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    Optimization-Based Train Timetables Generation with Demand Forecasting for Thailand High Speed Rail System
    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.
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    A Study on High-Speed Rail Pricing Strategy for Thailand Based on Dynamic Optimal Pricing Model
    The 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%.
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    A complementarity-based equilibrium model of biodiesel market, in Thailand
    This paper presents a complementarity-based model making a decision for energy production in biodiesel market. This model has three strategic players complete against each other on the biodiesel productions. All three players make up biodiesel from agricultural products and they are price-takers. The first player represents producers who produce and sell biodiesel from palm oil. The second and third players produce and sell biodiesel from coconut oil and soybean oil, respectively. The strategic player's decisions involve quantities of palm, coconut and soybean oil related with the market price. This paper uses operation research to optimize a decision maker's objectives within the limits of available agricultural products and also provide results for sustainable energy production.
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    Potential biodiesel production from palm oil, coconut oil and soybean oil for Thailand
    The government began subsidizing the use of B20 in large trucks on a voluntary basis beginning in 2016 and intends to implement the B10 requirement in 2018 for all diesel sales. However, policy makers in both the MOE and the Ministry of Agriculture and Cooperatives (MOAC) recently agreed that the mandatory biodiesel consumption plan for 2036 may be unattainable (given the strategy does not permit reliance on imports) and is therefore being reexamined. Despite an increase in harvested area, crude palm oil (CPO) production, the only feedstock used for biodiesel in Thailand, stagnated at 1.8-2.0 million from 2014-2016 due to unfavourable weather conditions. Thailand experiences a great economic and industrial development and is the second largest energy consumer in South East Asia. Being a net oil importer, Thai government has declared a renewable energy development programme in order to secure sustainable development and energy security. Thailand spends more than 10% of GDP for energy imports and transport sector accounts for 36% of total final energy consumption of which 50% is diesel. Diesel marks a huge impact on Thai economy.