Yuangyai, Chumpol

Preferred name

Yuangyai, Chumpol

Alternative Name

Yuangyai, Čhumpol

Yuangyai, C.

Main Affiliation

School of Engineering

chumpol.yu@kmitl.ac.th

Now showing 1 - 10 of 14

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Investigating of antenna selection for the adaptive centroid localization systems
(2015-09-01)
Yuangyai, Chumpol
;
Pan, Chung Yu
;
Lin, Yi Jou
;
Cheng, Chen Yang
Location-based services are widely integrated in our daily lives, and they are used in various ways such as for investigating inventory goods and personal tracking in healthcare. With increasing applications of wireless localization, accuracy of location estimation requirements has become more critical. However, indoor localization suffers from multi-path interference that affects the traditional algorithmic calculation methods based on radio signal strength. The strength of radio signal depends significantly on antenna types and the deployment of wireless sensor network. Therefore, the aim of this paper is to investigate a robust deployment of wireless sensor network considering antennas and optimal signal range to increase signal strength and to reduce receiving signal missing. A bi-response design approach was taken to evaluate antenna selection, signal range, and antenna power rate. The experiment result was applied with existing algorithm to prove effectives. Further, to avoid wireless sensors collision which may result in low accuracy of receiving radio signal, adaptive weight center of gravity localization (AWCG) were proposed. AWCG is based on an assumption of the dynamic relationship between the radio signal strength and the distance in different environments at different times. In the proposed localization algorithm, the error distance was approximately one meter. It is expected to significantly improve the location estimation accuracy with the suggested deployment and proposed algorithm.
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A Comparative Study of Unbalanced Production Lines Using Simulation Modeling: A Case Study for Solar Silicon Manufacturing
(2022-01-01)
Cheng, Chen Yang
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Li, Shu Fen
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Lee, Chia Leng
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Jientrakul, Ranon
;
Yuangyai, Chumpol
In the solar silicon manufacturing industry, the production time for crystal growth is ten times longer than at other workstations. The pre-processing time at the ingot-cutting station causes work-in-process (WIP) accumulation and an excessively long cycle time. This study aimed to find the most effective production system for reducing WIP accumulation and shortening the cycle time. The proposed approach considered pull production systems, and the response surface methodology was adopted for performance optimization. A simulation-based optimization technique was used for determining the optimal pull production system. The comparison between the results of various simulated pull production systems and those of the existing solar silicon manufacturing system showed that a hybrid production system in which a kanban station was installed before the bottleneck station with a CONWIP system incorporated for the rest of the production line could reduce the WIP volume by 26% and shorten the cycle time by 16% under the same throughput conditions.
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An auction bidding approach to balance performance bonuses in vehicle routing problems with time windows
(2021-08-02)
Cheng, Chen Yang
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Ying, Kuo Ching
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Lu, Chung Cheng
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Yuangyai, Chumpol
;
Chiang, Wan Chen
In the field of operations research, the vehicle routing problem with time windows (VRPTW) has been widely studied because it is extensively used in practical applications. Real-life situations discussed in the relevant research include time windows and vehicle capabilities. Among the constraints in a VRPTW, the practical consideration of the fairness of drivers’ performance bonuses has seldom been discussed in the literature. However, the shortest routes and balanced performance bonuses for all sales drivers are usually in conflict. To balance the bonuses awarded to all drivers, an auction bidding approach was developed to address this practical consideration. The fairness of performance bonuses was considered in the proposed mathematical model. The nearest urgent candidate heuristic used in the auction bidding approach determined the auction price of the sales drivers. The proposed algorithm both achieved a performance bonus balance and planned the shortest route for each driver. To evaluate the performance of the auction bidding approach, several test instances were generated based on VRPTW benchmark data instances. This study also involved sensitivity and scenario analyses to assess the effect of the algorithm’s parameters on the solutions. The results show that the proposed approach efficiently obtained the optimal routes and satisfied the practical concerns in the VRPTW.
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Intelligent robot of inclined assembly sequence planning in Industrial 4.0
(2018-08-14)
Chiang, Yu Cheng
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Yuangyai, Chumpol
;
Cheng, Chen Yang
In the industry 4.0, the Cyber-physical system (CPS) is one of the most important core which makes the manufacturing process more intelligent. Intelligent assembly operation is an important key in intelligent manufacturing of CPS. To complete the intelligent assembly operation, the cooperation between assembly robotic arm and assembly sequence planning (ASP) is necessary. However, the ASP and writing robotic codes manually is time consuming and requires professional knowledge and experience. Because the Local Coordinate System (LCS) is often ignored when checking for interference. If product have inclined interference and without considering LCS and causing and infeasible ASP. Therefore, this paper proposes a LCCPIAS (Local Coordinate Cyber-Physical Intelligent Assembly System) system to achieve three objective functions. First, this paper presents a dual-projected-based interference analysis approach (DPIAA) that analyzes the relations between components. Second, this paper generates optimal assembly sequence automatically to let the assembly sequence more suitable for the robotic arm to perform the assembly operation. The last one is LCS can recognize inclined interference between components and generate feasible ASP. Furthermore, this paper uses CAD model to verify that the DPIAA is faster and consider LCS interference can solve inclined interference problem. In the future assembly factory, the proposed method can help to realize intelligent manufacturing.
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Two-stage stochastic program for supply chain network design under facility disruptions
(2021-03-01)
Kungwalsong, Kanokporn
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Cheng, Chen Yang
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Yuangyai, Chumpol
;
Janjarassuk, Udom
A supply chain disruption is an unanticipated event that disrupts the flow of materials in a supply chain. Any given supply chain disruption could have a significant negative impact on the entire supply chain. Supply chain network designs usually consider two stage of decision process in a business environment. The first stage deals with strategic levels, such as to determine facility locations and their capacity, while the second stage considers in a tactical level, such as production quantity, delivery routing. Each stage’s decision could affect the other stage’s result, and it could not be determined individual. However, supply chain network designs often fail to account for supply chain disruptions. In this paper, this paper proposed a two-stage stochastic programming model for a four-echelon global supply chain network design problem considering possible disruptions at facilities. A modified simulated annealing (SA) algorithm is developed to determine the strategic decision at the first stage. The comparison of traditional supply chain network decision framework shows that under disruption, the stochastic solutions outperform the traditional one. This study demonstrates the managerial viability of the proposed model in designing a supply chain network in which disruptive events are proactively accounted for.
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Multiple Performance Optimization for Microstrip Patch Antenna Improvement
(2023-05-01)
Chen, Ja Hao
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Cheng, Chen Yang
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Chien, Chuan Min
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Yuangyai, Chumpol
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Chen, Ting Hua
As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.
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Image analysis and high dimensional control chart for inspection of sausage color homogeneity and uniformity
(2016-09-01)
Kaewsuwan, Piraya
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Yuangyai, Chumpol
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Cheng, Chen Yang
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Janjarassuk, Udom
Sausage color usually influences consumers' selection due to the perceptions of quality. Extensive studies have applied image processing to capture the characteristics of food products according to the high-dimensional nature of the resultant images. However, the color homogeneity (i. e. "within pack" variation) and uniformity (i. e. "between-pack" variation) have rarely been studied. Therefore, this paper proposes a new framework to detect both variations using images. In addition, a new approach has been developed to deal with high-dimension data involving colorimetric characteristics, namely L∗, a∗, b∗, hue (h) and chroma (C∗). These high-dimensional data are transformed to represent color homogeneity and uniformity. Hotelling T2 chart is used to detect color abnormalities. Our approach indicates that the out-of-control items can be identified with the control chart signals. Nonetheless, the out-of-control signals alone are inadequate for determination of the possible causes. Then, the proposed analysis framework was subsequently applied to identify possible causes that contributed to the process deviations. Furthermore, prior to the experiments with sausages, the image inspection device was tested for gauge repeatability and reproducibility.
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Smart monitoring of manufacturing systems for automated decision‐making: A multi‐method framework
(2021-10-02)
Cheng, Chen Yang
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Pourhejazy, Pourya
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Hung, Chia Yu
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Yuangyai, Chumpol
Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real‐time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi‐method framework for the smart monitoring of manufacturing systems and intelligent decision‐making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.
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Crowdsourced adaptive vehicle routing framework for Last-Mile delivery in dynamic traffic environments
(2025-01-01)
Dahlan, Ahmad Faisal
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Cheng, Chen Yang
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Sae-chai, Pornkanok
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Jientrakul, Ranon
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Yuangyai, Chumpol
Last-Mile Delivery (LMD) operations are significantly impacted by real-time traffic disruptions, leading to delays and increased costs. While traditional vehicle routing problem (VRP) model struggle to adapt to dynamic traffic environments, crowdsourced data from social media platforms presents a valuable source of real-time traffic data. This research proposes Crowdsourced Adaptive Vehicle Routing Framework (CAVRF) that integrates crowdsourced social media data into the VRP model for enhanced efficiency. The framework employs a machine learning model to classify tweets based on impact severity and effectively filtering relevant traffic information. Furthermore, a mathematical model known as the Adaptive Traffic VRP (AT-VRP) has been developed to accommodate the integration of social media data with the VRP model. The framework’s effectiveness is demonstrated through a case study using a package delivery network in Jakarta with various levels of traffic disruptions. The findings suggest that integrating crowdsourced social media data into AT-VRP significantly improves efficiency by avoiding any road closure. CAVRF offers a cost-effective and efficient solution to the dynamic challenges inherent in LMD.
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Locating an ambulance base by using social media: a case study in Bangkok
(2019-12-01)
Nilsang, Suriyaphong
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Yuangyai, Chumpol
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Cheng, Chen Yang
;
Janjarassuk, Udom
Response time reduction is a fundamental aspect of ambulance location management. To minimize patient mortality and disability, the response time of emergency medical services is critical. Therefore, real-time management is required to determine the location of an ambulance with a low response time or called or a dynamic allocation system. Dynamic allocation is moving the ambulance bases from low demand areas to high-demand areas that is useful in the operational level. However, the dynamic allocation model for real-time management requires re-allocation of ambulances, resulting in high costs and heavy workloads for the ambulance crews. This paper focuses on a covering model based on social media analysis. The model was used for developing an ambulance reallocation system. In addition to dynamic allocation, the proposed model considers real-time data from a social media application (Twitter) to minimize the response time and cost during emergencies and disasters. Twitter has been used in various ways to communicate during and manage emergencies. In this paper, we formulate the Maximal Covering Location Problem (MCLP), develop a solution procedure based on social media (Twitter application) and show the effect of the approach on the optimal solution by comparing it with the classical approach and also demonstrate our approach on Bangkok EMS.

Yuangyai, Chumpol

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