Chaikajonwat, Thanrada
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Item type:Publication, Modified K-Means Clustering for Demand-Weighted Locations: A Thailand’s Convenience Store Franchise-Case Study(2023-03-01); This research applies and modifies K-means clustering analysis from Data Mining to solving the location problem. First, a case study of Thailand’s convenience store franchise in locating distribution centers (DCs) is conducted. Then, the final centroids are served at suggested DC locations. Besides the typical distance, Euclidean, used in K-means, Manhattan, and Chebyshev, is also experimented with. Moreover, due to the stores’ different demands, a modification of the centroid calculation is needed to reflect the center-of-gravity effects. For the proposed centroid calculation, the above three distance metrics incorporating the demands as weights give rise to another three approaches and are thus named Weighted Euclidean, Weighted Manhattan, and Weighted Chebyshev, respectively. Besides the optimal locations, the effectiveness of these six clustering approaches is measured by the expected total distribution cost from DCs to their served stores and the expected Davies– Bouldin index (DBI). Concurrently, the efficiency is measured by the expected number of iterations to the final clusters. All these six clustering approaches are then implemented in the case study of locating eight DCs to distribute to 260 convenience stores in Eastern Thailand. The results show that though all approaches yield locations in close proximity, the Weighted Chebyshev is the most effective one having both the lowest expected distribution cost and lowest expected DBI. In contrast, Euclidean is the most efficient approach, with the lowest expected number of iterations to the final clusters, followed by Weighted Chebyshev. Therefore, the DC locations from Weighted Chebyshev could, ultimately, be chosen for this Thailand’s convenience store franchise. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparative Evaluation of Event-Based Forecasting Models for Thai Airport Passenger Traffic(2026-02-01); Accurate passenger traffic forecasting is vital for strategic planning in Thailand’s aviation industry. This study forecasts the monthly total number of passengers at Suvarnabhumi (BKK), Don Mueang (DMK), Chiang Mai (CNX), and Phuket (HKT) airports using data from 2017 to 2024. The dataset was partitioned into training (January 2017–December 2023) and testing (January–December 2024) sets. Six methods were compared: Single Exponential Smoothing, Holt’s, Holt’s with Events Adjustment, Holt–Winters Multiplicative, TBATS model, and Box–Jenkins. Performance was evaluated using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results indicate that the optimal forecasting method varies by airport characteristics. Holt’s Method with Events Adjustment, which incorporates major disruptions such as the COVID-19 pandemic, produced the most accurate forecasts for BKK and DMK by effectively capturing external shocks. In contrast, the Holt–Winters Multiplicative method performed best for CNX and HKT, reflecting strong seasonal patterns typically driven by tourism activities in these destinations. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Use of Spline Techniques in the Nonparametric Regression Analysis for the Sequence Data with a Random Walk Process(2025-01-01); This study evaluates and compares various spline techniques in the nonparametric regression analysis, specifically focusing on the smoothing spline regression, the natural spline regression, the B-spline regression, and the penalized spline regression. The dependent variable in this analysis is time series data generated by a random walk process, while the independent variable is represented as sequential data. The simulation data, derived from a random walk process with diverse variances and sample sizes, ensures an absence of fixed patterns in the variable's changes. In addition, real-world data from the monthly trading volume of the SET (Stock Exchange of Thailand) index is used for practical application. The criterion for model efficiency estimation is based on minimizing the average mean square error for the simulation and SET index data. At the same time, predictive performance for future values is assessed through the minimum of average mean absolute percentage error. Among the models tested, the natural spline regression achieved the minimum average mean square error in all simulations due to SET index data estimation, excelling in model fit. However, the B-spline regression proved highly effective for forecasting future values. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Event Forecasting for Thailand’s Car Sales during the COVID-19 Pandemic(2022-07-01); The COVID-19 pandemic that started in 2020 has affected Thailand’s automotive industry, among many others. During the several stages of the pandemic period, car sales figures fluctuate, and hence are difficult to fit and forecast. Due to the trend present in the sales data, the Holt’s forecasting method appears a reasonable choice. However, the pandemic, or in a more general term, the “event”, requires a subtle method to handle this extra event component. This research proposes a forecasting method based on Holt’s method to better suit the time-series data affected by large-scale events. In addition, when combined with seasonality adjustment, three modified Holt’s-based methods are proposed and implemented on Thailand’s monthly car sales covering the pandemic period. Different flags are carefully assigned to each of the sales data to represent different stages of the pandemic. The results show that Holt’s method with seasonality and events yields the lowest MAPE of 8.64%, followed by 9.47% of Holt’s method with events. Compared to the typical Holt’s MAPE of 16.27%, the proposed methods are proved strongly effective for time-series data containing the event component.