15 research outputs found

    āđāļšāļšāļˆāđāļēāļĨāļ­āļ‡āļāļēāļĢāļžāļĒāļēāļāļĢāļ“āđŒāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļ‚āļ­āļ‡āļ­āļąāļ•āļĢāļēāđāļĨāļāđ€āļ›āļĨāļĩāđˆāļĒāļ™āļŠāļāļļāļĨāđ€āļ‡āļīāļ™āļĒāļđāđ‚āļĢāđ€āļ—āļĩāļĒāļšāļāļąāļšāļ”āļ­āļĨāļĨāļēāļĢāđŒāļŠāļŦāļĢāļąāļāđāļšāļšāļĢāļēāļĒāļ§āļąāļ™āļ”āđ‰āļ§āļĒāļ‚āđ‰āļ­āļĄāļđāļĨāļ‚āļąāđ‰āļ§āļ„āļ§āļēāļĄāļĢāļđāđ‰āļŠāļķāļāļ‚āļ­āļ‡āļ‚āđˆāļēāļ§āļŠāļēāļĢāļ­āļ­āļ™āđ„āļĨāļ™āđŒA Trend Forecasting Model of Daily Euro/USD Exchange Rate Using Sentiment Analysis on Online News

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    āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļžāļĒāļēāļāļĢāļ“āđŒāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļ­āļąāļ•āļĢāļēāđāļĨāļāđ€āļ›āļĨāļĩāđˆāļĒāļ™āļŠāļāļļāļĨāđ€āļ‡āļīāļ™āļĒāļđāđ‚āļĢāđ€āļ—āļĩāļĒāļšāļāļąāļšāļ”āļ­āļĨāļĨāļēāļĢāđŒāļŠāļŦāļĢāļąāļāđƒāļ™āļ•āļĨāļēāļ”āļ‹āļ·āđ‰āļ­āļ‚āļēāļĒāđāļĨāļāđ€āļ›āļĨāļĩāđˆāļĒāļ™āļŠāļāļļāļĨāđ€āļ‡āļīāļ™āļĢāļ°āļŦāļ§āđˆāļēāļ‡āļ›āļĢāļ°āđ€āļ—āļĻ āđāļĨāļ°āļāļēāļĢāļ—āļ”āļŠāļ­āļšāļŠāļĄāļĄāļ•āļīāļāļēāļ™āđ€āļŠāļīāļ‡āđ€āļ›āđ‡āļ™āđ€āļŦāļ•āļļāđ€āļ›āđ‡āļ™āļœāļĨāļĢāļ°āļŦāļ§āđˆāļēāļ‡āļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāđāļāļĢāļ™āđ€āļˆāļ­āļĢāđŒ āđ‚āļ”āļĒāļ‚āđ‰āļ­āļĄāļđāļĨāļ—āļĩāđˆāļ™āļģāļĄāļēāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļĻāļķāļāļĐāļēāļ›āļĢāļ°āļāļ­āļšāļ”āđ‰āļ§āļĒāļ‚āđ‰āļ­āļĄāļđāļĨāđāļšāļšāļĄāļĩāđ‚āļ„āļĢāļ‡āļŠāļĢāđ‰āļēāļ‡āđ„āļ”āđ‰āđāļāđˆ āđ€āļŠāđ‰āļ™āļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ—āļĩāđˆ āļĢāļēāļ„āļēāļ—āļ­āļ‡āļ„āļģāđāļĨāļ°āļĢāļēāļ„āļēāļ™āđ‰āļģāļĄāļąāļ™āļ”āļīāļš āđāļĨāļ°āļ‚āđ‰āļ­āļĄāļđāļĨāđāļšāļšāđ„āļĄāđˆāļĄāļĩāđ‚āļ„āļĢāļ‡āļŠāļĢāđ‰āļēāļ‡āđ„āļ”āđ‰āđāļāđˆ āļŠāļąāļ”āļŠāđˆāļ§āļ™āļ‚āļ­āļ‡āļˆāļģāļ™āļ§āļ™āļ‚āđˆāļēāļ§āļ‚āļąāđ‰āļ§āļ„āļ§āļēāļĄāļĢāļđāđ‰āļŠāļķāļāđ€āļŠāļīāļ‡āļšāļ§āļ āđ€āļŠāļīāļ‡āļĨāļš āđāļĨāļ°āđ€āļ›āđ‡āļ™āļāļĨāļēāļ‡āļ‚āļ­āļ‡āļ‚āđˆāļēāļ§āļŠāļēāļĢāļ—āļĩāđˆāđ€āļāļĩāđˆāļĒāļ§āļ‚āđ‰āļ­āļ‡āļāļąāļšāļ­āļąāļ•āļĢāļēāļāļēāļĢāđāļĨāļāđ€āļ›āļĨāļĩāđˆāļĒāļ™āļŠāļāļļāļĨāđ€āļ‡āļīāļ™āļĒāļđāđ‚āļĢāđ€āļ—āļĩāļĒāļšāļāļąāļšāļ”āļ­āļĨāļĨāļēāļĢāđŒāļŠāļŦāļĢāļąāļ āļ‹āļķāđˆāļ‡āđ„āļ”āđ‰āđ€āļāđ‡āļšāļĢāļ§āļšāļĢāļ§āļĄāđƒāļ™āļĢāļ°āļŦāļ§āđˆāļēāļ‡āļ§āļąāļ™āļ—āļĩāđˆ 2 āđ€āļ”āļ·āļ­āļ™āļ•āļļāļĨāļēāļ„āļĄ āļž.āļĻ. 2563 āļ–āļķāļ‡āļ§āļąāļ™āļ—āļĩāđˆ 10 āđ€āļ”āļ·āļ­āļ™āļāļļāļĄāļ āļēāļžāļąāļ™āļ˜āđŒ āļž.āļĻ. 2564 āļˆāļģāļ™āļ§āļ™ 92 āļĢāļēāļĒāļāļēāļĢ āđ‚āļ”āļĒāļ‚āđ‰āļ­āļĄāļđāļĨāļ‚āđˆāļēāļ§āļŠāļēāļĢāļ—āļĩāđˆāđ„āļ”āđ‰āļˆāļ°āļ–āļđāļāļ™āļģāļĄāļēāļ›āļĢāļ°āļĄāļ§āļĨāļœāļĨāđ‚āļ”āļĒāļ§āļīāļ˜āļĩāđ€āļ—āļ„āļ™āļīāļ„āļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ‚āļąāđ‰āļ§āļ„āļ§āļēāļĄāļĢāļđāđ‰āļŠāļķāļāđāļĨāļ°āļ§āļīāļ˜āļĩāļāļēāļĢāļžāļĒāļēāļāļĢāļ“āđŒāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļ—āļĩāđˆāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļĻāļķāļāļĐāļēāļ™āļĩāđ‰āđ„āļ”āđ‰āđāļāđˆ 1) āđ‚āļ„āļĢāļ‡āļ‚āđˆāļēāļĒāļ›āļĢāļ°āļŠāļēāļ—āđ€āļ—āļĩāļĒāļĄ 2) āļ‹āļąāļžāļžāļ­āļĢāđŒāļ•āđ€āļ§āļāđ€āļ•āļ­āļĢāđŒāđāļĄāļ—āļŠāļĩāļ™ āđāļĨāļ° 3) āļāļēāļĢāļˆāļģāđāļ™āļāļ›āļĢāļ°āđ€āļ āļ—āđāļšāļšāļāļēāļĢāļŠāļļāđˆāļĄāļ›āđˆāļēāđ„āļĄāđ‰ āļ™āļ­āļāļˆāļēāļāļ™āļĩāđ‰āđ„āļ”āđ‰āļ—āļģāļāļēāļĢāđ€āļ›āļĢāļĩāļĒāļšāđ€āļ—āļĩāļĒāļšāļāļēāļĢāļ§āļąāļ”āļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāļ‚āļ­āļ‡āļ•āļąāļ§āđāļšāļšāļžāļĒāļēāļāļĢāļ“āđŒ āļ„āļ·āļ­āļ§āļīāļ˜āļĩāļāļēāļĢāđāļšāđˆāļ‡āļ‚āđ‰āļ­āļĄāļđāļĨāļŠāļģāļŦāļĢāļąāļšāļŠāļ­āļ™āđāļĨāļ°āļ§āļąāļ”āļœāļĨāđāļĨāļ°āļ§āļīāļ˜āļĩāļāļēāļĢāļ•āļĢāļ§āļˆāļŠāļ­āļšāđāļšāļšāđ„āļ‚āļ§āđ‰ āļœāļĨāļāļēāļĢāļĻāļķāļāļĐāļēāļ‚āđˆāļēāļ§āļ—āļĩāđˆāļĄāļĩāļ‚āļąāđ‰āļ§āļ„āļ§āļēāļĄāļĢāļđāđ‰āļŠāļķāļāđ€āļŠāļīāļ‡āļšāļ§āļāđāļĨāļ°āļ‚āđˆāļēāļ§āļ—āļĩāđˆāļĄāļĩāļ‚āļąāđ‰āļ§āļ„āļ§āļēāļĄāļĢāļđāđ‰āļŠāļķāļāđ€āļ›āđ‡āļ™āļāļĨāļēāļ‡āļĄāļĩāļ„āļ§āļēāļĄāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒāļāļąāļšāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļ­āļąāļ•āļĢāļēāđāļĨāļāđ€āļ›āļĨāļĩāđˆāļĒāļ™āļŠāļāļļāļĨāđ€āļ‡āļīāļ™āļĒāļđāđ‚āļĢāđ€āļ—āļĩāļĒāļšāļāļąāļšāļ”āļ­āļĨāļĨāļēāļĢāđŒāļŠāļŦāļĢāļąāļ āđāļĨāļ°āļ•āļąāļ§āđāļšāļšāļžāļĒāļēāļāļĢāļ“āđŒāđāļ™āļ§āđ‚āļ™āđ‰āļĄāļ—āļĩāđˆāļŠāļĢāđ‰āļēāļ‡āļ‚āļķāđ‰āļ™āļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāļāļēāļĢāļˆāļģāđāļ™āļāļ›āļĢāļ°āđ€āļ āļ—āđāļšāļšāļāļēāļĢāļŠāļļāđˆāļĄāļ›āđˆāļēāđ„āļĄāđ‰āļˆāļēāļāļāļēāļĢāđāļšāđˆāļ‡āļ‚āđ‰āļ­āļĄāļđāļĨāļ§āļīāļ˜āļĩāļ§āļīāļ˜āļĩāļāļēāļĢāđāļšāđˆāļ‡āļ‚āđ‰āļ­āļĄāļđāļĨāļŠāļģāļŦāļĢāļąāļšāļŠāļ­āļ™āđāļĨāļ°āļ§āļąāļ”āļœāļĨāđƒāļŦāđ‰āļœāļĨāļĨāļąāļžāļ˜āđŒāļ—āļĩāđˆāļ”āļĩāļ—āļĩāđˆāļŠāļļāļ” āđ‚āļ”āļĒāļĄāļĩāļ„āđˆāļēāļ„āļ§āļēāļĄāļ–āļđāļāļ•āđ‰āļ­āļ‡ āļ„āļīāļ”āđ€āļ›āđ‡āļ™āļĢāđ‰āļ­āļĒāļĨāļ° 71.43 āļ—āļąāđ‰āļ‡āļ™āļĩāđ‰āļ•āļąāļ§āđāļšāļšāļāļēāļĢāļˆāļģāđāļ™āļāļ›āļĢāļ°āđ€āļ āļ—āđāļšāļšāļāļēāļĢāļŠāļļāđˆāļĄāļ›āđˆāļēāđ„āļĄāđ‰ āļŠāļēāļĄāļēāļĢāļ–āļ™āļģāļĄāļēāđƒāļŠāđ‰āļžāļĒāļēāļāļĢāļ“āđŒāļ­āļąāļ•āļĢāļēāđāļĨāļāđ€āļ›āļĨāļĩāđˆāļĒāļ™āļŠāļāļļāļĨāđ€āļ‡āļīāļ™āļĒāļđāđ‚āļĢāđ€āļ—āļĩāļĒāļšāļāļąāļšāļ”āļ­āļĨāļĨāļēāļĢāđŒāļŠāļŦāļĢāļąāļāđ€āļžāļ·āđˆāļ­āđ€āļ›āđ‡āļ™āđāļ™āļ§āļ—āļēāļ‡āļāļēāļĢāļĨāļ‡āļ—āļļāļ™āļāļēāļĢāļ‹āļ·āđ‰āļ­āļ‚āļēāļĒāđ„āļ”āđ‰āļ­āļĒāđˆāļēāļ‡āļ”āļĩThe 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

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    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° ± 1.29° and 1.36° ± 1.08° P = 0.007), 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

    āļāļēāļĢāļ›āļĢāļ°āļĒāļļāļāļ•āđŒāđƒāļŠāđ‰āļ•āļąāļ§āđāļšāļšāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāļŦāļĨāļēāļĒāļŦāļĨāļąāļāđ€āļāļ“āļ‘āđŒāđāļšāļšāļœāļŠāļĄāđ€āļžāļ·āđˆāļ­āđ€āļĨāļ·āļ­āļāļ—āļĩāđˆāļ•āļąāđ‰āļ‡āļ‚āļ­āļ‡āđ‚āļĢāļ‡āđāļĢāļĄāđāļ–āļšāļŠāļēāļĒāļŦāļēāļ”āđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒApplying a Hybrid Multiple Criteria Decision Making Model for Selecting the Location of Beach Hotels in Thailand

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    āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āđ„āļ”āđ‰āļ™āļģāđ€āļŠāļ™āļ­āļāļēāļĢāđ€āļĨāļ·āļ­āļāļ—āļģāđ€āļĨāļ—āļĩāđˆāļ•āļąāđ‰āļ‡āđ‚āļĢāļ‡āđāļĢāļĄāđāļ–āļšāļŠāļēāļĒāļŦāļēāļ”āđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒ āđ‚āļ”āļĒāļāļēāļĢāļ›āļĢāļ°āļĒāļļāļāļ•āđŒāđƒāļŠāđ‰āļ§āļīāļ˜āļĩāļāļĢāļ°āļšāļ§āļ™āļāļēāļĢāļĨāļģāļ”āļąāļšāļŠāļąāđ‰āļ™āđ€āļŠāļīāļ‡āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđāļšāļšāļŸāļąāļ‹āļ‹āļĩ (Fuzzy Analytic Hierarchy Process : FAHP) āļĢāđˆāļ§āļĄāļāļąāļ™āļāļąāļšāļ§āļīāļ˜āļĩ PROMETHEE II āđ‚āļ”āļĒāļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļĻāļķāļāļĐāļēāļŦāļēāđ€āļāļ“āļ‘āđŒāļ—āļĩāđˆāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāđ€āļĨāļ·āļ­āļāļ—āļģāđ€āļĨāļ—āļĩāđˆāļ•āļąāđ‰āļ‡āđ‚āļĢāļ‡āđāļĢāļĄ āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļ„āđˆāļēāļ™āđ‰āļģāļŦāļ™āļąāļāđ€āļāļ“āļ‘āđŒāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāđƒāļ™āļāļēāļĢāļŦāļēāļ—āļģāđ€āļĨāļ—āļĩāđˆāļ•āļąāđ‰āļ‡āļ‚āļ­āļ‡āđ‚āļĢāļ‡āđāļĢāļĄāđ‚āļ”āļĒāļ­āļēāļĻāļąāļĒāļ§āļīāļ˜āļĩāļāļĢāļ°āļšāļ§āļ™āļāļēāļĢāļĨāļģāļ”āļąāļšāļŠāļąāđ‰āļ™āđ€āļŠāļīāļ‡āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđāļšāļšāļŸāļąāļ‹āļ‹āļĩ āđāļĨāļ°āļˆāļąāļ”āļĨāļģāļ”āļąāļšāļžāļĢāđ‰āļ­āļĄāļ—āļąāđ‰āļ‡āļĢāļ°āļšāļļāļ—āļēāļ‡āđ€āļĨāļ·āļ­āļāļˆāļēāļāļ„āđˆāļēāļ™āđ‰āļģāļŦāļ™āļąāļāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāđ‚āļ”āļĒāļ­āļēāļĻāļąāļĒāļ§āļīāļ˜āļĩ PROMETHEE II āđ€āļžāļ·āđˆāļ­āļ™āļģāļĄāļēāļŠāļ™āļąāļšāļŠāļ™āļļāļ™āļāļēāļĢāļ§āļēāļ‡āđāļœāļ™āļāļēāļĢāļĨāļ‡āļ—āļļāļ™āđāļĨāļ°āļāļĨāļĒāļļāļ—āļ˜āđŒāļ‚āļ­āļ‡āļšāļĢāļīāļĐāļąāļ—āļāļĢāļ“āļĩāļĻāļķāļāļĐāļē āđ‚āļ”āļĒāđƒāļ™āļāļēāļĢāļŠāļąāļĄāļ āļēāļĐāļ“āđŒāļœāļđāđ‰āļšāļĢāļīāļŦāļēāļĢāļ‹āļķāđˆāļ‡āđ€āļ›āđ‡āļ™āļœāļđāđ‰āđ€āļŠāļĩāđˆāļĒāļ§āļŠāļēāļāđƒāļ™āļ”āđ‰āļēāļ™āļāļēāļĢāļšāļĢāļīāļŦāļēāļĢāļˆāļąāļ”āļāļēāļĢāđ‚āļĢāļ‡āđāļĢāļĄāđ„āļ”āđ‰āļžāļīāļˆāļēāļĢāļ“āļēāđāļĨāļ°āļāļģāļŦāļ™āļ”āļ—āļēāļ‡āđ€āļĨāļ·āļ­āļāđ„āļ§āđ‰āļ—āļąāđ‰āļ‡āļŦāļĄāļ” 6 āļ—āļēāļ‡āđ€āļĨāļ·āļ­āļ āđ„āļ”āđ‰āđāļāđˆ āļāļĢāļ°āļšāļĩāđˆ āļ›āļĢāļ°āļˆāļ§āļšāļ„āļĩāļĢāļĩāļ‚āļąāļ™āļ˜āđŒ āļĢāļ°āļĒāļ­āļ‡ āļŠāļĨāļšāļļāļĢāļĩ āļ āļđāđ€āļāđ‡āļ• āđāļĨāļ°āļŠāļļāļĢāļēāļĐāļŽāļĢāđŒāļ˜āļēāļ™āļĩ āđƒāļ™āļŠāđˆāļ§āļ™āļ‚āļ­āļ‡āđ€āļāļ“āļ‘āđŒāļāļēāļĢāļ•āļąāļ”āļŠāļīāļ™āđƒāļˆāļĄāļĩāđ€āļāļ“āļ‘āđŒāļŦāļĨāļąāļāļ—āļĩāđˆāļŠāļģāļ„āļąāļāļ—āļąāđ‰āļ‡āļŦāļĄāļ” 7 āđ€āļāļ“āļ‘āđŒ āđ„āļ”āđ‰āđāļāđˆ āļ„āļ§āļēāļĄāļŠāļĄāļšāļđāļĢāļ“āđŒāļ‚āļ­āļ‡āļŠāļ āļēāļžāđāļ§āļ”āļĨāđ‰āļ­āļĄāļ—āļēāļ‡āļ˜āļĢāļĢāļĄāļŠāļēāļ•āļī āļ„āļ§āļēāļĄāļŦāļĨāļēāļāļŦāļĨāļēāļĒāļ‚āļ­āļ‡āļŠāļ–āļēāļ™āļ—āļĩāđˆāļ—āđˆāļ­āļ‡āđ€āļ—āļĩāđˆāļĒāļ§ āļ„āļ§āļēāļĄāļ›āļĨāļ­āļ”āļ āļąāļĒāđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆ āļāļēāļĢāļ„āļĄāļ™āļēāļ„āļĄāļ‚āļ™āļŠāđˆāļ‡ āļāļēāļĢāđ€āļ•āļīāļšāđ‚āļ•āļ‚āļ­āļ‡āļ™āļąāļāļ—āđˆāļ­āļ‡āđ€āļ—āļĩāđˆāļĒāļ§ āļāļēāļĢāđāļ‚āđˆāļ‡āļ‚āļąāļ™ āđāļĨāļ°āļŠāđˆāļ§āļ‡āđ€āļ§āļĨāļēāđƒāļ™āļāļēāļĢāļ—āđˆāļ­āļ‡āđ€āļ—āļĩāđˆāļĒāļ§ āļœāļĨāļˆāļēāļāļāļēāļĢāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđ‚āļ”āļĒāđƒāļŠāđ‰āđ‚āļ”āļĒāļ§āļīāļ˜āļĩāļāļĢāļ°āļšāļ§āļ™āļāļēāļĢāļĨāļģāļ”āļąāļšāļŠāļąāđ‰āļ™āđ€āļŠāļīāļ‡āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāđāļšāļšāļŸāļąāļ‹āļ‹āļĩ āļžāļšāļ§āđˆāļē āđ€āļāļ“āļ‘āđŒāļ—āļĩāđˆāļĄāļĩāļ„āđˆāļēāļ™āđ‰āļģāļŦāļ™āļąāļāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļĄāļēāļāļ—āļĩāđˆāļŠāļļāļ”āļ„āļ·āļ­ āļ„āļ§āļēāļĄāļ›āļĨāļ­āļ”āļ āļąāļĒāđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆ āđāļĨāļ°āđ€āļāļ“āļ‘āđŒāļ—āļĩāđˆāļĄāļĩāļ„āđˆāļēāļ™āđ‰āļģāļŦāļ™āļąāļāļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļĢāļ­āļ‡āļĨāļ‡āļĄāļēāļ„āļ·āļ­ āļ„āļ·āļ­ āļāļēāļĢāđ€āļ•āļīāļšāđ‚āļ•āļ‚āļ­āļ‡āļ™āļąāļāļ—āđˆāļ­āļ‡āđ€āļ—āļĩāđˆāļĒāļ§ āđāļĨāļ°āļ„āļ§āļēāļĄāļŦāļĨāļēāļāļŦāļĨāļēāļĒāļ‚āļ­āļ‡āļŠāļ–āļēāļ™āļ—āļĩāđˆāļ—āđˆāļ­āļ‡āđ€āļ—āļĩāđˆāļĒāļ§ āļ•āļēāļĄāļĨāļģāļ”āļąāļš āđ‚āļ”āļĒāļ—āļēāļ‡āđ€āļĨāļ·āļ­āļāļ—āļĩāđˆāđ€āļŦāļĄāļēāļ°āļŠāļĄāļˆāļēāļāļāļēāļĢāļ™āļģāļ§āļīāļ˜āļĩ PROMETHEE II āļĄāļēāđƒāļŠāđ‰āđƒāļ™āļāļēāļĢāļˆāļąāļ”āļĨāļģāļ”āļąāļšāļ‚āļ­āļ‡āļ—āļēāļ‡āđ€āļĨāļ·āļ­āļ āļ„āļ·āļ­ āļˆāļąāļ‡āļŦāļ§āļąāļ”āļŠāļĨāļšāļļāļĢāļĩ āļ‹āļķāđˆāļ‡āļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļąāļšāđāļ™āļ§āļ—āļēāļ‡āļ‚āļ­āļ‡āļœāļđāđ‰āļšāļĢāļīāļŦāļēāļĢāļĢāļ°āļ”āļąāļšāļŠāļđāļ‡ āđāļĨāļ°āđāļĄāđ‰āđāļ•āđˆāđƒāļ™āļŠāļ–āļēāļ™āļāļēāļĢāļ“āđŒāļŦāļĨāļąāļ‡āļāļēāļĢāđāļžāļĢāđˆāļĢāļ°āļšāļēāļ”āļ‚āļ­āļ‡āđ„āļ§āļĢāļąāļŠāđ‚āļ„āđ‚āļĢāļ™āđˆāļēāļŠāļēāļĒāļžāļąāļ™āļ˜āļļāđŒāđƒāļŦāļĄāđˆ 2019 āļˆāļēāļāļ‚āđ‰āļ­āļĄāļđāļĨāļˆāļģāļ™āļ§āļ™āļœāļđāđ‰āđ€āļ‚āđ‰āļēāļžāļąāļāđāļĢāļĄāđƒāļ™āļ›āļĩ 2563 āļ‚āļ­āļ‡āļāļĢāļ°āļ—āļĢāļ§āļ‡āļāļēāļĢāļ—āđˆāļ­āļ‡āđ€āļ—āļĩāđˆāļĒāļ§āđāļĨāļ°āļāļĩāļŽāļē āļžāļšāļ§āđˆāļē āļŠāļĨāļšāļļāļĢāļĩāļĒāļąāļ‡āļ„āļ‡āđ€āļ›āđ‡āļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āļ—āļĩāđˆāļĄāļĩāļ™āļąāļāļ—āđˆāļ­āļ‡āđ€āļ—āļĩāđˆāļĒāļ§āđ€āļ‚āđ‰āļēāļžāļąāļāđāļĢāļĄāļĄāļēāļāļ—āļĩāđˆāļŠāļļāļ”āđ€āļ›āđ‡āļ™āļĨāļģāļ”āļąāļšāļ—āļĩāđˆ 2 āļ‚āļ­āļ‡āļ›āļĢāļ°āđ€āļ—āļĻāđ„āļ—āļĒāļ­āļĩāļāļ”āđ‰āļ§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?

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    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

    āļ›āļąāļāļŦāļēāļāļēāļĢāļˆāļąāļ”āđ€āļŠāđ‰āļ™āļ—āļēāļ‡āļāļēāļĢāļ‚āļ™āļŠāđˆāļ‡āļŠāļīāļ™āļ„āđ‰āļēāļ›āļĢāļ°āđ€āļ āļ—āļ§āļąāļŠāļ”āļļāļāđˆāļ­āļŠāļĢāđ‰āļēāļ‡Vehicle Routing Problem for Construction Materials

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    āļ‡āļēāļ™āļ§āļīāļˆāļąāļĒāļ™āļĩāđ‰āļĄāļĩāļˆāļļāļ”āļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļĻāļķāļāļĐāļēāļāļĢāļ°āļšāļ§āļ™āļāļēāļĢāļāļĢāļ°āļˆāļēāļĒāļŠāļīāļ™āļ„āđ‰āļēāđ„āļ›āļĒāļąāļ‡āļĨāļđāļāļ„āđ‰āļēāđāļĨāļ°āļ­āļ­āļāđāļšāļšāđ€āļŠāđ‰āļ™āļ—āļēāļ‡āļāļēāļĢāļ‚āļ™āļŠāđˆāļ‡āļ›āļĢāļ°āđ€āļ āļ—āļ§āļąāļŠāļ”āļļāļāđˆāļ­āļŠāļĢāđ‰āļēāļ‡āļ‹āļķāđˆāļ‡āļĄāļĩāļ‚āļ™āļēāļ” āļĢāļđāļ›āļ—āļĢāļ‡āđāļĨāļ°āļ™āđ‰āļģāļŦāļ™āļąāļāļ—āļĩāđˆāđāļ•āļāļ•āđˆāļēāļ‡āļāļąāļ™ āđ‚āļ”āļĒāļ­āļēāļĻāļąāļĒāļ­āļąāļĨāļāļ­āļĢāļīāļ—āļķāļĄāļ‚āļ­āļ‡āļ›āļąāļāļŦāļēāļāļēāļĢāļˆāļąāļ”āđ€āļŠāđ‰āļ™āļ—āļēāļ‡āļāļēāļĢāļ‚āļ™āļŠāđˆāļ‡ (Vehicle Routing Problem) āļ‹āļķāđˆāļ‡āļˆāļ°āļžāļąāļ’āļ™āļēāļĢāļ°āļšāļšāļāļēāļĢāļˆāļąāļ”āđ€āļŠāđ‰āļ™āļ—āļēāļ‡āļāļēāļĢāļ‚āļ™āļŠāđˆāļ‡āļŠāļīāļ™āļ„āđ‰āļēāļˆāļēāļāļ—āļĩāđˆāļ•āļąāđ‰āļ‡āļˆāļļāļ”āļāļĢāļ°āļˆāļēāļĒāļŠāļīāļ™āļ„āđ‰āļēāđ„āļ›āļĒāļąāļ‡āļĨāļđāļāļ„āđ‰āļēāļ•āđˆāļēāļ‡āđ† āđ‚āļ”āļĒāļ„āļģāļ™āļķāļ‡āļ–āļķāļ‡āļ„āļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļ–āđƒāļ™āļāļēāļĢāļšāļĢāļĢāļ—āļļāļāļŠāļīāļ™āļ„āđ‰āļēāļ‚āļ­āļ‡āļĒāļēāļ™āļžāļēāļŦāļ™āļ°āļ—āļĩāđˆāļĄāļĩāļ­āļĒāļđāđˆ āļĢāļ§āļĄāļ—āļąāđ‰āļ‡āļ•āđ‰āļ­āļ‡āļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļ•āļēāļĄāļ‚āđ‰āļ­āļˆāļģāļāļąāļ”āļ‚āļ­āļ‡āļ—āļĢāļąāļžāļĒāļēāļāļĢāļ—āļĩāđˆāļĄāļĩāļ­āļĒāļđāđˆāđāļĨāļ°āļ„āļ§āļēāļĄāļ•āđ‰āļ­āļ‡āļāļēāļĢāļ‚āļ­āļ‡āļĨāļđāļāļ„āđ‰āļē āđ‚āļ”āļĒāļ„āļģāļ™āļ§āļ“āļŦāļēāļ•āđ‰āļ™āļ—āļļāļ™āđāļĨāļ°āđ€āļ›āļĢāļĩāļĒāļšāđ€āļ—āļĩāļĒāļšāļ„āđˆāļēāđƒāļŠāđ‰āļˆāđˆāļēāļĒāđƒāļ™āļāļēāļĢāļ‚āļ™āļŠāđˆāļ‡ āđ‚āļ”āļĒāđƒāļ™āļāļēāļĢāļĻāļķāļāļĐāļēāļ™āļĩāđ‰āđ„āļ”āđ‰āļ—āļģāļāļēāļĢāđ€āļāđ‡āļšāļĢāļ§āļšāļĢāļ§āļĄāļ‚āđ‰āļ­āļĄāļđāļĨāđƒāļ™āļĢāļ°āļŦāļ§āđˆāļēāļ‡āđ€āļ”āļ·āļ­āļ™āļĄāļāļĢāļēāļ„āļĄāļ–āļķāļ‡āļ˜āļąāļ™āļ§āļēāļ„āļĄ 2558 āļˆāļēāļāļĢāļ°āļšāļšāļāļēāļĢāļŠāļąāđˆāļ‡āļ‹āļ·āđ‰āļ­āļŠāļīāļ™āļ„āđ‰āļēāļ‚āļ­āļ‡āļĨāļđāļāļ„āđ‰āļēāđāļĨāļ°āļāļēāļĢāļ‚āļ™āļŠāđˆāļ‡āļ‚āļ­āļ‡āļšāļĢāļīāļĐāļąāļ—āļˆāļģāļŦāļ™āđˆāļēāļĒāļ§āļąāļŠāļ”āļļāļāđˆāļ­āļŠāļĢāđ‰āļēāļ‡āđāļŦāđˆāļ‡āļŦāļ™āļķāđˆāļ‡āđƒāļ™āļˆāļąāļ‡āļŦāļ§āļąāļ”āļ™āļ„āļĢāļĢāļēāļŠāļŠāļĩāļĄāļē āđāļĨāļ°āđ„āļ”āđ‰āļ™āļģāđ€āļŠāļ™āļ­āļ­āļąāļĨāļāļ­āļĢāļĩāļ—āļķāļĄāļ—āļĩāđˆāļžāļąāļ’āļ™āļēāļĄāļēāļˆāļēāļāļ§āļīāļ˜āļĩāļāļēāļĢāđāļšāļšāļ›āļĢāļ°āļŦāļĒāļąāļ” (Saving Algorithm) āđāļĨāļ°āļ§āļīāļ˜āļĩāļāļēāļĢāđ€āļŠāļīāļ‡āļžāļąāļ™āļ˜āļļāļāļĢāļĢāļĄ (Genetic Algorithm) āļ‹āļķāđˆāļ‡āđ€āļ›āđ‡āļ™āļ§āļīāļ˜āļĩāļāļēāļĢāļ—āļĩāđˆāđ€āļŦāļĄāļēāļ°āļŠāļĄāļŠāļģāļŦāļĢāļąāļšāļāļēāļĢāļˆāļąāļ”āđ€āļŠāđ‰āļ™āļ—āļēāļ‡āļ—āļĩāđˆāļĄāļĩāļŦāļĨāļēāļāļŦāļĨāļēāļĒāđ€āļŠāđ‰āļ™āļ—āļēāļ‡āļˆāļēāļāļāļēāļĢāļĻāļķāļāļĐāļēāļžāļšāļ§āđˆāļēāđ€āļŠāđ‰āļ™āļ—āļēāļ‡āđ€āļ”āļīāļ™āļĢāļ–āļ—āļĩāđˆāļˆāļąāļ”āđ‚āļ”āļĒāļ§āļīāļ˜āļĩāļāļēāļĢāđ€āļŠāļīāļ‡āļžāļąāļ™āļ˜āļļāļāļĢāļĢāļĄāļĄāļĩāļĢāļ°āļĒāļ°āļ—āļēāļ‡āļāļēāļĢāļ‚āļ™āļŠāđˆāļ‡āļ™āđ‰āļ­āļĒāļāļ§āđˆāļēāļ§āļīāļ˜āļĩāļāļēāļĢāļ”āļģāđ€āļ™āļīāļ™āļāļēāļĢāđƒāļ™āļ›āļąāļˆāļˆāļļāļšāļąāļ™āļ–āļķāļ‡ 32.99% āđāļĨāļ°āļĄāļĩāļ„āđˆāļēāđƒāļŠāđ‰āļˆāđˆāļēāļĒāļ—āļĩāđˆāļĨāļ”āļĨāļ‡āđ„āļ›āđ„āļ”āđ‰āļ–āļķāļ‡ 45.23% āļ”āļąāļ‡āļ™āļąāđ‰āļ™āļ§āļīāļ˜āļĩāļāļēāļĢāđ€āļŠāļīāļ‡āļžāļąāļ™āļ˜āļļāļāļĢāļĢāļĄāļŠāļēāļĄāļēāļĢāļ–āļˆāļąāļ”āđ€āļŠāđ‰āļ™āļ—āļēāļ‡āļāļēāļĢāļ‚āļ™āļŠāđˆāļ‡āđ„āļ”āđ‰āļ­āļĒāđˆāļēāļ‡āļĄāļĩāļ›āļĢāļ°āļŠāļīāļ—āļ˜āļīāļ āļēāļžāļŠāļģāļŦāļĢāļąāļšāļ›āļąāļāļŦāļēāļ”āļąāļ‡āļāļĨāđˆāļēāļ§āļ™āļĩāđ‰This 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

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    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?

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    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
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