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Seung-June Hwang
Institute of Knowledge Services, Hanyang University, Erica, Ansan 15588, Korea

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Journal article
Published: 15 June 2020 in Sustainability
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Recently, as global warming has become a major issue, many companies have increased their efforts to control carbon emissions in green supply chain management (GSCM) activities. This paper deals with the multi-item replenishment problem in GSCM, from both economic and environmental perspectives. A single buyer orders multiple items from a single supplier, and simultaneously considers carbon cap-and-trade under limited storage capacity and limited budget. In this case we can apply a can-order policy, which is a well-known multi-item replenishment policy. Depending on the market characteristics, we develop two mixed-integer programming (MIP) models based on the can-order policy. The deterministic model considers a monopoly market in which a company fully knows the market information, such that both storage capacity and budget are already determined. In contrast, the fuzzy model considers a competitive or a new market, in which case both of those resources are considered as fuzzy numbers. We performed numerical experiments to validate and assess the efficiency of the developed models. The results of the experiments showed that the proposed can-order policy performed far better than the traditional can-order policy in GSCM. In addition, we verified that the fuzzy model can cope with uncertainties better than the deterministic model in terms of total expected costs.

ACS Style

Jiseong Noh; Jong Soo Kim; Seung-June Hwang. A Multi-Item Replenishment Problem with Carbon Cap-And-Trade under Uncertainty. Sustainability 2020, 12, 4877 .

AMA Style

Jiseong Noh, Jong Soo Kim, Seung-June Hwang. A Multi-Item Replenishment Problem with Carbon Cap-And-Trade under Uncertainty. Sustainability. 2020; 12 (12):4877.

Chicago/Turabian Style

Jiseong Noh; Jong Soo Kim; Seung-June Hwang. 2020. "A Multi-Item Replenishment Problem with Carbon Cap-And-Trade under Uncertainty." Sustainability 12, no. 12: 4877.

Journal article
Published: 11 April 2020 in Mathematics
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Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies’ concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.

ACS Style

Jiseong Noh; Hyun-Ji Park; Jong Soo Kim; Seung-June Hwang. Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management. Mathematics 2020, 8, 565 .

AMA Style

Jiseong Noh, Hyun-Ji Park, Jong Soo Kim, Seung-June Hwang. Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management. Mathematics. 2020; 8 (4):565.

Chicago/Turabian Style

Jiseong Noh; Hyun-Ji Park; Jong Soo Kim; Seung-June Hwang. 2020. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management." Mathematics 8, no. 4: 565.

Journal article
Published: 01 February 2012 in Expert Systems with Applications
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Process monitoring and diagnosis have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. Although traditional statistical process control (SPC) tools are effective in simple manufacturing processes that generate a small volume of independent data, these tools are not capable of handling the large streams of multivariate and autocorrelated data found in modern systems. As the limitations of SPC methodology become increasingly obvious in the face of ever more complex processes, data mining algorithms, because of their proven capabilities to effectively analyze and manage large amounts of data, have the potential to resolve the challenging problems that are stretching SPC to its limits. In the present study we attempted to integrate state-of-the-art data mining algorithms with SPC techniques to achieve efficient monitoring in multivariate and autocorrelated processes. The data mining algorithms include artificial neural networks, support vector regression, and multivariate adaptive regression splines. The residuals of data mining models were utilized to construct multivariate cumulative sum control charts to monitor the process mean. Simulation results from various scenarios indicated that data mining model-based control charts performs better than traditional time-series model-based control charts.

ACS Style

Seoung Bum Kim; Weerawat Jitpitaklert; Sun-Kyoung Park; Seung-June Hwang. Data mining model-based control charts for multivariate and autocorrelated processes. Expert Systems with Applications 2012, 39, 2073 -2081.

AMA Style

Seoung Bum Kim, Weerawat Jitpitaklert, Sun-Kyoung Park, Seung-June Hwang. Data mining model-based control charts for multivariate and autocorrelated processes. Expert Systems with Applications. 2012; 39 (2):2073-2081.

Chicago/Turabian Style

Seoung Bum Kim; Weerawat Jitpitaklert; Sun-Kyoung Park; Seung-June Hwang. 2012. "Data mining model-based control charts for multivariate and autocorrelated processes." Expert Systems with Applications 39, no. 2: 2073-2081.