The purpose of this study is to address two major issues: (1) the spread of epidemics such as COVID-19 due to long waiting times caused by a large number of waiting for customers, and (2) excessive energy consumption resulting from the elevator patterns used by various customers. The first issue is addressed through the development of a mobile application, while the second issue is tackled by implementing two strategies: (1) determining optimal stopping strategies for elevators based on registered passengers and (2) assigning passengers to elevators in a way that minimizes the number of floors the elevators need to stop at. The mobile application serves as an input parameter for the optimization toolbox, which employs the exact method and multi-objective variable neighborhood strategy adaptive search (M-VaNSAS) to find the optimal plan for passenger assignment and elevator scheduling. The proposed method, which adopts an even-odd floor strategy, outperforms the currently practiced procedure and leads to a 42.44% reduction in waiting time and a 29.61% reduction in energy consumption. Computational results confirmed the effectiveness of the proposed approach.

This paper presented three types of models for forecasting the supply and demand of Thai ethanol, so called MR, ANN, and MR-ANN models. MR models were formulated using stepwise multiple regression analysis, which were statistically significant. However, MR models provided low performance in forecasting. ANN models were constructed using artificial neural networks, which provided satisfactory results. Moreover, the third type of models was an integration of multiple regression analysis and artificial neural networks. In MR-ANN models, influential factors from stepwise multiple regression, were taken as inputs for artificial neural networks. The integrated models provided a fair results comparing to the first two types of models. In summary, ANN models provided the lowest MAPE and the highest R² indicating that the models were the most appropriate among the three types of models. ANN models are therefore recommended to forecast the supply and demand of Thai ethanol.

This paper aims to analyze ethanol supply chain in Thailand. The analysis covered three main parts: supply chain structure, material flow and related costs in supply chain. The results showed that Thai ethanol supply chain is complex because it involves many stakeholders in both public and private agencies. Molasses and cassava are the main feedstocks of ethanol production in Thailand, so the upstream suppliers of ethanol supply chain are sugarcane and cassava farmers, and the collectors. The feedstocks cost of ethanol plants is high due to the fluctuation of sugarcane and cassava price. There is also a trade barrier to control both production and transportation of ethanol because it under the Liquor Act. Moreover, the distribution of ethanol has low efficiency and long distance of truck transport contribute to high transportation cost. Consequently, logistics system of Thai ethanol supply chain should be improved more efficient in order to support demand expansion of ethanol industry.

This paper aims to model the ethanol supply chain in Thailand. A mathematical model for the plants-to-customers supply chain was developed with the objective to minimize the total cost of supply chain. The model solution provided data for decision-making of supply chain stakeholder, particularly by the ethanol manufacturers and petroleum traders. These data can help ethanol manufacturers make appropriate decisions for monthly materials procurement and inventory control, including ethanol production plans, sale, and transportation scheduling for suitable customers. The results from the sensitivity analysis indicated that molasses prices affect feedstocks procurement and ethanol production, and diesel prices also affect ethanol distribution. Therefore, the Thai government should promote increasing the cassava supply if the molasses price increases. In addition, transportation infrastructure of the country, particularly the railway system, should be developed to decrease truck transportation to reduce ethanol distribution costs.

The log-normal distribution is a commonly used parametric distribution applied in reliability and survival analysis. For two or more populations, various techniques have been developed to derive confidence intervals for functions of the population parameters under the assumptions of independence and linear dependence between the samples. We extend the existing log-normal confidence interval methodologies by utilizing a jackknifing procedure based on the inference for margins methodology under parametrically defined copula dependence structures. We assess the performance of the procedure on simulated data and apply the methodology to calculate confidence intervals for the mean maximal damage area caused by ground based tornadoes in Canada.

ประเทศไทยมีการขนส่งทางบกเป็นสัดส่วนสูงสุดเมื่อเทียบกับโหมดการขนส่งอื่น งานวิจัยนี้จึงมีวัตถุประสงค์เพื่อพัฒนาตัวชี้วัดประสิทธิภาพด้านโลจิสติกส์สำหรับผู้ประกอบการขนส่ง โดยเป็นการขนส่งภายในประเทศด้วยรถบรรทุก จากการทบทวนวรรณกรรมทางวิชาการด้านโลจิสติกส์และการขนส่ง รวมทั้งการศึกษาข้อกำหนดและกฎหมายว่าด้วยการขนส่งทางบก ผู้วิจัยจึงออกแบบและพัฒนาตัวชี้วัดทั้งหมด 23 ตัวชี้วัด แบ่งเป็น 3 ด้าน ได้แก่ ด้านปัจจัยนำเข้า 6 ตัวชี้วัด ด้านกระบวนการ 9 ตัวชี้วัด และด้านผลลัพธ์ 8 ตัวชี้วัด โดยมีทั้งตัวชี้วัดเชิงปริมาณซึ่งมีเกณฑ์การวัดค่าเป็นร้อยละ และตัวชี้วัดเชิงคุณภาพซึ่งมีเกณฑ์วัดค่าเป็นคะแนนแบบรูบริค ทั้งนี้แต่ละตัวชี้วัดถูกแบ่งการประเมินค่าออกเป็น 5 ระดับ และได้รับการตรวจสอบความเหมาะสมโดยผู้เชี่ยวชาญด้านโลจิสติกส์และการขนส่ง นอกจากนี้ยังถูกนำไปประยุกต์ใช้กับบริษัทผู้ให้บริการขนส่งด้วยรถบรรทุกซึ่งเป็นสมาชิกของสมาคมการค้าโลจิสติกส์ไทยแห่งอนุภูมิภาคลุ่มแม่น้ำโขง พบว่า...