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.

The expansion of the Panama Canal will be completed by 2015. The route through the Panama Canal will shorten voyages from North America to Japan by more than 7,500 nautical miles. However, the competition for use of the Canal is high because it is a major route for container ships and other vessels including crude oil, metal ores, and other materials. Therefore, several questions have been raised regarding how much the capacity of the Panama Canal will be available for liquefied natural gas (LNG) passages and how much LNG will go through the Canal. Applying the 2014 version of the World Gas Model, a large-scale, game theory-based global gas model, this paper investigates the influence of the Panama Canal capacity level for LNG tankers on global gas markets and LNG exports from the Gulf of Mexico through five scenarios. The model results show that without the Panama Canal route with its expanded capacity, it is unprofitable to export LNG from the Gulf of Mexico to Asian markets. However, there may be noneconomic reasons such as energy security to motivate such trade. In addition, when Panama Canal capacity is limited, the United States becomes a swing LNG exporter who supplies both Asian and European markets.

In this paper, we propose a timetable optimizer (TO) consisting of the following parts: 1) Demand Forecasting Module 2) Train Optimization Module 3) Timetable Generator Module. TO is a specialized system for planning the train timetable which designed to help solve the problem of determining the number of trains that are suitable and scheduling the train suitably according to the number of trains designated by the system. TO integrates the passenger data from the latest round trip or historical information and forecasts a number of passengers of high-speed trains based on historical data in order to find the optimal number of trains by using a mixed integer programing model. Lastly, the TO system can applies the number of trains to calculate the appropriate train schedule so that the planning cycle is complete. A case study of high-speed railway system in Thailand with 36 trains to satisfy demand of 35,000 passengers/ each direction is conducted. The results show that the proposed system can quickly generate a timetable having an optimal number of train with suitable time interval according to a demand forecasting.