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Health care spending usually contributes to a large part of a developed country's economy. In 2011, the U.S. consumed about 17.7% of its GDP on health care. As one of the most significant components of the health care industry, the hospital sector plays a key role to provide healthcare services. Healthcare services industry can be affected by many factors, of which economic downturn is a crucial one. As a result, it is worth investigating the condition and state of hospital management when economic downturn occurs. This paper aims to analyze how the Great Recession affects hospital performance in Pennsylvania during the period 2005–2012 by using data envelopment analysis (DEA). Specifically, we measure efficiency for hospitals in Pennsylvania, and use several DEA models to calculate the global Malmquist index (GMI). We find that: (1) 15.4% hospitals are always efficient while 36.9% hospitals are always inefficient for all years in 2005–2012; (2) The relative distance for a group of hospitals to the frontier is almost unchanged post-recession and pre-recession; (3) The average efficiency/GMI decreases by 2.43%/3.07% from pre-recession to post-recession. The analysis indicates that hospital performance slightly decreased due to the economic downturn in Pennsylvania. [ABSTRACT FROM AUTHOR]

The health care sector is one of the fastest growing sectors in the United States. Researchers are interested in conducting studies in the area of health economics in order to propose solutions to curb the rapid increase in health care spending and to improve the efficiency of the health care system in the United States. Specifically, hospital efficiency is one important research area in health economics. In this paper, data envelopment analysis (DEA) is used to assess hospital efficiency. An additive super-efficiency model is presented and applied to a sample of general acute care hospitals in Pennsylvania. In addition to the conventional choice of input and output variables, we include the survival rate as a quality measure of health outcome in the set of output variables. Thus our model takes both the quantity and the quality of the output into account. With the results obtained from our proposed DEA model, inefficiencies can be identified for hospitals to address without sacrificing the quality of care. [ABSTRACT FROM AUTHOR]

The inventory routing problem is a distribution problem in which each customer maintains a local inventory of a product such as heating oil and consumes a certain amount of that product each day. Each day a fleet of trucks is dispatched over a set of routes to resupply a subset of the customers. In this paper, we describe and compare algorithms for this problem defined over a short planning period, e.g. one week. These algorithms define the set of customers to be serviced each day and produce routes for a fleet of vehicles to service those customers. Two algorithms are compared in detail, one which first allocates deliveries to days and then solves a vehicle routing problem and a second which treats the multi-day problem as a modified vehicle routing problem. The comparison is based on a set of real data obtained from a propane distribution firm in Pennsylvania. The solutions obtained by both procedures compare quite favorably with those in use by the firm. [ABSTRACT FROM AUTHOR]