Sharon A. Johnson

Numerical solution of continuous-state dynamic programs using linear and spline interpolation

This paper demonstrates that the computational effort required to develop numerical solutions to continuous-state dynamic programs can be reduced significantly when cubic piecewise polynomial functions, rather than tensor product linear interpolants, are used to approximate the value function. Tensor product cubic splines, represented in either piecewise polynomial or B-spline form, and multivariate Hermite polynomials are considered. Computational savings are possible because of the improved accuracy of higher-order functions and because the smoothness of higher-order functions allows efficient quasi-Newton methods to be used to compute optimal decisions. The use of the more efficient piecewise polynomial form of the spline was slightly superior to the use of Hermite polynomials for the test problem and easier to program. In comparison to linear interpolation, use of splines in piecewise polynomial form reduced the CPU time to obtain results of equivalent accuracy by a factor of 250–330 for a stochastic 4-dimensional water supply reservoir problem with a smooth objective function, and factors ranging from 25–400 for a sequence of 2-, 3-, 4-, and 5-dimensional problems. As a result, a problem that required two hours to solve with linear interpolation was solved in a less than a minute with spline interpolation with no loss of accuracy.

The Value of Hydrologic Information in Stochastic Dynamic Programming Models of a Multireservoir System

Reservoir operating policies can be derived using stochastic dynamic programming (SDP) with different hydrologic state variables. This paper considers several choices for such hydrologic state variables for SDP models of the Shasta-Trinity system in northern California, for three different benefit functions. We compare how well SDP models predict their policies will perform, as well as how well these policies performed when simulated. For a benefit function stressing energy maximization, all policies did nearly as well, and the choice of the hydrologic state variable mattered very little. For a benefit function with larger water and firm power targets and severe penalties on corresponding shortages, predicted performance significantly overestimated simulated performance, and policies that employed more complete hydrologic information performed significantly better.

Comparison of two approaches for implementing multireservoir operating policies derived using stochastic dynamic programming

This paper compares two approaches for implementing reservoir operating policies derived using stochastic dynamic programming (SDP) models. In particular, operating policies for the Shasta-Trinity system in Northern California are generated using SDP algorithms that employ either multilinear or multidimensional piecewise cubic functions to approximate the cost-to-go function. Release decisions in the simulations are then determined by either (1) interpolating in the policy tables or (2) reoptimizing the policy within the simulation, using the cost-to-go function generated by the SDP. The impact on simulated system performance of several discretization and interpolation schemes in the SDP is also evaluated. Reoptimizing the policy when a decision is made within the simulation resulted in better system performance, particularly when severe penalties were incurred for water and power shortages and coarse discretizations were employed in the SDP.

Heuristic operating policies for reservoir system simulation

Reservoir system simulation models are widely used to determine a systems firm water yield, average yield, or hydropower capacity. Most such models use heuristic guidelines to define the systems operating policy. Alternatively, optimization can be used within the simulation to identify a reasonable operating strategy. In this paper a theoretical justification is provided for several heuristic operating guidelines, including the widely used space rule. The guidelines are expressed as a mathematical objective function and combined with constraints on system operation to yield one-period optimization submodels that can be used to determine releases within a simulation. Use of these one-period optimization models improved the simulated operation of the Central Valley Project in California over the critical period of record and provided reasonable policies for other hydrologic scenarios.

Identifying ''best'' applicants in recruiting using data envelopment analysis

Abstract Selecting the most promising candidates to fill an open position can be a difficult task when there are many applicants. Each applicant achieves certain performance levels in various categories and the resulting information can be overwhelming. We demonstrate how data envelopment analysis (DEA) can be used as a fair screening and sorting tool to support the candidate selection and decision-making process. Each applicant is viewed as an entity with multiple achievements. Without any a priori preference or information on the multiple achievements, DEA identifies the non-dominated solutions, which, in our case, represent the “best” candidates. A DEA-aided recruiting process was developed that (1) determines the performance levels of the “best” candidates relative to other applicants; (2) evaluates the degree of excellence of “best” candidates’ performance; (3) forms consistent tradeoff information on multiple recruiting criteria among search committee members, and, then, (4) clusters the applicants.

Patient Portals: An Underused Resource for Improving Patient Engagement

The potential of patient portals to improve patient engagement and health outcomes has been discussed for more than a decade. The slow growth in patient portal adoption rates among patients and providers in the United States, despite external incentives, indicates that this is a complex issue. We examined evidence of patient portal use and effects with a focus on the pulmonary domain. We found a paucity of studies of patient portal use in pulmonary practice, and highlight gaps for future research. We also report on the experience of a pulmonary department using a patient portal to highlight the potential of these systems.

Exploring the effect of complex patients on care delivery tasks

PURPOSE The needs of complex patients with chronic conditions can be unpredictable and can strain resources. Exploring how tasks vary for different patients, particularly those with complex needs, can yield insights about designing better processes in healthcare. The purpose of this paper is to explore the tasks required to manage complex patients in an anticoagulation therapy context. DESIGN/METHODOLOGY/APPROACH The authors analyzed interviews with 55 staff in six anticoagulation clinics using the Systems Engineering Initiative for Patient Safety (SEIPS) work system framework. The authors qualitatively described complex patients and their effects on care delivery. FINDINGS Data analysis highlighted how identifying complex patients and their effect on tasks and organization, and the interactions between them was important. Managing complex patients required similar tasks as non-complex patients, but with greater frequency or more intensity and several additional tasks. After complex patients and associated patient interaction and care tasks were identified, a work system perspective was applied to explore how such tasks are integrated within clinics and the resulting implications for resource allocation. PRACTICAL IMPLICATIONS The authors present a complex patient management framework to guide workflow design in specialty clinics, to better support high quality, effective, efficient and safe healthcare. ORIGINALITY/VALUE The complex patient framework presented here, based on the SEIPS framework, suggests a more formal and integrated analysis be completed to provide better support for appropriate resource allocation and care coordination.

Clinical Workflow Analysis, Process Redesign, and Quality Improvement

Healthcare workflow is driven by individual, organizational, and societal factors. The resulting complexity and high variability make it challenging to examine workflow in healthcare delivery settings. In this chapter, the authors introduce relevant theories, frameworks, principles, tools, and techniques to guide both (a) workflow examination and (b) systematic organizational interventions for workflow redesign. These theories, frameworks, principles, tools, and techniques can be applied to diverse settings to improve the quality and safety of healthcare delivery.

Applying the axiomatic approach to business process redesign

Many approaches are available and widely used to represent and design business processes. The axiomatic approach developed by Suh has not received much attention because it is taught primarily as a product design tool. In this paper, we share our experience in applying this approach to a product obsolescence procedure in a semiconductor‐manufacturing environment. The approach provides structure for analyzing process outcomes and the means used to achieve them, a framework that is valuable in business process design. The rules that form the foundation of the axiomatic approach allow the user to evaluate the quality of a proposed design in satisfying the problem or perceived need. We found that the axiomatic approach was an excellent tool for generating process designs and evaluating them, although some effort is required initially to understand the approach. In addition, because it is essentially a process mapping technique, turning the proposed design into reality requires organizational understanding and...

Lean six sigma and environmental sustainability: a hospital perspective

ABSTRACT Organizations have demonstrated increasing interest in both leaning and greening activities. Lean manufacturing practices, with an emphasis on waste elimination, have an affinity with environmentally conscious green practices within organizations. Lean practices have diffused across the supply chain and to the service industry, while interest in the greening of supply chains has grown simultaneously. Linking the greening of supply chains and lean practices within a healthcare environment is relatively underexplored. This paper presents a process framework that defines specific dimensions and synergies between lean and green, to support further exploration of green lean supply chains in healthcare and particularly hospitals. In addition, the application of lean and green to hospitals explicitly brings in the third dimension of social sustainability because it focuses on the healthcare industry. The framework is applied to three cases to exemplify opportunities to green hospital processes. The process framework is also used to generate propositions for additional research in this emergent field.