Selection of Temperature Measuring Sensors Using the Analytic Hierarchy Process

This study presents an analytic hierarchy process (AHP) method to objectively select the best temperature sensor from among different alternative sensors in a certain industrial application. The underlying decision method based on AHP methodology, ranks temperature sensors with different features with a score resulting from the synthesis of relative preferences of each alternative with respect to the others at different levels considering independent evaluation criteria and sub-criteria. At each level, relative preferences of each candidate alternative with respect to the upper immediate level are calculated from pairwise comparisons among the candidate alternative sensors with respect to a selected application. Pair-wise comparison matrices are compiled based on views of experts in this field. Seven alternative sensors were considered: the thermocouple, the thermister, the resistance temperature detector (RTD), the bimetallic strip thermometer, the mercury-in-glass thermometer, the optical disappearing filament pyrometer, and the liquid crystal display semi conductor thermometer (LCD). Three industrial applications were also considered: Automotives, Chemical Processes, and Heating, Ventilating and Air Conditioning. A case study is conducted which involves selecting the best sensor for an automotive catalytic converter. The thermocouple is found to be the most preferred sensor for this application with the largest score of 0.37849, the second ranked sensor is the RTD with a score of 0.34589, and the least preferred sensor is the thermister with a score of 0.27560. To test the robustness of the proposed work, a sensitivity analysis was conducted in which variations in the relative preferences of the alternative sensors against sub-criteria and criteria were employed

A Software Application for the Selection of Temperature Measuring Sensors Using the Analytic Hierarchy Process (AHP)

This study presents a software application that applies the Analytic Hierarchy Process (AHP) to objectively select the best temperature sensors. Three industrial applications and seven sensor alternatives are considered. The developed application performs the selection process in a computerised, easy–to–use graphical user interface. The underlying decision method ranks temperature sensors with scores resulting from the synthesis of relative preferences of each alternative at different levels considering independent evaluation criteria. Pair–wise relative comparison matrices collected from experts are embedded and are retrieved according to user specifications. A case study is conducted which involves selecting the best sensor for an automotive catalytic converter. The thermocouple is found to be the most preferred sensor with the largest score of 0.37849, the second ranked sensor is the RTD with a score of 0.34589, and the least preferred sensor is the thermister with a score of 0.27560. Sensitivity analysis shows that the selection of the best sensor is dependent on the relative weights of the criteria as well as the chosen application. AHP is shown to provide a quantitative evaluation method which is simpler, easier and more organised than subjective opinions

A Simulation Model for Performance Improvement of Triage-Based Emergency Care

Abstract— The paper presents a discrete event simulation model to evaluate the performance in an emergency department. Following evaluation of current state of emergency care, the study proposes establishing a general medicine clinic within or next to the emergency department such that patients with low emergency priorities can be directed to the clinic where they can see a non emergency physician. Results obtained from testing the proposed model illustrate performance advantage with respect to urgent classified cases ranging from 19.27% to 24.36% reduction in waiting time compared to it in the traditional emergency department system

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Using expanded real options analysis to evaluate capacity expansion decisions under uncertainty in the construction material industry

Capacity expansion generally requires large capital expenditure on illiquid assets. Therefore, decisions to enlarge capacity must support the organisation’s strategic objectives and provide valuable input for the budgeting process. This paper applies an expanded form of Real Options Analysis (ROA) to generate and evaluate capacity expansion strategies under uncertainty in the construction material industry. ROA is applied to different expansion strategies associated with different demand scenarios. Evaluating a wider variety of strategies can reduce risk and sponsor decisions that maximise the firm’s value. The case study shows that the execution of a lead expansion strategy with 10-year intervals under a 50 per cent demand satisfaction scenario produces superior results

IMPROVING PERFORMANCE IN AN ALUMINUM EXTRUSION PLANT USING DISCRETE EVENT SIMULATION: A CASE STUDY

Simulation has been used in many industrial applications for performance improvement. It excels over other system analysis methods in its high flexibility and ability to model system details with high accuracy. In this study, Discrete Event Simulation (DES) is used to improve the performance of an aluminum extrusion plant. A case study is presented in a local factory in which problems are identified, and their effects on efficiency are monitored. The main problem noticed was high production rates with respect to demand rates which resulted in large amounts of work-in-process (WIP) inventory. It was found that the current base system is unstable and suggestions were made to lower production rates in order to stabilize it. Average WIP was reduced by 324% once the system was stabilized with only 1.77% difference in weekly throughput which improved the system considerably. Next, alternatives were suggested to improve throughput and reduce WIP while maintaining stability. The alternative with optimized batch sizes had the best improvement in throughput of 3.54%. The combined model with optimized batch sizes and an added pool for chemical treatment had the most WIP versus other alternatives