Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Powering a Biosensor Using Wearable Thermoelectric Technology

Wearable medical devices such as insulin pumps, glucose monitors, hearing aids, and electrocardiograms provide necessary medical aid and monitoring to millions of users worldwide. These battery powered devices require battery replacement and frequent charging that reduces the freedom and peace of mind of users. Additionally, the significant portion of the world without access to electricity is unable to use these medical devices as they have no means to power them constantly. Wearable thermoelectric power generation aims to charge these medical device batteries without a need for grid power. Our team has developing a wristband prototype that uses body heat, ambient air, and heat sinks to create a temperature difference across thermoelectric modules thus generating ultra-low voltage electrical power. A boost converter is implemented to boost this voltage to the level required by medical device batteries. Our goal was to use this generated power to charge medical device batteries off-the-grid, increasing medical device user freedom and allowing medical device access to those without electricity. We successfully constructed a wearable prototype that generates the voltage required by an electrocardiogram battery; however, further thermoelectric module and heat dissipation optimization is necessary to generate sufficient current to charge the battery

An integrated methodology for the design of Ro-Ro passenger ships

The present paper provides a brief introduction to the holistic approach to ship design, defines the generic ship design optimization problem and demonstrates its solution by use of advanced optimization techniques

Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data

Reliable human pose estimation (HPE) is essential to many clinical applications, such as surgical workflow analysis, radiation safety monitoring and human-robot cooperation. Proposed methods for the operating room (OR) rely either on foreground estimation using a multi-camera system, which is a challenge in real ORs due to color similarities and frequent illumination changes, or on wearable sensors or markers, which are invasive and therefore difficult to introduce in the room. Instead, we propose a novel approach based on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in real ORs. We extend the PS framework in two ways. First, we build robust and discriminative part detectors using both color and depth images. We also present a novel descriptor for depth images, called histogram of depth differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise constraints and a new method that makes exact inference tractable. Our approach is evaluated for pose estimation and clinician detection on a challenging RGB-D dataset recorded in a busy operating room during live surgeries. We conduct series of experiments to study the different part detectors in conjunction with the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D PS with RGB-D part detectors significantly improves the results in a visually challenging operating environment.Comment: The supplementary video is available at https://youtu.be/iabbGSqRSg

A database management capability for Ada

The data requirements of mission critical defense systems have been increasing dramatically. Command and control, intelligence, logistics, and even weapons systems are being required to integrate, process, and share ever increasing volumes of information. To meet this need, systems are now being specified that incorporate data base management subsystems for handling storage and retrieval of information. It is expected that a large number of the next generation of mission critical systems will contain embedded data base management systems. Since the use of Ada has been mandated for most of these systems, it is important to address the issues of providing data base management capabilities that can be closely coupled with Ada. A comprehensive distributed data base management project has been investigated. The key deliverables of this project are three closely related prototype systems implemented in Ada. These three systems are discussed

Optimizing a multiple objective surgical case scheduling problem.

The scheduling of the operating theater on a daily base is a complicated task and is mainly based on the experience of the human planner. This, however, does not mean that this task can be seen as unimportant since the schedule of individual surgeries influences a medical department as a whole. Based on practical suggestions of the planner and on real-life constraints, we will formulate a multiple objective optimization model in order to facilitate this decision process. We will show that this optimization problem is NP-hard and hence hard to solve. Both exact and heuristic algorithms, based on integer programming and on implicit enumeration (branch-and-bound), will be introduced. These solution approaches will be thoroughly tested on a realistic test set using data of the surgical day-care center at the university hospital Gasthuisberg in Leuven (Belgium). Finally, results will be analyzed and conclusions will be formulated.Algorithms; Belgium; Branch-and-bound; Constraint; Data; Decision; Experience; Healthcare; Heuristic; Integer; Integer programming; Model; Optimization; Order; Processes; Real life; Scheduling; University;

Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified

Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant

The energy efficiency of industrial plants is an important issue in any type of business but particularly in the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring better product quality, and generally increasing profits. This article describes a decision system developed for optimizing the energy efficiency of a petrochemical plant. The system has been developed after a data mining process of the parameters registered in the past. The designed system carries out an optimization process of the energy efficiency of the plant based on a combined algorithm that uses the following for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in the past and, on the other hand, a module of two neural networks to obtain new interpolated operation points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to select the parameters most important in the plant and to study the behavior of the energy efficiency index. This study also helped ensure an optimal training of the neural networks. The robustness of the system as well as its satisfactory results in the testing process (an average rise in the energy efficiency of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to implement and to integrate the decision system as a pilot software in an SCADA