Tatcha Sudtasan

Optimal Combination of Energy Sources for Electricity Generation in Thailand with Lessons from Japan Using Maximum Entropy

This study uses maximum entropy method to find an optimal combination of energy sources for electricity generation in Thailand. It sets three targets including unit cost, risk and pollution. In the optimization process, it forms three constraints according to these three targets. It solves the system following the guideline of Golan, Judge and Miller (1996). It analyses six scenarios of the targets. For the major results, it finds that hydropower, nuclear, wind and solar energy are major sources of electricity generation. The country cannot avoid adopting nuclear energy for its electricity generation in order to meet all the three targets that are optimal for its electricity generation and economic development.

Making Profit in Stock Investment Before XD Dates by Using Genetic Algorithm

This study extends the work of Sudtasan (Int. J. Intell. Techn. Appl. Stat. 5, 143–155, 2012) to apply genetic algorithm to detect regime switching of eight stock prices before XD dates in the stock exchange of Thailand during 2005–2011. It reveals that regime switching does exist before XD dates only in the first half of the year. The study successfully discovers that ADVANC and PTT are good for short-term investment. CPALL and SCC are appropriate for medium-term investment. CPF, IVL, KBANK, and TCAP are potential for the long-term investment. Average buying days for all stocks are around 31 days before the XD dates. Rates of return of the investment in the first half of the year are higher than in the second half. Average annual rate of return is around 76 %. Technically, genetic algorithm without mutation performs better than a model with mutation. For the performance of the best genetic algorithm, a model with zero mutation rate that is applied to the data in the first half of the year can extract around 62 % of the highest potential profit.

Cellular Automata Simulation of Spatial Promotion Strategies for Organic Agriculture

This study aims at finding efficient promotion strategies of organic agriculture in a spatial dimension using cellular automata simulation. The simulation is based on the decision making of farmers influenced by the decisions of their neighboring farmers. Farmers who do chemical agriculture will switch to organic agriculture following their neighbors who majorly do organic agriculture. The behavior can turn into the opposite direction when they are surrounded by neighbors who majorly do chemical agriculture. The study varies the spatial strategies from clustering, random dispersion, patterned dispersion and patterned deployment. The results discover that the promotion should be spread countrywide and be patterned. Clustering is the worst strategy that cannot convert chemical farms into the organic ones. The best pattern is the patterned deployment that spread to cover all parts of the area. This strategy can induce the surrounded farmers to switch their farms into organic farming more than 3 times of the initial deployment areas. The paper may assist planners and policy makers related to the promotion of organic agriculture to find appropriate spatial promotion strategies.

Simulation Using Fighter Ants Algorithm with an Application on Efficient Team Building

This paper compares the performance of two teams with the same average skill but different diversities. It constructs the Fighter Ants Algorithm to mimic the fighting between two groups of ants, the black and red ants. The battle matches individual ants from both sides, compares their strengths and makes only the stronger survives. The winning side is determined by the last-man-standing rule. There are five major results. First, the group with more diversity naturally wins. Second, skill upgrades of the loser side turn the side to be the winner even after a slight improvement of the strength. Third, the addition of some experts with the strongest strength will be able to help the side to be the winner. Fourth, skill improvement of the whole team benefits the organization more than hiring experts. The results will guide an organization to choose a suitable strategy to recruit and develop its human resource in order to improve the competitive performance of the organization.