Determining optimal fuel delivery strategies under uncertainty

During the preparation before a hurricane makes landfall, affected individuals may be asked to evacuate. Large and small-scale evacuations can cause rapid increases in the demand for gasoline fuel. However, during a hurricane vessels carrying gas may be delayed and/or rerouted, adding to the difficulty of providing the necessary gas in affected areas. In this work, we determine alternate delivery locations and times for vessels carrying fuel that are scheduled to arrive and deliver fuel at ports impacted by an approaching hurricane. Motivated by Hurricane Irma in Florida, we develop a multi-period stochastic scheduling model that incorporates hurricane (weather) advisories, fuel delivery schedules, port storage capacities, and port docking capacities. Our model determines the best schedule based on two objectives: (1) minimize the total unmet demand at each port, and (2) minimize inequities in unmet demands among the ports. We also present a case study and a numerical experiment based on fuel delivery data from ports in Florida. Among our key findings is that port availability is the driving factor in determining feasible schedules for vessel gas deliveries. We also present a scheduling heuristic that dynamically adapts to weather advisories so as to minimize the impact of unmet demand in the affected areas.     

A goal programming model for the siting of multilevel EMS systems.

Facility siting models known as location covering techniques have proven to be useful particularly for emergency medical services (EMS) planning, given the importance of ambulances responding to demand within some maximum time constraint. These models represent a set of methods which focus the health planner's attention on the access of people to health care, since they attempt to “cover” people in need of service within some specified time standard.This research develops a technique for the locational planning of sophisticated EMS systems, characterized by multiple levels of emergency health services. Specifically, a two-tiered system with “basic life support” and “advanced life support” capabilities is modeled as a goal program.By applying location covering techniques within a goal programming framework, this study develops a method for the siting of multilevel EMS systems so that (1) each service level maximizes coverage of its own demand population, and (2) “back-up” coordination between levels is assured. The usefulness of this goal program as a health planning tool is evidenced in the model's explicit articulation of EMS policy objectives and its ability to link system levels in terms of “goal-directed behavior”. The working of this multilevel covering model is demonstrated by reference to EMS planning scenarios and related numerical examples.     

Data-driven forecasting for operational planning of emergency medical services

Emergency medical services (EMS) play a vital role in delivering pre-hospital care. The operational efficiency of such services is critical and adequate demand forecasts can contribute to such a goal. But for that, the available data need to be well characterized before being used. Previous studies have failed to address some important aspects of this need, such as exploring a comprehensive list of contextual data to decide which are relevant to explain the EMS demand behavior. Moreover, modern forecasting techniques have been explored in the EMS context, including neural networks, but the computational complexity inherent to the methods and their use was not discussed. Finally, it is also unclear how different demand patterns can be when predicting the volume of emergency calls considering the priority level and the number of dispatches according to vehicle type. This study proposes a generic data-driven forecasting method to address these shortcomings and to support operational decisions. The results obtained with the proposed method indicate that each priority call and vehicle type shows different patterns, which suggests that such differentiation should contribute to better resource allocation. At the same time, the operational impact of the demand shared by neighboring zones proved to be significant at bases near the border. The models developed resulted in important decision tools that can be used to predict the dynamic demand of EMS on an hourly or shift basis. Additionally, the method adds value for decision-makers that want to plan not only when and how many but also where resources are demanded, avoiding assumptions that impact the operational performance.     

Ambulance response time in a Brazilian emergency medical service

Research efforts on ambulance response times for Emergency Medical Services (EMS) calls have been made for decades, especially in developed countries, using different techniques and with varying objectives. In Brazil, a developing country, the scarce scientific production on this vital indicator prioritizes scenarios for EMS in cities with more than one million inhabitants. This shows the importance of extending research to the reality of small and medium-sized cities. This paper presents SAMU, the Brazilian EMS that follows the Franco-German emergency medicine model, compiling numbers related to service at the national level. The use of quantile regression allows the identification of the RT for the EMS and helps to explain the effects of factors at the system level, at the patient level, and specific factors on response time intervals of Southwest Paraná SAMU. This specific EMS, characterized as an inter-municipal consortium of prehospital services, is responsible for prehospital emergency care for an approximate population of 635,000 inhabitants in 42 small towns in the State of Paraná in southern Brazil. From the analysis of the records of 12,050 ambulance dispatches, it was possible to identify the average ambulance response time of 14 min and 25 s. The regression model was able to explain the influence of the independent variables at the system level (presumed severity of the emergency, ambulance dispatch time, and ambulance travel time), at the patient level (age, gender, and characteristic of the emergency) and specific variables of the emergency (day of the week and time of day) on the dependent variable response time over the quantiles, showing that the dispatch time, travel time, time of day, service to male patients and critical cases influence the ambulance response time. This work contributes to deepening the understanding of the management of EMS operations in a developing country, allows the comparison of the RT identified in relation to other countries, and identifies factors that impact the RT for other actors directly or indirectly involved. The practical implications are also presented, as well as how the study impacts the decision-making and management process of the EMS in the short, medium and long term.     

应急物流特点及我国应急物流配送机制研究

近几年来全球大规模发生公共事件,例如非典、大洋海啸、大地震、飓风等。类似这样的突发事件给事发地民众留下了难以忘怀的惨痛记忆并造成的巨大损失。本文章探讨了应急物流的定义、特点,指出了我们国家自然灾害救助的应急物流配送现状,设想建立一套较为完善的应急物流配送机制。     

The disaster response performance of hospitals in Taiwan: evaluation and classification

Hospitals need disaster response plannings and temporary shelters to rescue victims in disasters. In Taiwan, there are 82 emergency medical service (EMS) hospitals are requested to possess guildlines of preparedness and responses to mitigation the damage and recovery the pre-event status in a disaster. A measurement chart including three major factors??facility, manpower, and disaster plan and drill mode??was created to evaluate the performance of disaster management of these EMS hospitals. Based on the expert opinions, some hospitals were selected as a reference set prior to the accreditation. Data envelopment analysis (DEA) is applied to classify each of the remaining hospitals into qualified or unqualified classes. Then, each unqualified hospital was recommended to improve its practice of disaster preparedness and responses into the qualified level. We also find that, on average, private hospitals perform better than public hospitals and medical centers perform better than the regional hospitals. But, the differences are not statistically significant.     

Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand

To ensure a timely response to emergencies, governments are obliged to implement effective ambulance allocation plans. In practice, an emergency medical service (EMS) system works in an uncertain environment, with stochastic demand, response-times, and travel-times. This uncertainty significantly affects ambulance allocation planning. However, few studies in this field adequately consider the effect of spatiotemporal uncertainty in demand, because it is difficult to measure it quantitatively. As a result, few analytic models capture the dynamic nature of an EMS system and, thus, the allocation plans they generate are not efficient in practice. Therefore, this study proposes a simulation-based optimization method for ambulance allocation. A simulation model is constructed to mimic the operational processes of an EMS system, and to evaluate the performance of an ambulance allocation plan in an uncertain environment. Gaussian mixture model clustering is used to derive the uncertain spatial demand. Then, the simulation generates emergency demand based on the obtained spatial distribution. A Gaussian-process-based search algorithm is used together with the simulation model to identify optimal solutions. To validate the proposed method, a case study is conducted using data on emergency patients in the Shanghai Songjiang District. Compared with the current plan adopted in Songjiang, the experimental results demonstrate that the delay time and frequency of the EMS system can be reduced significantly by employing the proposed methods. Furthermore, nearly 41% of the allocation cost can be saved.     

Coordination Through Contracting: Experience with the Estonian: out-of-hospital emergency medicine

Abstract

The article examines the effects of market-type contracting on the capacity of the government to integrate public service stakeholders and to assure coherence in service provision. The study focuses on the case of the Estonian out-of-hospital emergency medical care and analyses it through an analytical framework, concentrating on basic coordination mechanisms, coordination resources and their application in a specific policy field. It is found that effective market-based coordination presumes long-term learning and the use of various coordination resources that go beyond simple bargaining. In addition, contracting for service delivery has a significant influence on the capacity of the government to coordinate both policy-making and the interlinkages of different policies.     

Political Authority and Bureaucratic Resilience

Abstract

Consumerism and choice have become prominent ideas in the design and delivery of public services. Often perceived as a way to improve the quality and value of public services, potential downsides and areas of concern that relate to a consumerist approach are frequently ignored. This review essay takes a critical stance on the application of a consumerist discourse to public service provision and management by exploring four key areas of concern: definitional problems, questions about the concept's transferability from a private to a public sector setting, the problematic nature of ‘choice’, and difficulties associated with implementing consumerist ideas within public service contexts..     

突发公共卫生事件的 应急医疗资源配置研究——基于网格化管理和大数据平台

应急医疗资源是应对突发公共卫生事件最重要的物质基础和保障。在对前人研究进行总结的基础上,分析了目前我国在突发公共卫生事件时应急医疗资源调配机制的不足和网格化管理应用到资源配置中的可行性和优越性,以此将大数据平台和网格化管理模式引入到应急医疗资源的配置研究中。在对应急医疗资源的配置过程进行网格化模型设计的基础上,根据我国应急医疗资源配置的需要和特点,确定了网格系统中的基本单元,即网格划分,并选择五层沙漏模型作为突发公共卫生事件时应急医疗资源配置的网格模型架构,以期完善我国的应急资源管理体系,进一步提高我国在突发公共卫生事件时应急医疗资源的调配和管理水平。     

从宁夏突发公共卫生事件看宁夏疾控机构图书馆(室)信息服务工作

针对宁夏突发公共卫生事件,探讨宁夏各级疾控机构图书馆(室)如何有效加强图书馆信息资源管理,转变服务模式,以促进宁夏图书馆(室)信息服务水平的进一步提升。     

Integrating expected coverage and local reliability for emergency medical services location problems

Daskin's MEXCLP model [Daskin M. A maximum expected covering location model: formulation, properties, and heuristic solution. Transportation Science 1983;17:48-70] was one of the first efforts to capture the stochastic nature of emergency medical services (EMS) location problems within a mixed-integer formulation. With their subsequent introduction of MALP, ReVelle and Hogan [The maximum availability location problem. Transportation Science 1989;23:192-200] offered two key advances, local vehicle busyness estimates and the α-reliability objective. While these constructs have influenced many subsequent EMS location models, they have been subjected to relatively little empirical analysis. To address this, we introduce the LR-MEXCLP, a hybrid model combining the local busyness estimates of MALP with the maximum coverage objective of MEXCLP. We then solve a series of problems with all three models and employ simulation to estimate aggregate service levels. We find that LR-MEXCLP leads to modest but consistent service gains over both MALP and MEXCLP. These results support the merits of local busyness estimates, but they also suggest that the α-reliability objective may be inappropriate when seeking to maximize aggregate system response capabilities. More generally, our research underscores the utility of (a) linking modeling assumptions and goals with real-world application contexts, and (b) employing simulation or other techniques to validate theoretical results.     

应急物流服务网点选址模型研究

应急物流是应对突发事件的重要保障。应急物流可以降低灾害影响程度,缩短受害持续时间,使突发性事件造成损失最小化。而在进行应急物流决策时,首先会面临应急物流服务网点的选址问题。应急物流服务网点置于合理的位置,不仅可以降低成本,而且还能够保证提供应急物资的时效性,从而避免可能导致的更大损失。在给定限制条件下应急服务网点选址模型基础上,综合考虑应急需求的随机性和由灾害导致应急配送时间模糊的不确定情况,研究新的模糊和随机混合机会约束规划选址模型。     

Forecasting extreme financial risk: A score-driven approach

This paper develops a new class of dynamic models for forecasting extreme financial risk. This class of models is driven by the score of the conditional distribution with respect to both the duration between extreme events and the magnitude of these events. It is shown that the models are a feasible method for modeling the time-varying arrival intensity and magnitude of extreme events. It is also demonstrated how exogenous variables such as realized measures of volatility can easily be incorporated. An empirical analysis based on a set of major equity indices shows that both the arrival intensity and the size of extreme events vary greatly during times of market turmoil. The proposed framework performs well relative to competing approaches in forecasting extreme tail risk measures.     

Pre-positioning hurricane supplies in a commercial supply chain

Inventory control for retailers situated in the projected path of an observed hurricane or tropical storm can be challenging due to the inherent uncertainties associated with storm forecasts and demand requirements. In many cases, retailers react to pre- and post-storm demand surge by ordering emergency supplies from manufacturers posthumously. This wait-and-see approach often leads to stockout of the critical supplies and equipment used to support post-storm disaster relief operations, which compromises the performance of emergency response efforts and proliferates lost sales in the commercial supply chain. This paper proposes a proactive approach to managing disaster relief inventories from the perspective of a single manufacturing facility, where emergency supplies are pre-positioned throughout a network of geographically dispersed retailers in anticipation of an observed storm's landfall. Once the requirements of a specific disaster scenario are observed, supplies are then transshipped among retailers, with possible direct shipments from the manufacturer, to satisfy any unfulfilled demands. The manufacturer's pre-positioning problem is formulated as a two-stage stochastic programming model which is illustrated via a case study comprised of real-world hurricane scenarios. Our findings indicate that the expected performance of the proposed pre-positioning strategy over a variety of hurricane scenarios is more effective than the wait-and-see approach; currently used in practice.     

Co-Production and the Co-Creation of Value in Public Services: A suitable case for treatment?

Co-production is currently one of cornerstones of public policy reform across the globe. Inter alia, it is articulated as a valuable route to public service reform and to the planning and delivery of effective public services, a response to the democratic deficit and a route to active citizenship and active communities, and as a means by which to lever in additional resources to public service delivery. Despite these varied roles, co-production is actually poorly formulated and has become one of a series of ‘woolly-words’ in public policy. This paper presents a conceptualization of co-production that is theoretically rooted in both public management and service management theory. It argues that this is a robust starting point for the evolution of new research and knowledge about co-production and for the development of evidence-based public policymaking and implementation.     

Modeling emergency medical response to a mass casualty incident using agent based simulation

Emergency managers have to develop plans for responding to disasters within their jurisdiction. This includes coordinating multiple independent agencies participating in the response. While much of this is currently done by use of intuition and expert judgment, models can be used to test assumptions and examine the impact of policies and resource levels. The autonomous nature of responders as well as the rapidly changing information during a disaster suggests that agent based models can be especially suited for examining policy questions. In this work, we built an agent based model of a given urban area to simulate the emergency medical response to a mass casualty incident (MCI) in that area. The model was constructed from publicly available geographic information system and data regarding available response resources (such as ambulances, EMS personnel and hospital beds). Three different agent types are defined to model heterogeneous entities in the system. By simulating various response policies, the model can inform emergency responders on the requirements and response protocols for disaster response and build intuition and understanding in advance of facing actual incidents that are rare in the day-to-day operating experiences.     

Sustainable supply chains: An introduction

Consideration is given to the convergence of supply chains and sustainability. In doing so, the focus on environmental management and operations is moved from local optimization of environmental factors to consideration of the entire supply chain during the production, consumption, customer service and post-disposal disposition of products. This is a critical and timely topic that captures increasing concerns over sustainability, whether driven by current legislation, public interest, or competitive opportunity. As such, sustainable development is a rich area for academic research that is still in its infancy and has the potential to affect future government policy, current production operations, and identify new business models. This paper provides a background to better understand current trends in this multidisciplinary field that intersect with operations management, and the research opportunities and challenges it presents.     

The Impact of Climate Change on Agriculture in the Southwestern United States: The Ricardian Approach Revisited

This paper estimates a Ricardian model of farmland value across the counties of the semiarid Southwestern United States. Compared to previous contributions, we focus on one climate zone and include the presence of extreme weather events and of farm subsidies in our analysis. We also control for heterogeneity and for various types of spillover effects. Once calibrated, the model is used to project changes due to future climate conditions. We find that the probability of a decrease is great in highland counties while an increase or decrease is equally probable in lowland counties where climate impacts farmland value less.     

Assessing hospital system resilience to disaster events involving physical damage and Demand Surge

This paper investigates the effectiveness of formalized collaboration strategies through which patients can be transferred and resources, including staff, equipment and supplies, can be shared across hospitals in response to a disaster incident involving mass casualties and area-wide damage. Inflicted damage can affect hospital infrastructure and its supporting lifelines, thus impacting capacity and capability or, ultimately, services that are provided. Using a discrete event simulation framework and underlying open queuing network conceptualization involving patient flows through 9 critical units of each hospital, impacts on critical resources, physical spaces and demand are modeled and the hospital system's resilience to these hazard events is evaluated. Findings from numerical experiments on a case study involving multiple hospitals spaced over a large metropolitan region replicating a system similar to the Johns Hopkins Hospital System show the potential of strategies involving not only transfers and resource sharing, but also joint capacity enhancement alternatives to improve post-disaster emergency health care service delivery through joint action.