Leenawong, Chartchai

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Leenawong, Chatchai

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School of Science

chartchai.le@kmitl.ac.th

Now showing 1 - 10 of 13

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The effects of special events on regression for subcompact car sales in Thailand
(2016-11-01)
Rattanametawee, Witchaya
;
Leenawong, Chartchai
;
Netisopakul, Ponrudee
This research proposes a method to dealing with multiple linear regression that integrates the seasonality as well as the effects of some special or unanticipated events for sales figures. The method is then applied to the car sales figures in Thailand after having been through the 2011 national big flood and the 2011-2012 government’s initiative tax-incentive program for boosting the automobile industry. Besides Thailand’s Gross Domestic Products (GDP) and the 12-month Loan’s Interest Rate as explanatory variables, seasonal dummy variables along with the proposed special event variables and appropriate event tagging are incorporated. The statistical results obtained from the proposed regression model with seasons and events, compared to the models with neither seasons nor both yields highest adjusted coefficient of determination (R-squre) and accuracy (MAPE).
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Computational models for studying leadership modes
(2010-03-01)
Wattanasiripong, Nisakorn
;
Wang, Karen
;
Leenawong, Chartchai
Computational models for studying the effects of permanent and rotating leadership on the performances of teams that have full interaction among team members are proposed. In each model, the leader performs both leading roles and regular tasks as an ordinary team member. Permanent leadership refers to the situation when a team has only one leader for the entire time the team exists while rotating leadership refers to the situation when every team member rotates for the leader position. Computer simulations are used for examining the effects of the parameters in these models such as the amount of time the team exists for, the learning ability of a member, and the skill level of the leader on the expected team performance. © 2010 Pushpa Publishing House.
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Mathematical models for studying the value of motivational leadership in teams
(2005-05-01)
Solow, Daniel
;
Piderit, Sandy
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Burnetas, Apostolos
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Leenawong, Chartchai
Mathematical models are presented for studying the value of leadership in a team where the members interact with each other. The models are based on a leader's role of motivating each team member to perform closer to his/her maximum ability. These models include controllable parameters whose values reflect the amount of task interdependence among the workers as well as the motivational skill and variability in the skill of the leader. Confirming results - such as the fact that the skill level of the leader is a critical factor in the expected performance of the team - establish credibility in the models. Mathematical analysis and computer simulations are used to provide new managerial insights into the value of the leader - such as the fact that the skill of the leader can be more important than controlling the amount of interdependence among the team members and that having a choice of multiple leaders with no particular motivating skill is beneficial to the performance of small teams but not to large teams. © Springer Science + Business Media, Inc. 2005.
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Modified K-Means Clustering for Demand-Weighted Locations: A Thailand’s Convenience Store Franchise-Case Study
(2023-03-01)
Leenawong, Chartchai
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Chaikajonwat, Thanrada
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.
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Facebook Commerce Issues in Thailand and Precautionary Benchmarks
(2024-01-01)
Leenawong, Chatchai
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Watanapa, Bunthit
;
Suwichachanan, Pornvarat
This research proposes precautionary benchmarks for addressing Facebook purchasing issues. Motivated by the increasing reliance on e-commerce and the prevalence of online purchasing problems in Thailand, this study adopts qualitative methodologies. It begins with in-depth interviews to gather data from two distinct groups of buyers: “experienced” frequent buyers who have never encountered issues, and “inexperienced” buyers who have faced problems. With narrative analysis and analytical methods integrating the customer journey, decision-making process, and hierarchical decomposition, key insights and precautionary measures are identified. The findings indicate that experienced buyers tend to directly search for products, thoroughly investigate sellers, compare with other platforms, and prefer pre-payment methods. However, inexperienced buyers often respond to ads or direct sales without detailed checks, focus on low prices, and use both pre/post-payment methods. The hierarchical decomposition classifies Facebook purchasing issues into three main sub-issues: shipping delays, incorrect products/characteristics, and shipment absences, further categorized into seven causes. The synthesis from systematic variation and induction approaches yields preventive measures for buyers, such as verifying seller credibility, checking product details, and selecting secure payments. Additionally, the study offers suggestions to improve Facebook's system to enhance buyer confidence. The comprehensive knowledge from this research can be disseminated for public benefit.
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Event index computation for forecasting case study: Car sales in Thailand
(2020-12-01)
Rattanametawee, Witchaya
;
Leenawong, Chartchai
Due to the impact of special events, both positive and negative, on the sales data, the ordinary Time-series Decomposition (TSD) forecasting model cannot merely capture these effects, even with the added seasonality and trends. Therefore, in this research, a new method for computing the event indices, representing the unusual fluctuations for a certain period in the time series, is proposed in order for it to be incorporated into TSD, alongside the conventional trend, seasonal, and cyclical components. A case study of subcompact car sales monthly data in Thailand during the years 2011-2018 is examined as for that time period contains the 2011 nationwide big flood reflecting the negative impact, as well as the nation’s tax-incentive first-car buyer scheme reflecting the positive impact on the dataset. The mean absolute percentage error (MAPE) is used as an accuracy measure of the proposed forecasting model and it illustrates the promising results in the end.
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Fuel Costs Optimization for Long-Haul Flight with Refueling Layovers
(2024-01-01)
Leenawong, Chartchai
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Ritthipakdee, Amarita
This study introduced a mathematical model aimed at optimizing fuel costs for long-haul flights, particularly those requiring refueling. The primary objective was to minimize fuel expenses by considering key factors such as flight routes, aircraft types, refueling points, and refueling quantities. The proposed solution used a 0-1 mixed-integer linear programming (MILP) model, supported by auxiliary variables, to effectively manage the constraints of this optimization problem. The MILP model also considered differences in fuel costs at refueling points, including the departure airport. For validation, a case study was conducted involving a long-haul flight from airport AAA to DDD, with refueling options at airports BBB and CCC. The model effectively determined the most economical flight route, assessed the necessity of refueling, and calculated the required fuel amounts at each refueling location. In summary, this study demonstrated that the proposed model could successfully address the challenges of optimizing fuel costs in long-haul flight.
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Choices of interacting positions on multiple team assembly
(2007-01-01)
Leenawong, Chartchai
;
Wattanasiripong, Nisakorn
This paper proposes a new method for choosing interacting positions that affect team performance on multiple team assembly in an organization. Various approaches for replacing team members are also reviewed and adjusted so that the resulting team obtained will be as effective and efficient as possible. This multiple team assembly is a combinatorial optimization problem that focuses on examining complexity in an organization. The objective of the problem is to achieve maximum performance of the team while at the same time trying to reduce the expected number of replacements and the expected number of trials needed to arrive at that performance level. Computer simulation is used to implement and demonstrate the proposed ideas. © Springer-Verlag Berlin Heidelberg 2007.
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Decision Support Model and Software for Consolidated Order Assignment to Delivery Trucks
(2017-11-15)
Leenawong, Chartchai
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Wattanawalun, Champ
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Wongsa, Kittipish
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Lethaisong, Karnkanok
In this research, a decision model for assisting in loading consolidated orders onto delivery trucks to distribute to scattered customers is constructed. Each truck can have more than one drop-off location. The case-study company based in Bangkok produces bubble wraps that can simply be stacked up on top of the companys small trucks. Most customer orders are less than truck-load. Thus, consolidating orders into full trucks according to customer locations is necessary to ensure minimum transportation and in-transit inventory carrying costs. 0-1 integer programming is used in the model construction. Also, a decision support system for dealing with this problem is developed for ease of use to logistics managers and planners.
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Multiple linear regression using gradient descent: A case study on Thailand car sales
(2017-01-01)
Netisopakul, Ponrudee
;
Leenawong, Chartchai
Severe fluctuations in Thailand car sales had enormous impacts on the automobile and related industries. A reliable forecasting model is needed to accurately forecast the car sales for the next production batch. Using ten-year car sales data, this research proposes a machine learning approach using gradient descent (GD) to fitting multiple linear regression for Thailand car sales forecasts. The resulted forecasting accuracy is then compared with that of a normal equation method (NE) as well as that obtained from a statistical package (SP). First, two independent variables (2IVs): Thailand’s Gross Domestic Product and the 12-month Loan Rate are used in the proposed models. Then, dummy seasonal variables (Season) are added to the regression equations. Finally, dummy event flag variables (Event) are added. Totally, five sets of experiments are conducted. The experiment results show that NE produces the same regression equations as SP. Both GD and NE methods yield exactly the same results for 2IVs, but GD yields slightly less prediction accuracy than NE’s in Season and Event experiments. This research concludes that gradient descent has comparable forecasting accuracy to those from other methods. Nevertheless, when the regression contains dummy variables, caution is recommended.

Leenawong, Chartchai

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