Mass casualty incidents are a concern in many urban areas. A community’s ability to cope with such events depends on the capacities and capabilities of its hospitals for handling a sudden surge in demand of patients with resource-intensive and specialized medical needs. This paper uses a whole-hospital simulation model to replicate medical staff, resources, and space for the purpose of investigating hospital responsiveness to mass casualty incidents. It provides details of probable demand patterns of different mass casualty incident types in terms of patient categories and arrival patterns, and accounts for related transient system behavior over the response period. Using the layout of a typical urban hospital, it investigates a hospital’s capacity and capability to handle mass casualty incidents of various sizes with various characteristics, and assesses the effectiveness of designed demand management and capacity-expansion strategies. Average performance improvements gained through capacity-expansion strategies are quantified and best response actions are identified. Capacity-expansion strategies were found to have superadditive benefits when combined. In fact, an acceptable service level could be achieved by implementing only 2 to 3 of the 9 studied enhancement strategies. (Disaster Med Public Health Preparedness. 2018;12:778-790)

Natural or human-made incidents in urban areas cause surges in demand for hospitals while often simultaneously limiting their capacity to serve patients due to direct physical damage or service interruptions in supporting lifelines. A hierarchical modeling concept is proposed to quantify the resilience of regional hospital response under disaster. To facilitate the optimization of pre- and postevent resilience-enhancing actions for a range of potential hazard-demand-damage scenarios, a multistage stochastic, mixed-integer decision problem (SMIP) is proposed. Dependencies on interdependent, potentially damaged critical lifelines are explicitly modeled. Resilience is estimated in terms of total patient waiting time and unserved patients. These quantities are captured in the objective of the SMIP through the adoption of a simulation-based metamodel of a detailed hospital model. The SMIP is demonstrated in a numerical example developed to represent the main hospitals of the Johns Hopkins Health System Corporation. The proposed structure is broadly applicable to other societal functions that take place in interrelated buildings connected by transportation and communications links and are dependent on interdependent lifeline networks.

A dynamic intermodal multiproduct freight network simulation–assignment model is presented with application to a large-scale inter-modal rail network. Methodology pertaining to the simulation–assignment framework and representation and implementation considerations in dealing with large networks are discussed. The model is applied to a pan-European rail network spanning 11 countries from Scandinavia to Greece. Shipper decisions are disaggregated to an individual shipment level using a dynamic microassignment methodology in which a joint mode, path, service, and carrier choice is made. A mode choice function for this joint choice is calibrated, and issues relating to heterogeneity of freight characteristics are discussed. The model is validated by analyzing convergence patterns and measures of performance, namely, mode splits, compared with other studies. Two examples demonstrate the role of the model as a policy and decision support tool. The impact of expedited operations at international borders and infrastructure improvements on rail services is quantified.

Maintaining roadway pavements and bridge decks is key to providing high levels of service for road users. However, improvement actions incur downtime. These actions are typically scheduled by asset class, yet implemented on any asset type, they have network-wide impacts on traffic performance. This paper presents a bilevel program wherein the upper level involves a Markov decision process (MDP) through which potential roadway improvement actions across asset classes are prioritized and scheduled. The MDP approach considers uncertainty in component deterioration effects, while incorporating the benefits of implemented improvement actions. The upper level takes as input traffic flow estimates obtained from a lower-level user equilibrium traffic formulation that recognizes changes in capacities determined by decisions taken in the upper level. Because an exact solution of this bilevel, stochastic, dynamic program is formidable, a deep reinforcement learning (DRL) method is developed. The model and solution methodology were tested on a hypothetical problem from the literature. The importance of obtaining optimal activity plans that account for downtime effects, traffic congestion impacts, uncertainty in deterioration processes, and multiasset classes is demonstrated.

Objective: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. Methods: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care. Results: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. Conclusions: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed.

This paper deals with quantifying the resilience of a network of pavements. Calculations were carried out by modeling network performance under a set of possible damage–meteorological scenarios with known probability of occurrence. Resilience evaluation was performed a priori while accounting for optimal preparedness decisions and additional response actions that can be taken under each of the scenarios. Unlike the common assumption that the pre-event condition of all system components is uniform, fixed, and pristine, component condition evolution was incorporated herein. For this purpose, the health of the individual system components immediately prior to hazard event impact, under all considered scenarios, was associated with a serviceability rating. This rating was projected to reflect both natural deterioration and any intermittent improvements due to maintenance. The scheme was demonstrated for a hypothetical case study involving LaGuardia Airport. Results show that resilience can be impacted...