Laokhongthavorn, Laemthong
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Preferred name
Laokhongthavorn, Laemthong
Alternative Name
Laokhongthavorn, L.
Laokhongthavorn, Leamthong
Main Affiliation
Email
laemthong.la@kmitl.ac.th
3 results
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Item type:Publication, LNG Portfolio Diversification Optimization Model of Thailand(2023-01-01); With the increase of reliance of LNG for Thailand, tons of liquefied natural gas (LNG) are imported, however there is no report that describes the engagement by a mathematical model. This study constructed a mathematical model to analyze LNG supply of Thailand. Based on the outcome in 2022, the offered LNG supply pattern, which reduced the total LNG price, risk of politics and risk of maritime, exhibited that Malaysia, Brunei, and Australia are LNG exporter of Thailand. Additionally, when the demand of LNG in Thailand increase, the demand from Qatar does not increase due to the low competitiveness. Moreover, in the study of diversification, the details suggested the raking of suppliers that Thailand should import. The rank is as follows: Brunei, Malaysia, Australia, USA, and Trinidad & Tobacco. The study of the objective factors on the decision of Thailand shows that the risk associated with shipping is ineffective to Thailand’s LNG supply due to the geographical closeness of the supplier countries. However, the risk connected to the political of supplier nations is influenced by the decision of the supply. Furthermore, this study revealed the limitation of the research associated with the LNG supply of Thailand. Hence, the finding of this study not only presented the alternative policy of Thailand but also consolidated Thailand’s energy security. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System(2021-09-01); ; ; ; Suwannatrai, ArkomIn this paper, we propose a timetable optimizer (TO) consisting of the following parts: 1) Demand Forecasting Module 2) Train Optimization Module 3) Timetable Generator Module. TO is a specialized system for planning the train timetable which designed to help solve the problem of determining the number of trains that are suitable and scheduling the train suitably according to the number of trains designated by the system. TO integrates the passenger data from the latest round trip or historical information and forecasts a number of passengers of high-speed trains based on historical data in order to find the optimal number of trains by using a mixed integer programing model. Lastly, the TO system can applies the number of trains to calculate the appropriate train schedule so that the planning cycle is complete. A case study of high-speed railway system in Thailand with 36 trains to satisfy demand of 35,000 passengers/ each direction is conducted. The results show that the proposed system can quickly generate a timetable having an optimal number of train with suitable time interval according to a demand forecasting. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Study on High-Speed Rail Pricing Strategy for Thailand Based on Dynamic Optimal Pricing Model(2021-06-30); ; ; ; Moryadee, SeksunThe Bangkok–Nong Khai high-speed railway is the first high-speed line in Thailand and is currently under construction. The project is due to be completed by 2023, with ticket prices starting at 80 baht plus 1.8 baht per km which can be considered as a straight-line fare set with a fixed rate increase. Different from the current price policy, this paper examines the application of dynamic pricing to Thailand’s HSR. Dynamic Pricing Optimizer proposed in this paper is a unique system of dynamic pricing by considering the changes in the amount of service requirements and the time of purchasing tickets designed to solve the problem of pricing that is appropriate. First, The Dynamic Pricing Optimizer (DPO) system will integrate the user data from the latest trip or historical information. After that, there will be a forecast for users of high-speed trains based on historical data in order to create a demand function that is linear. After that, the price will be determined by the optimization method to find the most suitable price in order to maximize the revenue. Lastly, we compare both fare policies and found that The Dynamic Pricing policy can increase revenue up to 6.5%.