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Author: search.filters.author.Moryadee, SeksunSubject: search.filters.subject.Optimization

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    LNG Portfolio Diversification Optimization Model of Thailand
    (2023-01-01)
    Tuchinda, Nutmethee
    ;
    Moryadee, Seksun
    ;
    U-Tapao, Chalida
    ;
    Laokhongthavorn, Leamthong
    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.
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    Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System
    (2021-09-01)
    Khwanpruk, Somkiat
    ;
    U-tapao, Chalida
    ;
    Khwanpruk, Kankanit
    ;
    Laokhongthavorn, Laemthong
    ;
    Suwannatrai, Arkom
    In 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.