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Enhancing Novel Clean Room Learning Metaheuristic Algorithm on Noisy Response Surfaces: Parameter Design through Dual Response Optimization
(2024-05-24)
Atthirawong, Walailak
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Aungkulanon, Pasura
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Tangsomboon, Ratchakrit
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Chadchawansin, Sureerat
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Luangpaiboon, Pongchanun
Metaheuristics, such as the clean room learning approach, are utilized in order to tackle complex optimization issues. The potential hindrance of performance might arise from the existence of response surfaces that exhibit noise, which is manifested as random disturbances introduced during the evaluation of objective functions. The objective of this work is to determine algorithm parameters by employing dual response optimization approaches. The optimization of dual response entails the concurrent assessment of both the estimated mean difference from the target and the standard deviation. The program adeptly oversees the distribution of resources, skillfully balancing the pursuit of research in uncharted domains and capitalizing on the insights derived from analyzing flawed information. The effectiveness of the method has been shown by experiments done on benchmark functions with different levels of noise. The technique discussed above demonstrates higher performance when compared to fixed-parameter competitors in terms of its capacity to withstand noise, achieve high precision, and maintain stability during the process of searching for optimal solutions. Our study is centered around the examination of the effects of different forms of noise on algorithmic performance, alongside the determination of the most favorable parameter values for different levels of noise. The current research highlights the lack of structure inherent in the algorithm. This paper gives a comprehensive investigation into the optimization of dual responses in the presence of surface noise, with the objective of improving the efficacy of metaheuristic algorithms employed in clean room learning. The technique presented in this study aims to tackle the issue of noise in the objective function, which ultimately results in improved optimization results. The findings of this research study provide a significant contribution to the advancement of metaheuristic optimization approaches and has extensive applicability.
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Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection
(2024-05-01)
Aungkulanon, Pasura
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Atthirawong, Walailak
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Luangpaiboon, Pongchanun
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Chanpuypetch, Wirachchaya
Globalization and multinational commerce have increased the dynamism and complexity of supply networks, thereby increasing their susceptibility to disruptions along interconnected supply chains. This study aims to tackle the significant concern of supplier selection disruptions in the Thai agri-food industry as a response to the aforementioned challenges. A novel supplier evaluation system, PROMETHEE II, is suggested; it combines the Fuzzy Analytical Hierarchy Process (FAHP) with inferential statistical techniques. This investigation commences with the identification of critical indicators of risk in the sustainable supply chain via three phases of analysis and 315 surveys of management teams. Exploratory factor analysis (EFA) is utilized to ascertain six supply risk criteria and twenty-three sub-criteria. Following this, the parameters are prioritized by FAHP, whereas four prospective suppliers for an agricultural firm are assessed by PROMETHEE II. By integrating optimization techniques into sensitivity analysis, this hybrid approach improves supplier selection criteria by identifying dependable solutions that are customized to risk scenarios and business objectives. The iterative strategy enhances the resilience of the agri-food supply chain by enabling well-informed decision-making amidst evolving market dynamics and chain risks. In addition, this research helps agricultural and other sectors by providing a systematic approach to selecting low-risk suppliers and delineating critical supply chain risk factors. By bridging complexity and facilitating informed decision-making in supplier selection processes, the results of this study fill a significant void in the academic literature concerning sustainable supply chain risk management.
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Driving Educational Excellence: A Data Envelopment Analysis Study for Decision-Making Enhancement
(2024-04-01)
Luangpaiboon, Pongchanun
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Phinkrathok, Chiramet
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Atthirawong, Walailak
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Aungkulanon, Pasura
The education faculty aims to assess departmental effectiveness by analyzing the relationship between service levels, output variables, and input variables. This objective is coupled with the formulation of faculty development strategies tailored to enhance efficiency while accommodating individual professionals’ unique requirements and aspirations. Furthermore, beyond the annual University Evaluation System, faculty members conduct thorough evaluations to identify discrepancies in departmental reports detailing service levels and input components (factors of production) over a three-year period. Through descriptive statistics and hypothesis testing, reliable data guides the decision-making process regarding the optimal level for data envelopment analysis (DEA). The study employs four simulated scenarios to evaluate the overall performance of five departments using DEA. Findings reveal varying efficacy ratings across departments, with the Department of Health and Physical Education achieving a moderate rating and the Educational Technology Department exhibiting the lowest efficacy. Conversely, departments such as Art Education, Music Education, and Business Education showcase exemplary efficacy levels. Insights gleaned from quantitative analysis and questionnaires contribute significantly to faculty development, enhancing knowledge, competencies, values, attitudes, and future endeavors. Recommendations provided advocate for the widespread implementation of efficacious approaches to bolster faculty efficacy. Moreover, the study underscores the potential to enhance the efficiency and quality of service across faculties through the adoption of best practices and initiatives.
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Optimizing maintenance responsibility distribution in real estate management: A complexity-driven approach for sustainable efficiency
(2024-03-01)
Aungkulanon, Pasura
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Hirunwat, Anucha
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Atthirawong, Walailak
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Phimsing, Kulanid
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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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Fuzzy Techniques and Adjusted Mixture Design-Based Scenario Analysis in the CLMV (Cambodia, Lao PDR, Myanmar and Vietnam) Subregion for Multi-Criteria Decision Making in the Apparel Industry
(2023-12-01)
Aungkulanon, Pasura
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Atthirawong, Walailak
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Sangmanee, Woranat
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Luangpaiboon, Pongchanun
This research paper presents an all-encompassing methodology for multi-criteria decision-making in the apparel sector, with the particular objective of aiding in the determination of the most appropriate location within the CLMV subregion. The research is conducted in three crucial stages. The process began with the administration of a survey to proprietors of garment businesses in both Thailand and the CLMV countries. This survey resulted in the compilation of an exhaustive list of site-selection criteria and sub-criteria. Based on the findings of subject matter-expert interviews, Cambodia (C), Vietnam (V), and Myanmar (M) were identified as feasible alternatives. Subsequently, the questionnaire criteria and sub-criteria were evaluated utilizing the Fuzzy Analytic Network Process (Fuzzy ANP), which involved the utilization of meticulously designed pair-wise comparison matrices and local priorities. Five specialists from the Thai entrepreneurial community affirmed the effectiveness of Fuzzy ANP and expressed interest in expanding manufacturing operations in the CLMV subregion. The optimal location for Thai apparel manufacturers was subsequently determined using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). The results indicated that Vietnam is the most favorable option. In order to improve the dependability of results, an amended mixture-design scenario analysis was implemented. This analysis assessed the sensitivity and dependability of the proposed model in different scenarios, ensuring its applicability in real-world situations. In contrast to traditional models, this study integrates managerial judgments and preferences into the decision-making procedure, thereby accounting for the complex interdependencies among numerous criteria. The suggested methodology functions as a beneficial instrument for decision-makers, both domestic and international, as it integrates effortlessly into the organizational structure of the CLMV region. By harmonizing objectives pertaining to data acquisition, manipulation, retention, and dissemination, this framework not only enables enhanced decision-making processes, but also optimizes system efficiency.
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Fuzzy Analytical Hierarchy Process for Strategic Decision Making in Electric Vehicle Adoption
(2023-04-01)
Aungkulanon, Pasura
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Atthirawong, Walailak
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Luangpaiboon, Pongchanun
In response to the requirement to address the global climate crisis in urban areas caused by the logistics sector, an increasing number of governments around the world have begun aggressive strategic actions to encourage manufacturers and consumers to adopt electric vehicle (EV) technology. One of the most beneficial aspects of driving an EV is that it reduces pollution while also reducing the use of fossil fuels, as well as improving public health by improving local air quality. Nevertheless, the level of EV adoption differs significantly across markets and geographies. EV adoption barriers slow the overall rate of electric mobility. This study ranks a list of obstacles and sub-hindrances to the adoption of electric vehicles in Thailand using the Fuzzy Analytical Hierarchy Process (FAHP), a Multi-Criteria Decision Making (MCDM) technique. The results showed that infrastructure policy barrier (A), which had the highest weight of 0.6058, was the biggest barrier to EV adoption, followed by technological barrier (B) with a weight of 0.2657, and then by market barrier with a weight of 0.1285. Insufficient charging infrastructure network (A3), lack of proper government support/incentives and collaboration (A1), insufficient electric power supply (A2), high capital cost (C3), and EV charging time (B3) were key sub-barriers to EV adoption in Thailand. Decision Making Systems (DMS) have additionally been created to assist executives in making decisions about the aforementioned barriers. The DMS is based on the concept of computer-aided decision making in that it allows for direct user interaction, analysis, and the ability to change circumstances and the decision-making process based on the executives’ own experience and abilities. Thus, the findings of this study aid in the formulation of market strategies for relevant stakeholders and shed light on potential policy responses.
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Estimation of CNC Machining Parameter Levels for Brass Union Using an Adaptive Constrained Response Surface Optimization Model
(2023-01-01)
Luangpaiboon, Pongchanun
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Kantaputra, Napatchya
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Chongsawad, Natchira
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Aungkulanon, Pasura
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Ruekkasaem, Lakkana
The selection of appropriate levels of machining parameters is an important consideration that determines machinability or other quality measures. In this study, the CNC machining process was designed to optimize the effects of machining parameters such as feed rate, spindle speed, and tool life in the production of brass unions, which are commonly used in air conditioners. The numerical design was carried out using a proposed adaptive constrained response surface optimization model (ACRSOM). The ACRSOM's evolutionary operations began with the conventional factorial design, which was used to identify the influential effects of the main and some selected parameter interactions on transformed proportion responses. The first phase was used to move quickly toward the optimum with adequate design points, whereas the second phase was used to minimize the standard deviation of transformed responses under the desired mean target. The ACRSOM was generated in linear or nonlinear forms, based on either unreplicated or replicated designed plans, which were then combined to generate the new operating condition. With the optimal setting of 0.08 feed rate; 2,300 spindle speed and 10,000 tool life obtained from the proposed model, the percentage of defects is reduced from 0.3371 to 0.0610. Furthermore, process variation is greatly reduced from the previous operating condition.
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Forecasting Commercial Vehicle Demand Using a Multiple Linear Regression Model
(2023-01-01)
Aungkulanon, Pasura
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Hirunwat, Anucha
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Atthirawong, Walailak
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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.

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