Komgrit Leksakul

Tailoring of microstructure in hydrogenated nanocrystalline Si thin films by ICP-assisted RF magnetron sputtering

Utilizing plasma-assisted deposition by combining an RF magnetron and an inductively coupled plasma (ICP) source it is possible to fabricate highly crystallized nc-Si:H films at a relatively low substrate temperature (300 °C). Microstructural analysis reveals enhancement in crystallinity along with (2 2 0) preferential orientation throughout the depth of the film. The possible mechanism of crystallinity enhancement and preferential orientation is presented on the basis of plasma diagnostics using optical emission spectroscopy and various film analysis tools. This work also reports the effectiveness of the ICP source and elevated temperature for the control of film microstructure and crystallinity.

Value creation through collaborative supply chain: holistic performance enhancement road map

This paper proposes an integrated novel framework between B2B-SCM using data mining techniques such as K-Means based on particle swarm intelligence (particle swarm optimisation) and association rule. It constructs relationship rules of holistic performance enhancement road map. The data-set of relationships between enterprise and its direct customers of the case study organisations in France was used for demonstration. The experiment results show how domain managers powerfully utilise the graphical analysis results to provide the holistic performance improvement and weakness resolution relationship rules. In the long run, organisations are able to use this framework to design and adjust their units to conform the exact customer needs. This paper introduces and explains a new idea of measuring value added along the supply chain from a collaborative perspective. The extended model is adapted from our previous model and from balanced scorecard model. It provides a tool to measure tangible and intangible value between partners.

Forecast of off-season longan supply using fuzzy support vector regression and fuzzy artificial neural network

NN and SVR were discussed and proposed for forecasting of off-season longan supply.Reducing NN and SVR proposed for improving computational runtime.NN and SVR forecasting models were extended by fuzzy algorithms.Totally, six proposed models were compared in term of their efficiency.Multi regression was compared and over fitting was verified. An over-supply crisis in longans in northern Thailand adversely affected farmer income. Cultivating longans off-season was adapted as an alternative solution to this over-supply problem. However, lacking information management and analysis, over supply occurred even during the off-season, leading to a slump in the sale price. Supply forecasting plays an important role in solving this problem. To solve this problem, we proposed a systematic approach for off-season longan forecasting using neural network, fuzzy neural network, support vector regression and Fuzzy Support Vector Regression (FSVR). In addition, grid search was applied to each support vector model to find its optimum architecture. Real data sets were used to evaluate and compare the effectiveness and efficiency of the algorithms. The experimental results showed that FSVR was the most effective forecasting technique.

Nurse Scheduling Using Genetic Algorithm

This study applied engineering techniques to develop a nurse scheduling model that, while maintaining the highest level of service, simultaneously minimized hospital-staffing costs and equitably distributed overtime pay. In the mathematical model, the objective function was the sum of the overtime payment to all nurses and the standard deviation of the total overtime payment that each nurse received. Input data distributions were analyzed in order to formulate a simulation model to determine the optimal demand for nurses that met the hospital’s service standards. To obtain the optimal nurse schedule with the number of nurses acquired from the simulation model, we proposed a genetic algorithm (GA) with two-point crossover and random mutation. After running the algorithm, we compared the expenses and number of nurses between the existing and our proposed nurse schedules. For January 2013, the nurse schedule obtained by GA could save 12% in staffing expenses per month and 13% in number of nurses when compare with the existing schedule, while more equitably distributing overtime pay between all nurses.

Predictive performance model in collaborative supply chain using decision tree and clustering technique

This paper proposes an integrated framework between B2B supply chains (B2B-SC) and performance evaluation systems. This framework is based on data mining techniques, enabling the development of a predictive collaborative performance evolution model and decision making which has forward-looking collaborative capabilities. The results are deployment for collaborative performance guidelines, which were validated by the domain experts in terms of its real practical usage efficiency. This framework enables managers to develop systematic manners to predict future collaborative performance and recognize latent problems in their relationship. Its usages and difficulties were also discussed. Furthermore, the final predictive results and rules contain vital information relating to SC improvement in the long term.

Predictive performance measurement system for retail industry using neuro-fuzzy system based on swarm intelligence

Between 2011 and 2013, convenience store retail business grew dramatically in Thailand. As a result, most companies have increasingly been choosing the application of performance measurement systems. This significantly results in poor performance measurement regarding future business lagging measure. To solve this problem, this research presents a hybrid predictive performance measurement system (PPMS) using the neuro-fuzzy approach based on particle swarm optimization (ANFIS-PSO). It is constructed from many leading aspects of convenience store performance measures and projects the competitive level of future business lagging measure. To do so, monthly store performance measures were first congregated from the case study value chains. Second, data cleaning and preparations by headquarter accounting verification were carried out before the proposed model construction. Third, these results were used as the learning dataset to derive a predictive performance measurement system based on ANFIS-PSO. The fuzzy value of each leading input was optimized by parallel processing PSO, before feeding to the neuro-fuzzy system. Finally, the model provides a future performance for the next month’s sales and expense to managers who focused on managing a store using desirability function (

Wall angle control of reactive ion etched features on a silicon substrate

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Improving Mechanical Properties of Co-Cr-W Alloys by Powder Metallurgy

Di). It boosted the sales growth in 2012 by ten percentages using single PPMS. Additionally, the composite PPMS was also boosted by the same growth rate for the store in the blind test (July 2013–February 2014). From the experimental results, it can be concluded that ANFIS-PSO delivers high-accuracy modeling, delivering much smaller error and computational time compared to artificial neural network model and supports vector regression but its component searching time differs significantly because of the complexity of each model.