A Problem-Specific and Effective Metaheuristic for Flexibility Design

Matching uncertain demand with capacities is notoriously hard. Operations managers can use mix-flexible resources to shift excess demands to unused capacities. To find the optimal configuration of a mix-flexible production network, a flexibility design problem (FDP) is solved. Existing literature on FDPs provides qualitative structural insights, but work on solution methods is rare. We contribute the first metaheuristic which integrates these structural insights and is specifically tailored to solve FDPs. Our genetic algorithm is compared to commercial solvers on instances of up to 15 demand types, resources, and 500 demand scenarios. Experimental evidence shows that in the realistic case of flexible optimal configurations, it dominates the comparison methods regarding runtime and solution quality.Flexibility, Metaheuristic, Network Design

Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, the landscape of the semiconductor field in the last 15 years has constituted power as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and/or power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, with research works applying approximations across the entire traditional computing stack, i.e., at software, hardware, and architectural levels. Over the last decade, there is a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories). The current article is Part I of our comprehensive survey on Approximate Computing, and it reviews its motivation, terminology and principles, as well it classifies and presents the technical details of the state-of-the-art software and hardware approximation techniques.Comment: Under Review at ACM Computing Survey

Modeling Second Order Impacts of Healthcare Innovation

Any single health service organization today is likely engaged in dozens of concurrent, often times unrelated change initiatives. Each of these change initiatives is likely supported by evidence that demonstrates the innovation’s intended, first order impact. However, very little attention has been paid to the unintended, second order impacts of innovation. In this dissertation we introduce a model to provide a framework for inquiring about this very type of non-immediate impact. Next, using three innovations currently being implemented in the healthcare industry—training primary care residents to perform in-office colonoscopies, Studer Group’s ‘Evidence Based Leadership,’ and implementation of electronic health records in a hospital-integrated pediatric network—we model the innovations’ second order impacts within the context of our second order impact conceptual model. Cost effectiveness analysis, multiple analysis of variance (MANOVA), and two-level fixed effects modeling are used to across the three interventions. Results from the primary care residency intervention support further investment in colorectal cancer screening training for primary care residents. Results from the Studer Group’s ‘Evidence Based Leadership’ intervention demonstrate mixed results across change interventions and across categories of tenure, suggesting receptivity towards change and organization tenure is highly dependent upon the nuances of a specific change intervention. Finally, results from the implementation of the electronic health record demonstrate improved charge capture. We conclude that this further probing of popular innovations in the industry is warranted for multiple reasons. For one, it is entirely possible that social scientists and economists are prematurely ‘moving on’ to other innovations as soon they have published results from an initial round of inquiry. However, as we will demonstrate in our model, it is conceivable that after the “lights have dimmed” on an innovation’s initial glow, the artifacts of the innovation could very well continue to disrupt structures and processes long after its implementation. If these latent disruptions adversely affect the organization, one could argue that any initial positive impacts were likely overstated. Conversely, if these latent disruptions go on to produce additional benefit to the organization one could argue that any initial positive results were actually understated

From Cost Sharing Mechanisms to Online Selection Problems

We consider a general class of online optimization problems, called online selection problems, where customers arrive sequentially, and one has to decide upon arrival whether to accept or reject each customer. If a customer is rejected, then a rejection cost is incurred. The accepted customers are served with minimum possible cost, either online or after all customers have arrived. The goal is to minimize the total production costs for the accepted customers plus the rejection costs for the rejected customers. These selection problems are related to online variants of offline prize collecting combinatorial optimization problems that have been widely studied in the computer science literature. In this paper, we provide a general framework to develop online algorithms for this class of selection problems. In essence, the algorithmic framework leverages any cost sharing mechanism with certain properties into a poly-logarithmic competitive online algorithm for the respective problem; the competitive ratios are shown to be near-optimal. We believe that the general and transparent connection we establish between cost sharing mechanisms and online algorithms could lead to additional online algorithms for problems beyond the ones studied in this paper.National Science Foundation (U.S.) (CAREER Award CMMI-0846554)United States. Air Force Office of Scientific Research (FA9550-11-1-0150)United States. Air Force Office of Scientific Research (FA9550-08-1-0369)Solomon Buchsbaum AT&T Research Fun