Simulation emerges as an important technique in recent years for modeling complex operational dynamics in various healthcare institutions and hence providing deep insights for potential improvement. In particular, Accident and Emergency Department (A&ED) has been a place for such research as it accounts for a large proportion of the total hospital visits and admissions. To create a viable simulation for A&ED, accurate description and forecast of patient visits is the foremost step. This paper investigates several contributing factors to A&ED visits, and various time-series methods of modeling A&ED visits with different triage categories and mode of arrival. All the methods are compared in terms of goodness-of-fit and forecast accuracy. The purpose of this research is two-fold. First, this research is part of attempt to build a simulation model for A&ED of a local hospital. Second, the results may be useful for reexamine the resource allocation plan of the A&ED.
Abstract Web‐based appointment systems are emerging in the healthcare industry with mass data, but homogenized online information and poor evaluation criteria lead to blindness in selecting doctors. To select an appropriate doctor when making appointments online, a comprehensive decision support model is proposed. First, one class of multi‐criteria is built from reviews by text mining technologies. For quantitative analysis, interval‐valued neutrosophic numbers (IVNNs) are utilized to describe reviews, and related integration operators of IVNNs are employed. Second, another class of multi‐criteria is established by the website‐given labels. A disease similarity measure‐based transformation method is proposed to redefine the doctors’ specialization, making the evaluation values more discriminable. Finally, a personalized doctor ranking result is derived by integrating the two classes of multi‐criteria values with a preference parameter. A case study of Wedoctor.com is conducted to validate the proposed model, and the comparison result indicates that the model can effectively support users’ decision‐making.
This article focuses on employing the dynamic programming algorithm to solve the large-scale group decision-making problems, where the preference information takes the form of linguistic variables. Specifically, considering the linguistic variables cannot be directly computed, the interval type-2 fuzzy sets are employed to encode them. Then, new distance model and similarity model are respectively developed to measure the relationships between the interval type-2 fuzzy sets. After that, a dynamic programming algorithm-based clustering model is proposed to cluster the decision-makers from the overall perspective. Moreover, by taking both the cluster center and the group size into consideration, a new model is introduced to determine the weights of clusters and decision-makers, respectively. Finally, a centroid-based ranking method is developed to compare and rank the alternatives, and two illustrative experiments are provided to illustrate the effectiveness of the proposed method. Comparisons and discussions are also conducted to verify its superiority.
Collaborative decision making (CDM) with linguistic computational techniques has recently achieved significant advancements. Due to the widespread use of sophisticated linguistic constructions, such as generalized comparative linguistic expressions (GCLEs), additional information associated with subjective appraisals has been exploited with the aim of addressing accuracy improvements in multifarious CDM, given that partial information loss is almost inevitable while dealing with complex linguistic comprehension. This paper brings an innovative perspective into CDM from COmponent ANalysis with GCLEs (COANG) to formalize problems involved in making optimal choices, mainly in CDM problems with participants who are usually characterized by domain specificity. Consequently, the focus of this paper is on the domain-specific CDM (DSCDM) in which individual semantics should be built predominantly to model various implications of their decision appraisals with heterogeneity in the knowledgeable domain for the effort of computational reinforcements. The attitude orientation and strength are crucial decompositions to incorporate COANG into DSCDM to establish an elastic paradigm that puts forward individual perception comprehension ahead of exerting collective efforts. The DSCDM based on COANG model enables agents to turn complex challenges of sophisticated linguistic constructions into substantial opportunities by translating them into customized individual semantics (CIS), and CIS into useful insights for making better decisions and improving results. The potential advantages of the proposed COANG-based DSCDM framework are validated with a clinical psychological practice related to the severity assessment of symptoms of schizophrenia.
An analytical numerical model to optimize the shape of concave surface texture for the achievement of low friction in reciprocating sliding motion has been developed. The model uses: (i) average Reynolds equation to evaluate friction coefficient and (ii) genetic algorithm (GA) to optimize and obtain several preferable texture shapes. Analysis of distribution contour maps of hydrodynamic pressure gives the possible mechanisms involved. Moreover, experimental comparisons of tribological performances between the optimized and the circular textures were conducted to verify the simulation results. It is shown that surface textures of the elliptical and fusiform shapes can effectively enhance the load bearing capacity and reduce the friction coefficient compared with circular textures. The increase in hydrodynamic pressure for these optimized texture shapes is considered to be the major mechanism responsible for improving their tribological performance. Experimental results confirm that the elliptical-shaped textures have preferable tribological behaviors of low friction coefficient under the operating condition of light load.
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