Mohammad T. Khasawneh

A MARKOVIAN APPROACH TO DETERMINING OPTIMUM PROCESS TARGET LEVELS FOR A MULTI-STAGE SERIAL PRODUCTION SYSTEM

Consider a production system where products are produced continuously and screened for conformance with their specification limits. When product performance falls below a lower specification limit or above an upper limit, a decision is made to rework or scrap the product. The majority of the process target models in the literature deal with a single-stage production system. In the real-world industrial settings, however, products are often processed through multi-stage production systems. If the probabilities associated with its recurrent, transient and absorbing states are known, we can better understand the nature of a production system and thus better capture the optimum target for a process. This paper first discusses the roles of a Markovian approach and then develops the general form of a Markovian model for optimum process target levels within the framework of a multi-stage serial production system. Numerical examples and sensitivity analysis are performed. 2003 Elsevier B.V. All rights reserved.

Lead‐Time Reduction Utilizing Lean Tools Applied to Healthcare: The Inpatient Pharmacy at a Local Hospital

&NA; The healthcare arena, much like the manufacturing industry, benefits from many aspects of the Toyota lean principles. Lean thinking contributes to reducing or eliminating nonvalue‐added time, money, and energy in healthcare. In this paper, we apply selected principles of lean management aiming at reducing the wasted time associated with drug dispensing at an inpatient pharmacy at a local hospital. Thorough investigation of the drug dispensing process revealed unnecessary complexities that contribute to delays in delivering medications to patients. We utilize DMAIC (Define, Measure, Analyze, Improve, Control) and 5S (Sort, Set‐in‐order, Shine, Standardize, Sustain) principles to identify and reduce wastes that contribute to increasing the lead‐time in healthcare operations at the pharmacy understudy. The results obtained from the study revealed potential savings of >45% in the drug dispensing cycle time.

Entropy generation due to laminar forced convection in the entrance region of a concentric annulus

This study is focused on the entropy production due to laminar forced convection in the entrance region of a concentric cylindrical annulus. The present hydrodynamic and temperature fields are obtained numerically. Local entropy generation distributions are obtained based on the resulting velocity and temperature fields by solving the entropy generation equation. The effect of different flow parameters on thermal, viscous, and total entropy generation is studied for different thermal boundary conditions. Moreover, the effect of radius ratio on the entropy generation is investigated. Entropy generation was found to be inversely proportional to both Reynolds number and the dimensionless entrance temperature. The results also show that increasing Eckert number and/or the radius ratio will increase the entropy generation. Finally, it is found that thermal entropy generation is relatively dominant over viscous entropy generation.

Tables of a Truncated Standard Normal Distribution: A Doubly Truncated Case

ABSTRACT As an extension to the singly truncated case, this article further examines a doubly truncated normal distribution. In this article, we consider both symmetric and asymmetric cases with two truncation points on the left and the right, and develop the tables of a truncated mean, a truncated variance, and a cumulative probability.

The Added Usefulness of Process Measures Over Performance Measures in Interface Design

Various studies concerning the application of usability evaluation methods have shown the usefulness of these testing methods. The objective of this study was to evaluate the added advantage of collecting process measures (data used to learn how and why an error occurred) over performance measures (data used to determine what went wrong) in interface design. The most salient finding of this study is that when used in the design/redesign process, performance data improved effectiveness (i.e., reduced the number of errors), whereas process measures data improved efficiency by reducing the time needed to complete tasks. The results from this study, as with any other usability study, should be of particular interest to the software industry, which is constantly trying to reduce development time and resources while improving user-friendly interface design.

A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence

Abstract This research proposes a new hybrid approach for feature selection and Support Vector Machine (SVM) model selection based on a new variation of Cohort Intelligence (CI) algorithm. Feature selection can improve the accuracy of classification algorithms and reduce their computation complexity by removing the irrelevant and redundant features. SVM is a classification algorithm that has been used in many areas, such as bioinformatics and pattern recognition. However, the classification accuracy of SVM depends mainly on tuning its hyperparameters (i.e., SVM model selection). This paper presents a framework that is comprised of the following two major components. First, Self-Adaptive Cohort Intelligence (SACI) algorithm is proposed, which is a new variation of the emerging metaheuristic algorithm, Cohort Intelligence (CI). Second, SACI is integrated with SVM resulting in a new hybrid approach referred to as SVM–SACI for simultaneous feature selection and SVM model selection. SACI differs from CI by employing tournament-based mutation and self-adaptive scheme for sampling interval and mutation rate. Furthermore, SACI is both real-coded and binary-coded, which makes it directly applicable to both binary and continuous domains. The performance of SACI for feature selection and SVM model selection was examined using ten benchmark datasets from the literature and compared with those of CI and five well-known metaheuristics, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC). The comparative results demonstrate that SACI outperformed CI and comparable to or better than the other compared metaheuristics in terms of the SVM classification accuracy and dimensionality reduction. In addition, SACI requires less tuning efforts as the number of its control parameters is less than those of the other compared metaheuristics due to adopting the self-adaptive scheme in SACI. Finally, this research suggests employing more efficient methods for high-dimensional or large datasets due to the relatively high training time required by search strategies based on metaheuristics when applied to such datasets.

Reengineering the Cardiac Catheterization Lab Processes: A Lean Approach

This paper presents a cross-functional effort in a US community hospital for an overall process improvement in its Cardiac Catheterization Lab (CCL). One of the key system performance metrics identified was the patient turnaround time. The objective of this study was to identify the sources of delays in the system that lead to prolonged patient turnaround time using a structured lean approach. A set of qualitative recommendations were proposed and implemented. Quantification of some of these recommendations and certain additional ‘what-if’ scenarios were evaluated using Discrete Event Simulation (DES). The simulation results showed that significant reduction in patient turnaround time could be achieved if the proposed recommendations were implemented. This study demonstrated the benefits of adopting the lean philosophy in the continuous process improvement journey in the healthcare delivery arena.

New framework that uses patterns and relations to understand terrorist behaviors

A new framework is proposed to understand the activity patterns and relations.A new similarity function is proposed to estimate the relationships among events.The new model is proposed to estimate the importance of the features.Over-training in the model is prevented by using the LASSO regularization.The government can control the terrorist behaviors using the intelligent framework. Terrorism is a complex phenomenon with high uncertainties in user strategy. The uncertain nature of terrorism is a main challenge in the design of counter-terrorism policy. Government agencies (e.g., CIA, FBI, NSA, etc.) cannot always use social media and telecommunications to capture the intentions of terrorists because terrorists are very careful in the use of these environments to plan and prepare attacks. To address this issue, this research aims to propose a new framework by defining the useful patterns of suicide attacks to analyze the terrorist activity patterns and relations, to understand behaviors and their future moves, and finally to prevent potential terrorist attacks. In the framework, a new network model is formed, and the structure of the relations is analyzed to infer knowledge about terrorist attacks. More specifically, an Evolutionary Simulating Annealing Lasso Logistic Regression (ESALLOR) model is proposed to select key features for similarity function. Subsequently, a new weighted heterogeneous similarity function is proposed to estimate the relationships among attacks. Moreover, a graph-based outbreak detection is proposed to define hazardous places for the outbreak of violence. Experimental results demonstrate the effectiveness of our framework with high accuracy (more than 90% accuracy) for finding patterns when compared with that of actual terrorism events in 2014 and 2015. In conclusion, by using this intelligent framework, governments can understand automatically how terrorism will impact future events, and governments can control terrorists behaviors and tactics to reduce the risk of future events.

A simulation study of outpatient scheduling with multiple providers and a single device

AbstractEffective outpatient appointment scheduling aims at reducing patient waiting time and operational costs, and improving resource utilization, especially given the stochastic nature of patient arrivals. Unlike many western developed countries, China faces challenges due to imperfect appointment systems and ineffective resource allocation. Those challenges lead to long patient waiting times and significant pressure to provide accurate and reliable medical diagnosis that can handle the increasing demand for patient care. In this paper, an outpatient appointment scheduling model for primary care settings was developed. The model was structured for a multi-provider clinic with a single medical device that allows for revisit (i.e., follow-up) procedures. A total of six scheduling rules were designed and tested. Then, sensitivity analysis was performed with respect to two parameters: the percentage of patients with fixed appointments and walk-ins, and the scheduled physician assessment times. The experime...

Enhancing the scheduling of interventional radiology department using data analysis and modeling and simulation

This paper focuses on improving the overall operations of an interventional radiology department at a community hospital in USA. In the first phase of the improvement process, significant delays, involving waiting time of patients in preparation rooms, delays in their arrival to procedure rooms, and waiting time for the radiologists, were observed. A thorough analysis of the delays led to the second improvement phase, wherein the case scheduling methodology was evaluated. Through a Design of Experiment (DOE) approach, it was determined that patient¿s age and the different radiologists do not have significant impacts on the procedure time. A discrete-event simulation model was then created to replicate a baseline of the current system, upon which a new scheduling model, which incorporates procedure time-blocks, was identified and evaluated. The proposed scheduling model resulted in a 16% increase in room utilization and 11% increase in daily procedure throughput, without requiring any additional resources.