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The methods employed by the ISE discipline offer powerful new insights to understand and improve public health emergency preparedness and response systems. The models can be used by public health practitioners not only to inform their planning decisions but also to provide a quantitative argument to support public health decision making and investment.

We study the problem of transmitting data from a set of sensors to a base-station where the data is to be gathered. Each sensor continuously generates data and has to transmit it through the network (via other sensor nodes) to the basestation. Considering the battery limitations of the sensors, our goal is to find an optimum location of the base-station and a corresponding data transmission scheme for routing the data from the sensors, such that the network is operating for the longest possible time. We focus mainly on tree networks for 2-level trees, with at most 2 hops from sensor to the base-station. For such networks we give efficient algorithms for forwarding data from sensors to the base-station and for locating the base-station optimally for maximizing network lifetime. Further, we show that determining a transmission protocol for trees with 3 or more levels is NP-hard. We demonstrate the effectiveness of our methods with experimental results on simulated data, comparing our 2-level tree algorithm with methods based on linear programming.

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Over the last decade, chemotherapy treatments have dramatically shifted to outpatient services such that nearly 90% of all infusions are now administered outpatient. This shift has challenged oncology clinics to make chemotherapy treatment as widely available as possible while attempting to treat all patients within a fixed period of time. Historical data from a Veterans Affairs chemotherapy clinic in the United States and staff input informed a discrete event simulation model of the clinic. The case study examines the impact of altering the current schedule, where all patients arrive at 8:00 AM, to a schedule that assigns patients to two or three different appointment times based on the expected length of their chemotherapy infusion. The results identify multiple scheduling policies that could be easily implemented with the best solutions reducing both average patient waiting time and average nurse overtime requirements. ARTICLE HISTORY

A case study for improving the quality of a wave-soldering process that produced printed circuit boards (PCB's) is presented. A mixed-level fractional factorial design was implemented in a high-volume production system during normal operational hours. The observed ordered-categorical data from the bottom-side soldered leads were weighted to formulate the average, spatial uniformity, and dispersion process performance measurements. For lead classes like the integrated circuit and printed grid array, a polynomial model was established using the least squares method with weights provided by a dispersion function. The main-effect and interaction model terms were selected by forward and all-subsets regressions. Production quotas, topside defects, presoldering board temperature, and variance models were used to set the constraints for simultaneously optimizing predictions from the average and the uniformity models of all leads. A nonlinear optimization routine was used to determine the best and most robust settings for the continuous and discrete process variables. Results from a confirmatory experiment showed an improvement of mean soldering quality by 33% and of uniformity by 39%.

A case study of improving the quality of a complex wave soldering process which produces printed circuit boards (PCB) is presented in this article. Experimental design with a mixed-level fractional factorial structure was implemented in a high-volume production system during regular hours of operation. The observed ordered-categorical data from the bottom-side joints of PCB's are weighted to formulate performance measurements such as the average and the uniformity of solder-qualities. These summary statistics take into account the spatial correlation that occurs in joint-quality within the same type of components such as integrated circuit and PGA's. Several polynomial regreSSIOn models with possible higher-order interactions between controllable variables are fitted to these statistics. Dispersions effects are computed and modeled from repetitions of the average and the uniformity measurements. Topside soldering quality, pre-soldering board temperature and dispersion effects are used to set the constraints for optimizing the average and the uniformity soldering-quality simultaneously. A nonlinear programming method of constrained optimization is employed to determine the best and most robust settings for the continuous and discrete process variables. The analysis of results from a small confirmatory experiment shows a completely uniform soldering-quality and an improvement of 32.85% in mean scores in comparing samples of 20 PCB's taken before and after optimization.

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