Janjarassuk, Udom

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Janjarassuk, Udom
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Janjarassuk, Udom
Janjarassuk, U.
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udom.ja@kmitl.ac.th

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    Two-stage stochastic program for supply chain network design under facility disruptions
    (2021-03-01)
    Kungwalsong, Kanokporn
    ;
    Cheng, Chen Yang
    ;
    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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    Image analysis and high dimensional control chart for inspection of sausage color homogeneity and uniformity
    (2016-09-01)
    Kaewsuwan, Piraya
    ;
    Yuangyai, Chumpol
    ;
    Cheng, Chen Yang
    ;
    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<sup>∗</sup>, a<sup>∗</sup>, b<sup>∗</sup>, hue (h) and chroma (C<sup>∗</sup>). These high-dimensional data are transformed to represent color homogeneity and uniformity. Hotelling T<sup>2</sup> 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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    Locating an ambulance base by using social media: a case study in Bangkok
    (2019-12-01)
    Nilsang, Suriyaphong
    ;
    Yuangyai, Chumpol
    ;
    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.