15 research outputs found
āđāļāļāļāđāļēāļĨāļāļāļāļēāļĢāļāļĒāļēāļāļĢāļāđāđāļāļ§āđāļāđāļĄāļāļāļāļāļąāļāļĢāļēāđāļĨāļāđāļāļĨāļĩāđāļĒāļāļŠāļāļļāļĨāđāļāļīāļāļĒāļđāđāļĢāđāļāļĩāļĒāļāļāļąāļāļāļāļĨāļĨāļēāļĢāđāļŠāļŦāļĢāļąāļāđāļāļāļĢāļēāļĒāļ§āļąāļāļāđāļ§āļĒāļāđāļāļĄāļđāļĨāļāļąāđāļ§āļāļ§āļēāļĄāļĢāļđāđāļŠāļķāļāļāļāļāļāđāļēāļ§āļŠāļēāļĢāļāļāļāđāļĨāļāđA Trend Forecasting Model of Daily Euro/USD Exchange Rate Using Sentiment Analysis on Online News
āļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāļĄāļĩāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļ·āđāļāļāļĒāļēāļāļĢāļāđāđāļāļ§āđāļāđāļĄāļāļąāļāļĢāļēāđāļĨāļāđāļāļĨāļĩāđāļĒāļāļŠāļāļļāļĨāđāļāļīāļāļĒāļđāđāļĢāđāļāļĩāļĒāļāļāļąāļāļāļāļĨāļĨāļēāļĢāđāļŠāļŦāļĢāļąāļāđāļāļāļĨāļēāļāļāļ·āđāļāļāļēāļĒāđāļĨāļāđāļāļĨāļĩāđāļĒāļāļŠāļāļļāļĨāđāļāļīāļāļĢāļ°āļŦāļ§āđāļēāļāļāļĢāļ°āđāļāļĻ āđāļĨāļ°āļāļēāļĢāļāļāļŠāļāļāļŠāļĄāļĄāļāļīāļāļēāļāđāļāļīāļāđāļāđāļāđāļŦāļāļļāđāļāđāļāļāļĨāļĢāļ°āļŦāļ§āđāļēāļāļāđāļ§āļĒāļ§āļīāļāļĩāđāļāļĢāļāđāļāļāļĢāđ āđāļāļĒāļāđāļāļĄāļđāļĨāļāļĩāđāļāļģāļĄāļēāđāļāđāđāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļĢāļ°āļāļāļāļāđāļ§āļĒāļāđāļāļĄāļđāļĨāđāļāļāļĄāļĩāđāļāļĢāļāļŠāļĢāđāļēāļāđāļāđāđāļāđ āđāļŠāđāļāļāđāļēāđāļāļĨāļĩāđāļĒāđāļāļĨāļ·āđāļāļāļāļĩāđ āļĢāļēāļāļēāļāļāļāļāļģāđāļĨāļ°āļĢāļēāļāļēāļāđāļģāļĄāļąāļāļāļīāļ āđāļĨāļ°āļāđāļāļĄāļđāļĨāđāļāļāđāļĄāđāļĄāļĩāđāļāļĢāļāļŠāļĢāđāļēāļāđāļāđāđāļāđ āļŠāļąāļāļŠāđāļ§āļāļāļāļāļāļģāļāļ§āļāļāđāļēāļ§āļāļąāđāļ§āļāļ§āļēāļĄāļĢāļđāđāļŠāļķāļāđāļāļīāļāļāļ§āļ āđāļāļīāļāļĨāļ āđāļĨāļ°āđāļāđāļāļāļĨāļēāļāļāļāļāļāđāļēāļ§āļŠāļēāļĢāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļąāļāļĢāļēāļāļēāļĢāđāļĨāļāđāļāļĨāļĩāđāļĒāļāļŠāļāļļāļĨāđāļāļīāļāļĒāļđāđāļĢāđāļāļĩāļĒāļāļāļąāļāļāļāļĨāļĨāļēāļĢāđāļŠāļŦāļĢāļąāļ āļāļķāđāļāđāļāđāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāđāļāļĢāļ°āļŦāļ§āđāļēāļāļ§āļąāļāļāļĩāđ 2 āđāļāļ·āļāļāļāļļāļĨāļēāļāļĄ āļ.āļĻ. 2563 āļāļķāļāļ§āļąāļāļāļĩāđ 10 āđāļāļ·āļāļāļāļļāļĄāļ āļēāļāļąāļāļāđ āļ.āļĻ. 2564 āļāļģāļāļ§āļ 92 āļĢāļēāļĒāļāļēāļĢ āđāļāļĒāļāđāļāļĄāļđāļĨāļāđāļēāļ§āļŠāļēāļĢāļāļĩāđāđāļāđāļāļ°āļāļđāļāļāļģāļĄāļēāļāļĢāļ°āļĄāļ§āļĨāļāļĨāđāļāļĒāļ§āļīāļāļĩāđāļāļāļāļīāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļąāđāļ§āļāļ§āļēāļĄāļĢāļđāđāļŠāļķāļāđāļĨāļ°āļ§āļīāļāļĩāļāļēāļĢāļāļĒāļēāļāļĢāļāđāđāļāļ§āđāļāđāļĄāļāļĩāđāđāļāđāđāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļĩāđāđāļāđāđāļāđ 1) āđāļāļĢāļāļāđāļēāļĒāļāļĢāļ°āļŠāļēāļāđāļāļĩāļĒāļĄ 2) āļāļąāļāļāļāļĢāđāļāđāļ§āļāđāļāļāļĢāđāđāļĄāļāļāļĩāļ āđāļĨāļ° 3) āļāļēāļĢāļāļģāđāļāļāļāļĢāļ°āđāļ āļāđāļāļāļāļēāļĢāļŠāļļāđāļĄāļāđāļēāđāļĄāđ āļāļāļāļāļēāļāļāļĩāđāđāļāđāļāļģāļāļēāļĢāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāļēāļĢāļ§āļąāļāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļāļāļāļąāļ§āđāļāļāļāļĒāļēāļāļĢāļāđ āļāļ·āļāļ§āļīāļāļĩāļāļēāļĢāđāļāđāļāļāđāļāļĄāļđāļĨāļŠāļģāļŦāļĢāļąāļāļŠāļāļāđāļĨāļ°āļ§āļąāļāļāļĨāđāļĨāļ°āļ§āļīāļāļĩāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāđāļāļāđāļāļ§āđ āļāļĨāļāļēāļĢāļĻāļķāļāļĐāļēāļāđāļēāļ§āļāļĩāđāļĄāļĩāļāļąāđāļ§āļāļ§āļēāļĄāļĢāļđāđāļŠāļķāļāđāļāļīāļāļāļ§āļāđāļĨāļ°āļāđāļēāļ§āļāļĩāđāļĄāļĩāļāļąāđāļ§āļāļ§āļēāļĄāļĢāļđāđāļŠāļķāļāđāļāđāļāļāļĨāļēāļāļĄāļĩāļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđāļāļąāļāđāļāļ§āđāļāđāļĄāļāļąāļāļĢāļēāđāļĨāļāđāļāļĨāļĩāđāļĒāļāļŠāļāļļāļĨāđāļāļīāļāļĒāļđāđāļĢāđāļāļĩāļĒāļāļāļąāļāļāļāļĨāļĨāļēāļĢāđāļŠāļŦāļĢāļąāļ āđāļĨāļ°āļāļąāļ§āđāļāļāļāļĒāļēāļāļĢāļāđāđāļāļ§āđāļāđāļĄāļāļĩāđāļŠāļĢāđāļēāļāļāļķāđāļāļāđāļ§āļĒāļ§āļīāļāļĩāļāļēāļĢāļāļģāđāļāļāļāļĢāļ°āđāļ āļāđāļāļāļāļēāļĢāļŠāļļāđāļĄāļāđāļēāđāļĄāđāļāļēāļāļāļēāļĢāđāļāđāļāļāđāļāļĄāļđāļĨāļ§āļīāļāļĩāļ§āļīāļāļĩāļāļēāļĢāđāļāđāļāļāđāļāļĄāļđāļĨāļŠāļģāļŦāļĢāļąāļāļŠāļāļāđāļĨāļ°āļ§āļąāļāļāļĨāđāļŦāđāļāļĨāļĨāļąāļāļāđāļāļĩāđāļāļĩāļāļĩāđāļŠāļļāļ āđāļāļĒāļĄāļĩāļāđāļēāļāļ§āļēāļĄāļāļđāļāļāđāļāļ āļāļīāļāđāļāđāļāļĢāđāļāļĒāļĨāļ° 71.43 āļāļąāđāļāļāļĩāđāļāļąāļ§āđāļāļāļāļēāļĢāļāļģāđāļāļāļāļĢāļ°āđāļ āļāđāļāļāļāļēāļĢāļŠāļļāđāļĄāļāđāļēāđāļĄāđ āļŠāļēāļĄāļēāļĢāļāļāļģāļĄāļēāđāļāđāļāļĒāļēāļāļĢāļāđāļāļąāļāļĢāļēāđāļĨāļāđāļāļĨāļĩāđāļĒāļāļŠāļāļļāļĨāđāļāļīāļāļĒāļđāđāļĢāđāļāļĩāļĒāļāļāļąāļāļāļāļĨāļĨāļēāļĢāđāļŠāļŦāļĢāļąāļāđāļāļ·āđāļāđāļāđāļāđāļāļ§āļāļēāļāļāļēāļĢāļĨāļāļāļļāļāļāļēāļĢāļāļ·āđāļāļāļēāļĒāđāļāđāļāļĒāđāļēāļāļāļĩThe objectives of this research were to forecast the movement of the dollar-euro exchange rates in the Foreign Exchange (FOREX) Market and examine the relationship of the polarity of the news articles and the EUR to USD exchange rate using Granger Causality test. The variables used in the study consisted of structured variables such as a moving averages indicator, gold prices and crude oil prices, while the unstructured variables were the proportions of the positive, negative and neutral polarities of online news related to the EUR/USD exchange rates. Daily data was collected from October 2, 2020 to February 10, 2021, a total of 92 items in the study. The news articles were processed using the sentiment analysis and the following methods for predictive modeling: 1) Artificial Neural Network 2) Support Vector Machine and 3) Random Forest Classification. Two techniques for model evaluation are 1) Train-Test Split and 2) Cross Validation. The results showed that the positive, negative and neutral sentiments were related to the EUR to USD exchange rate trends. Moreover, The Random Forest based on Train-Test Split yielded the best results with accuracy accounting for 71.43%. Concisely, the Random Forest model can be used as an effective trading guide for forecasting the movement of the USD-EUR exchange rates
Risk factors and outcomes in asymmetrical femoral component size for posterior referencing bilateral total knee arthroplasty: a matched pair analysis
Abstract Background Theoretically, potential errors in femoral component (FC) sizing can affect postoperative functional outcomes after total knee arthroplasty (TKA), including range of motion (ROM), anterior knee pain, and flexion stability. Incidences of asymmetrical femoral components (AFC) in bilateral TKA have been reported; however; there is a lack of data on exactly why AFC size selection may differ in patients who have had posterior referencing system bilateral TKA. Therefore, this study was conducted to determine risk factors of AFC size selection in patients specifically undergoing posterior referencing bilateral TKA and to compare clinical outcomes between those with AFC or symmetrical femoral component (SFC) sizes. Methods We conducted a retrospective matched-pair study comparing thirty-four patients who had undergone simultaneous and staged bilateral TKA using AFC size (Group I) and thirty-five patients with SFC size (Group II). Patients were matched according to gender, body mass index, prosthesis type, and operative technique. Preoperative radiographic morphology of both distal femurs including anteroposterior/mediolateral diameters, anterior-posterior femoral offset, and postoperative radiographic data of FC comprising flexion and valgus angle were recorded. The postoperative functional outcomes including ROM, anterior knee pain, knee society score, and functional score at 6 weeks, 3, 6, 12 and 24 months were compared. Results There were no differences in morphology between left and right distal femurs from preoperative radiographic data in both groups. The postoperative radiograph showed a significantly greater FC flexion angle difference in Group I vs. Group II (2.18°âÂąâ1.29° and 1.36°âÂąâ1.08° Pâ=â0.007), while the other parameters were the same. The postoperative clinical outcomes displayed no distinction between groups. Conclusion The factor primarily associated with AFC size selection in bilateral TKAs is the difference in FC flexion angle but not the morphological diversity between sides. The postoperative functional outcomes were not inferior in AFC patients in comparison with SFC patients
āļāļēāļĢāļāļĢāļ°āļĒāļļāļāļāđāđāļāđāļāļąāļ§āđāļāļāļāļēāļĢāļāļąāļāļŠāļīāļāđāļāļŦāļĨāļēāļĒāļŦāļĨāļąāļāđāļāļāļāđāđāļāļāļāļŠāļĄāđāļāļ·āđāļāđāļĨāļ·āļāļāļāļĩāđāļāļąāđāļāļāļāļāđāļĢāļāđāļĢāļĄāđāļāļāļāļēāļĒāļŦāļēāļāđāļāļāļĢāļ°āđāļāļĻāđāļāļĒApplying a Hybrid Multiple Criteria Decision Making Model for Selecting the Location of Beach Hotels in Thailand
āļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāđāļāđāļāļģāđāļŠāļāļāļāļēāļĢāđāļĨāļ·āļāļāļāļģāđāļĨāļāļĩāđāļāļąāđāļāđāļĢāļāđāļĢāļĄāđāļāļāļāļēāļĒāļŦāļēāļāđāļāļāļĢāļ°āđāļāļĻāđāļāļĒ āđāļāļĒāļāļēāļĢāļāļĢāļ°āļĒāļļāļāļāđāđāļāđāļ§āļīāļāļĩāļāļĢāļ°āļāļ§āļāļāļēāļĢāļĨāļģāļāļąāļāļāļąāđāļāđāļāļīāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļāļāļąāļāļāļĩ (Fuzzy Analytic Hierarchy Process : FAHP) āļĢāđāļ§āļĄāļāļąāļāļāļąāļāļ§āļīāļāļĩ PROMETHEE II āđāļāļĒāļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāļĄāļĩāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļ·āđāļāļĻāļķāļāļĐāļēāļŦāļēāđāļāļāļāđāļāļĩāđāđāļāđāđāļāļāļēāļĢāļāļąāļāļŠāļīāļāđāļāđāļĨāļ·āļāļāļāļģāđāļĨāļāļĩāđāļāļąāđāļāđāļĢāļāđāļĢāļĄ āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļēāļāđāļģāļŦāļāļąāļāđāļāļāļāđāļāļēāļĢāļāļąāļāļŠāļīāļāđāļāđāļāļāļēāļĢāļŦāļēāļāļģāđāļĨāļāļĩāđāļāļąāđāļāļāļāļāđāļĢāļāđāļĢāļĄāđāļāļĒāļāļēāļĻāļąāļĒāļ§āļīāļāļĩāļāļĢāļ°āļāļ§āļāļāļēāļĢāļĨāļģāļāļąāļāļāļąāđāļāđāļāļīāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļāļāļąāļāļāļĩ āđāļĨāļ°āļāļąāļāļĨāļģāļāļąāļāļāļĢāđāļāļĄāļāļąāđāļāļĢāļ°āļāļļāļāļēāļāđāļĨāļ·āļāļāļāļēāļāļāđāļēāļāđāļģāļŦāļāļąāļāļāļēāļĢāļāļąāļāļŠāļīāļāđāļāđāļāļĒāļāļēāļĻāļąāļĒāļ§āļīāļāļĩ PROMETHEE II āđāļāļ·āđāļāļāļģāļĄāļēāļŠāļāļąāļāļŠāļāļļāļāļāļēāļĢāļ§āļēāļāđāļāļāļāļēāļĢāļĨāļāļāļļāļāđāļĨāļ°āļāļĨāļĒāļļāļāļāđāļāļāļāļāļĢāļīāļĐāļąāļāļāļĢāļāļĩāļĻāļķāļāļĐāļē āđāļāļĒāđāļāļāļēāļĢāļŠāļąāļĄāļ āļēāļĐāļāđāļāļđāđāļāļĢāļīāļŦāļēāļĢāļāļķāđāļāđāļāđāļāļāļđāđāđāļāļĩāđāļĒāļ§āļāļēāļāđāļāļāđāļēāļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļąāļāļāļēāļĢāđāļĢāļāđāļĢāļĄāđāļāđāļāļīāļāļēāļĢāļāļēāđāļĨāļ°āļāļģāļŦāļāļāļāļēāļāđāļĨāļ·āļāļāđāļ§āđāļāļąāđāļāļŦāļĄāļ 6 āļāļēāļāđāļĨāļ·āļāļ āđāļāđāđāļāđ āļāļĢāļ°āļāļĩāđ āļāļĢāļ°āļāļ§āļāļāļĩāļĢāļĩāļāļąāļāļāđ āļĢāļ°āļĒāļāļ āļāļĨāļāļļāļĢāļĩ āļ āļđāđāļāđāļ āđāļĨāļ°āļŠāļļāļĢāļēāļĐāļāļĢāđāļāļēāļāļĩ āđāļāļŠāđāļ§āļāļāļāļāđāļāļāļāđāļāļēāļĢāļāļąāļāļŠāļīāļāđāļāļĄāļĩāđāļāļāļāđāļŦāļĨāļąāļāļāļĩāđāļŠāļģāļāļąāļāļāļąāđāļāļŦāļĄāļ 7 āđāļāļāļāđ āđāļāđāđāļāđ āļāļ§āļēāļĄāļŠāļĄāļāļđāļĢāļāđāļāļāļāļŠāļ āļēāļāđāļ§āļāļĨāđāļāļĄāļāļēāļāļāļĢāļĢāļĄāļāļēāļāļī āļāļ§āļēāļĄāļŦāļĨāļēāļāļŦāļĨāļēāļĒāļāļāļāļŠāļāļēāļāļāļĩāđāļāđāļāļāđāļāļĩāđāļĒāļ§ āļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒāđāļāļāļ·āđāļāļāļĩāđ āļāļēāļĢāļāļĄāļāļēāļāļĄāļāļāļŠāđāļ āļāļēāļĢāđāļāļīāļāđāļāļāļāļāļāļąāļāļāđāļāļāđāļāļĩāđāļĒāļ§ āļāļēāļĢāđāļāđāļāļāļąāļ āđāļĨāļ°āļāđāļ§āļāđāļ§āļĨāļēāđāļāļāļēāļĢāļāđāļāļāđāļāļĩāđāļĒāļ§ āļāļĨāļāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļĒāđāļāđāđāļāļĒāļ§āļīāļāļĩāļāļĢāļ°āļāļ§āļāļāļēāļĢāļĨāļģāļāļąāļāļāļąāđāļāđāļāļīāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļāļāļąāļāļāļĩ āļāļāļ§āđāļē āđāļāļāļāđāļāļĩāđāļĄāļĩāļāđāļēāļāđāļģāļŦāļāļąāļāļāļ§āļēāļĄāļŠāļģāļāļąāļāļĄāļēāļāļāļĩāđāļŠāļļāļāļāļ·āļ āļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒāđāļāļāļ·āđāļāļāļĩāđ āđāļĨāļ°āđāļāļāļāđāļāļĩāđāļĄāļĩāļāđāļēāļāđāļģāļŦāļāļąāļāļāļ§āļēāļĄāļŠāļģāļāļąāļāļĢāļāļāļĨāļāļĄāļēāļāļ·āļ āļāļ·āļ āļāļēāļĢāđāļāļīāļāđāļāļāļāļāļāļąāļāļāđāļāļāđāļāļĩāđāļĒāļ§ āđāļĨāļ°āļāļ§āļēāļĄāļŦāļĨāļēāļāļŦāļĨāļēāļĒāļāļāļāļŠāļāļēāļāļāļĩāđāļāđāļāļāđāļāļĩāđāļĒāļ§ āļāļēāļĄāļĨāļģāļāļąāļ āđāļāļĒāļāļēāļāđāļĨāļ·āļāļāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄāļāļēāļāļāļēāļĢāļāļģāļ§āļīāļāļĩ PROMETHEE II āļĄāļēāđāļāđāđāļāļāļēāļĢāļāļąāļāļĨāļģāļāļąāļāļāļāļāļāļēāļāđāļĨāļ·āļāļ āļāļ·āļ āļāļąāļāļŦāļ§āļąāļāļāļĨāļāļļāļĢāļĩ āļāļķāđāļāļŠāļāļāļāļĨāđāļāļāļāļąāļāđāļāļ§āļāļēāļāļāļāļāļāļđāđāļāļĢāļīāļŦāļēāļĢāļĢāļ°āļāļąāļāļŠāļđāļ āđāļĨāļ°āđāļĄāđāđāļāđāđāļāļŠāļāļēāļāļāļēāļĢāļāđāļŦāļĨāļąāļāļāļēāļĢāđāļāļĢāđāļĢāļ°āļāļēāļāļāļāļāđāļ§āļĢāļąāļŠāđāļāđāļĢāļāđāļēāļŠāļēāļĒāļāļąāļāļāļļāđāđāļŦāļĄāđ 2019 āļāļēāļāļāđāļāļĄāļđāļĨāļāļģāļāļ§āļāļāļđāđāđāļāđāļēāļāļąāļāđāļĢāļĄāđāļāļāļĩ 2563 āļāļāļāļāļĢāļ°āļāļĢāļ§āļāļāļēāļĢāļāđāļāļāđāļāļĩāđāļĒāļ§āđāļĨāļ°āļāļĩāļŽāļē āļāļāļ§āđāļē āļāļĨāļāļļāļĢāļĩāļĒāļąāļāļāļāđāļāđāļāļāļąāļāļŦāļ§āļąāļāļāļĩāđāļĄāļĩāļāļąāļāļāđāļāļāđāļāļĩāđāļĒāļ§āđāļāđāļēāļāļąāļāđāļĢāļĄāļĄāļēāļāļāļĩāđāļŠāļļāļāđāļāđāļāļĨāļģāļāļąāļāļāļĩāđ 2 āļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒāļāļĩāļāļāđāļ§āļĒThis research presented the location selection of beach hotels in Thailand by applying a hybrid multiple criteria decision making model using Fuzzy Analytic Hierarchy Process (FAHP) and PROMETHEE II Method. This study aims to investigate criteria for making decision on the location of tourism hotels, analyze the weight of criteria, and prioritize and identify appropriate alternatives of the location of tourism hotels by PROMETHEE II for supporting business investment plan and business strategy of the company. From interviewing with the executives who are the experts in hotel management, six alternatives are considered: Krabi, Prachuap Khiri Khan, Rayong, Chonburi, Phuket and Surat Thani with respect to seven criteria which are Environmental Integrity, Various Tourist Attractions, Security, Transportation, Tourist Growth Rate, Competition and Seasonality in tourism. The result from FAHP analysis showed that the most important criterion is Security followed by Tourist Growth Rate, and Various Tourist Attractions, respectively. After using PROMETHEE II Method to prioritize alternative, the results revealed that Chonburi is the most appropriate alternative in line with the opinion of the executives. Even though during the COVID-19 pandemic in 2019, the number of guests in 2020 provided by Ministry of Tourism and Sports showed that Chonburi had the second highest number of tourists in Thailand
Patient choice modelling: How do patients choose their hospitals?
As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient's choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient's choice of hospital rather than statistics available on the Internet
āļāļąāļāļŦāļēāļāļēāļĢāļāļąāļāđāļŠāđāļāļāļēāļāļāļēāļĢāļāļāļŠāđāļāļŠāļīāļāļāđāļēāļāļĢāļ°āđāļ āļāļ§āļąāļŠāļāļļāļāđāļāļŠāļĢāđāļēāļVehicle Routing Problem for Construction Materials
āļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāļĄāļĩāļāļļāļāļāļĢāļ°āļŠāļāļāđāđāļāļ·āđāļāļĻāļķāļāļĐāļēāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĢāļ°āļāļēāļĒāļŠāļīāļāļāđāļēāđāļāļĒāļąāļāļĨāļđāļāļāđāļēāđāļĨāļ°āļāļāļāđāļāļāđāļŠāđāļāļāļēāļāļāļēāļĢāļāļāļŠāđāļāļāļĢāļ°āđāļ āļāļ§āļąāļŠāļāļļāļāđāļāļŠāļĢāđāļēāļāļāļķāđāļāļĄāļĩāļāļāļēāļ āļĢāļđāļāļāļĢāļāđāļĨāļ°āļāđāļģāļŦāļāļąāļāļāļĩāđāđāļāļāļāđāļēāļāļāļąāļ āđāļāļĒāļāļēāļĻāļąāļĒāļāļąāļĨāļāļāļĢāļīāļāļķāļĄāļāļāļāļāļąāļāļŦāļēāļāļēāļĢāļāļąāļāđāļŠāđāļāļāļēāļāļāļēāļĢāļāļāļŠāđāļ (Vehicle Routing Problem) āļāļķāđāļāļāļ°āļāļąāļāļāļēāļĢāļ°āļāļāļāļēāļĢāļāļąāļāđāļŠāđāļāļāļēāļāļāļēāļĢāļāļāļŠāđāļāļŠāļīāļāļāđāļēāļāļēāļāļāļĩāđāļāļąāđāļāļāļļāļāļāļĢāļ°āļāļēāļĒāļŠāļīāļāļāđāļēāđāļāļĒāļąāļāļĨāļđāļāļāđāļēāļāđāļēāļāđ āđāļāļĒāļāļģāļāļķāļāļāļķāļāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļĢāļĢāļāļļāļāļŠāļīāļāļāđāļēāļāļāļāļĒāļēāļāļāļēāļŦāļāļ°āļāļĩāđāļĄāļĩāļāļĒāļđāđ āļĢāļ§āļĄāļāļąāđāļāļāđāļāļāļŠāļāļāļāļĨāđāļāļāļāļēāļĄāļāđāļāļāļģāļāļąāļāļāļāļāļāļĢāļąāļāļĒāļēāļāļĢāļāļĩāđāļĄāļĩāļāļĒāļđāđāđāļĨāļ°āļāļ§āļēāļĄāļāđāļāļāļāļēāļĢāļāļāļāļĨāļđāļāļāđāļē āđāļāļĒāļāļģāļāļ§āļāļŦāļēāļāđāļāļāļļāļāđāļĨāļ°āđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāđāļēāđāļāđāļāđāļēāļĒāđāļāļāļēāļĢāļāļāļŠāđāļ āđāļāļĒāđāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļĩāđāđāļāđāļāļģāļāļēāļĢāđāļāđāļāļĢāļ§āļāļĢāļ§āļĄāļāđāļāļĄāļđāļĨāđāļāļĢāļ°āļŦāļ§āđāļēāļāđāļāļ·āļāļāļĄāļāļĢāļēāļāļĄāļāļķāļāļāļąāļāļ§āļēāļāļĄ 2558 āļāļēāļāļĢāļ°āļāļāļāļēāļĢāļŠāļąāđāļāļāļ·āđāļāļŠāļīāļāļāđāļēāļāļāļāļĨāļđāļāļāđāļēāđāļĨāļ°āļāļēāļĢāļāļāļŠāđāļāļāļāļāļāļĢāļīāļĐāļąāļāļāļģāļŦāļāđāļēāļĒāļ§āļąāļŠāļāļļāļāđāļāļŠāļĢāđāļēāļāđāļŦāđāļāļŦāļāļķāđāļāđāļāļāļąāļāļŦāļ§āļąāļāļāļāļĢāļĢāļēāļāļŠāļĩāļĄāļē āđāļĨāļ°āđāļāđāļāļģāđāļŠāļāļāļāļąāļĨāļāļāļĢāļĩāļāļķāļĄāļāļĩāđāļāļąāļāļāļēāļĄāļēāļāļēāļāļ§āļīāļāļĩāļāļēāļĢāđāļāļāļāļĢāļ°āļŦāļĒāļąāļ (Saving Algorithm) āđāļĨāļ°āļ§āļīāļāļĩāļāļēāļĢāđāļāļīāļāļāļąāļāļāļļāļāļĢāļĢāļĄ (Genetic Algorithm) āļāļķāđāļāđāļāđāļāļ§āļīāļāļĩāļāļēāļĢāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāļāļąāļāđāļŠāđāļāļāļēāļāļāļĩāđāļĄāļĩāļŦāļĨāļēāļāļŦāļĨāļēāļĒāđāļŠāđāļāļāļēāļāļāļēāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļāļ§āđāļēāđāļŠāđāļāļāļēāļāđāļāļīāļāļĢāļāļāļĩāđāļāļąāļāđāļāļĒāļ§āļīāļāļĩāļāļēāļĢāđāļāļīāļāļāļąāļāļāļļāļāļĢāļĢāļĄāļĄāļĩāļĢāļ°āļĒāļ°āļāļēāļāļāļēāļĢāļāļāļŠāđāļāļāđāļāļĒāļāļ§āđāļēāļ§āļīāļāļĩāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļĢāđāļāļāļąāļāļāļļāļāļąāļāļāļķāļ 32.99% āđāļĨāļ°āļĄāļĩāļāđāļēāđāļāđāļāđāļēāļĒāļāļĩāđāļĨāļāļĨāļāđāļāđāļāđāļāļķāļ 45.23% āļāļąāļāļāļąāđāļāļ§āļīāļāļĩāļāļēāļĢāđāļāļīāļāļāļąāļāļāļļāļāļĢāļĢāļĄāļŠāļēāļĄāļēāļĢāļāļāļąāļāđāļŠāđāļāļāļēāļāļāļēāļĢāļāļāļŠāđāļāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļŠāļģāļŦāļĢāļąāļāļāļąāļāļŦāļēāļāļąāļāļāļĨāđāļēāļ§āļāļĩāđThis research paper aimed to study outbound logistics and to design a delivery route plan for construction materials by using the algorithms for the vehicle routing problem, where products are distributed from a distribution center to several customers. The saving algorithm and genetic algorithm were applied to solve the vehicle routing problem with construction materials that were of different sizes, shapes, and weight under the available resource constraints and customer requests. The total distances and costs obtained from two algorithms were compared and the best solution was proposed. The company in Nakhon Ratchasima province in Thailand was used as a case study. Data were collected between January and December 2015 and the results showed that the generic algorithm provided a shorter distance than the current delivery system by 32.99%. Moreover, the genetic algorithm reduced the total cost of the current delivery method by 45.23%. Therefore, the solution of the generic algorithm is presented for scheduling the delivery routes for construction materials
Location of low-cost blood collection and distribution centres in Thailand
Decision making on facility locations for blood services has an impact on the efficiency of supply chain and logistics systems. In the blood supply chain operated by the Thai Red Cross Society (TRCS), problems are faced with amounts of blood collected in different provinces of Thailand being insufficient to meet demand. At the present time, TRCS operates one National Blood Centre in the capital and twelve Regional Blood Centres in different provinces to collect, prepare, test, and distribute safe blood. A proposal has been made to extend this network of blood centres using low-cost collection and distribution centres. Increasing numbers of fixed collection sites can improve access for donors. In addition, some facilities will be able to perform preparation and storage for blood that hospitals can receive directly. This paper addresses the selection of sites for two types of facility, either a blood donation room only or donation room with a distribution centre. A range of investment budgets is investigated to inform the strategic plan of this non-profit organisation. We present a novel binary integer programming model for this location-allocation problem based on objectives of improving the supply of blood products while reducing costs of transportation. Computational results are reported, using real life data, that are of practical importance to decision makers
Designing the blood supply chain: How much, how and where?
BACKGROUND: The blood supply chain network can take many forms in different settings, depending on local factors such as geography, politics, costs, etc.; however, many developed countries are moving towards centralized facilities. The goal for all blood distribution networks, regardless of design, remains the same: to satisfy demand at minimal cost and minimal wastage.STUDY DESIGN AND METHODS: Mathematically, the blood supply system design can be viewed as a location-allocation problem, where the aim is to find the optimal location of facilities and to assign hospitals to them to minimize total system cost. However, most location-allocation models in the blood supply chain modeling literature omit important aspects of the problem, such as selecting amongst differing methods of collection and production. In this paper, we present a location-allocation model that takes these factors into account to support strategic decision-making at different levels of centralization. RESULTS: Our approach is illustrated by a case study (Colombia) to redesign the national blood supply chain under a range of realistic travel time limitations. For each scenario, an optimal supply chain configuration is obtained, together with optimal collection and production strategies. We show that the total costs for the most centralized scenario are around 40% of the costs for the least centralized scenario.CONCLUSION: Centralized systems are more efficient than decentralized systems. However, the latter may be preferred for political or geographical reasons. Our model allows decision-makers to redesign the supply network per local circumstances, and determine optimal collection and production strategies that minimize total costs