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Author: search.filters.author.Hirunwat, Anucha

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    Optimizing maintenance responsibility distribution in real estate management: A complexity-driven approach for sustainable efficiency
    (2024-03-01)
    Aungkulanon, Pasura
    ;
    Hirunwat, Anucha
    ;
    Atthirawong, Walailak
    ;
    Phimsing, Kulanid
    ;
    Chanhom, Sirintra
    Efficient route management is critical for optimizing maintenance activities in real estate management. This study delves into the intricate task of allocating maintenance duties among building surveyors, a pivotal concern for property management firms. The primary goal of route planning is to enhance operational efficiency by minimizing travel time. To achieve this, the study explores three distinct algorithms: Saving, Nearest Neighbor, and an Evolutionary Algorithm (EA) customized with Dual Response Surface Optimization (DRSO). The integration of DRSO and EA enhances adaptability, allowing for dynamic responses to changes and improved allocation of maintenance tasks. Practical limitations, such as time and capacity, are considered through a case study involving a well-established building management organization. Results indicate that the Nearest Neighbor Algorithm generates 16–18 routes, the Saving Algorithm produces 18 routes, and the DRSO-driven EA also yields 18 routes. Significantly, the DRSO-driven EA consistently outperforms traditional methods, achieving a remarkable 17.7% reduction in route distance and an 18.8% reduction in journey time. In specific scenarios with 50 and 80 locations in Northeast and Central Bangkok, the DRSO-driven EA demonstrates practicality and efficacy. The algorithm's ability to address real-world challenges is underscored by these examples, showcasing its potential for broader implementation in real estate management. This study contributes significantly to the optimization of maintenance routes, presenting a clear roadmap for enhancing operational efficiency and customer satisfaction. Furthermore, it addresses the challenges posed by capitalism's growth constraints, offering insights for the adoption of sustainable economic practices in management, economics, and engineering.
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    Forecasting Commercial Vehicle Demand Using a Multiple Linear Regression Model
    (2023-01-01)
    Aungkulanon, Pasura
    ;
    Hirunwat, Anucha
    ;
    Atthirawong, Walailak
    ;
    Luangpaiboon, Pongchanun
    The purpose of this study was to develop predictive equations and explore the elements that are having an effect on the demand for commercial vehicles in Thailand. The Consumer Price Index (CPI), the Business Sentiment Index (BSI), the price of diesel fuel, and the desire for electric cars were all factors that were considered while compiling this information from a database of newly registered automobiles. Multiple regression analysis, consisting of a linear model and a quadratic model, was utilized to investigate how the presence of a variety of influences influenced the demand for commercial cars. Both the Consumer Price Index and the relationship between the CPI and diesel fuel costs were shown to have a considerable impact on the demand for commercial vehicles. This was the case regardless of which factor was considered first. It was determined that there was a connection between these two parameters of 74.96% after doing the necessary calculations.