ 38 , 39 , 53 , 54 , 55 , 56 , 57 ], smart communication service [ 39 , 40 , 45 , 58 ], and smart government service [ 40 , 44 , 58 , 59 , 60 , 61 , 62 ]), and there are a variety of services within each category [ 38 , 44 , 58 ]. A list of these services is provided in Table 1 (Detailed information about different types of community services can be seen in Supplementary File S1 ).…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

Chen

Wang

et al. 2023

IJERPH

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Smart community enables a sustainable and livable community future, in which residents’ demands play an important role in its success. Though great efforts have been made to encourage residents’ participation in the implementation of smart communities, inefficient service supply still exists. Thus, this study aimed to classify residents’ demands for community services in smart communities and to explore relevant influencing factors based on the developed conceptual framework. Data from 221 respondents in Xuzhou city of China were analyzed by using binary logistic regression. The results indicated that more than 70% of respondents had demands for all community services in smart communities. Moreover, the demands were influenced by distinct factors, including sociodemographic characteristics, living characteristics, economic characteristics, and individual attitude characteristics. The types of community services in smart communities are clarified and fresh insights are provided into associated factors related to residents’ demands for these services in this study, through which enhanced provision of community services and effective implementation of smart communities can be achieved.

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Section: Literature Review and Hypothesesmentioning

confidence: 99%

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

Chen

Wang

et al. 2023

IJERPH

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“…To deal with the high computational efforts involved, Larson (1975) later developed the approximate HQM. Atkinson et al (2006), Budge et al (2009) and Ansari et al (2017) also developed approximation techniques to reduce computational burdens. Recent extensions of the basic HQM include the modeling of real-world waiting lines in order to preserve calls that cannot be served immediately (Iannoni et al, 2015;De Souza et al, 2015;Rodrigues et al, 2017).…”

Section: Emergency Vehicles For Pandemic Outbreaksmentioning

confidence: 99%

A spatial queuing model for the location decision of emergency medical vehicles for pandemic outbreaks: the case of Za'atari refugee camp

Blank

2021

JHLSCM

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PurposeRefugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.Design/methodology/approachA spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.FindingsThe derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.Research limitations/implicationsSome parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.Practical implicationsThe model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.Originality/valueThe study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

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“…The final type is the hypercube queuing models that consider the spatial distribution as well as the status of vehicles (i.e. these models differentiate between both idle and busy vehicles) as well as coordination among the fleet (Larson 1974;Larson 1975;Jarvis 1985;Goldberg & Paz 1991;Batta et al 1989;Takeda et al 2007;Ansari et al 2017).…”

Section: Accessibilitymentioning

confidence: 99%

Measuring the spatial accessibility to fire stations using enhanced floating catchment method

Corcoran

Chhetri

2020

Socio-Economic Planning Sciences

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Enhancing the spatial accessibility of population to fire services is a key strategy to help improve emergency response, minimise property loss, and reduce injuries and deaths. Given its significance for fire service policy and strategic planning, we draw on small area population forecasts, fire station locations and the road network, and employ the enhanced two-step floating catchment method to compute the levels of spatial accessibility of population to fire services in relation to current and future population growth in Brisbane, Australia. Results show that lower levels of spatial accessibility to fire services exist in the most populated areas compared to those that are least populated. Further, we reveal that some areas are more likely to suffer a reduction in spatial accessibility than others. Spatial accessibility of population to fire services over the period to 2036 is likely to reduce as a consequence of rising demand for service. The identification of locales that experience lower levels of accessibility to fire services will enable fire services agencies to strategically plan infrastructure investment and help enhance the operational efficiency of emergency response.

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An approximate hypercube model for public service systems with co-located servers and multiple response

Cited by 16 publications

References 33 publications

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

A spatial queuing model for the location decision of emergency medical vehicles for pandemic outbreaks: the case of Za'atari refugee camp

Measuring the spatial accessibility to fire stations using enhanced floating catchment method

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