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• Employs non-identical parallel machines in manufacturing with multiple energy sources. • Designs a bi-objective model for a high-mix, low-volume production scheduling problem. • Provides Pareto-optimal solutions that minimize both total cost and carbon emissions. • Validates a model with real-world application to a manufacturer in Ulsan, South Korea. • Analyzes the effects of price fluctuations, different combinations of energy sources. This paper addresses the production scheduling problem with non-identical parallel machines in a high-mix, low-volume production environment with due dates, considering carbon emissions savings and diverse types of energy sources for machine operation. To this end, we present a bi-objective mixed-integer programming model that minimizes both the total production-related cost and the total carbon emissions in the production process to determine the optimal production strategy. For each machine, we consider various types of energy sources with different electricity generation costs and different amounts of carbon emissions. The proposed model is validated with an application to a manufacturer in Ulsan, the industrial capital of the Republic of Korea. Our results showcase the potential use of non-identical parallel machines to minimize the total cost and carbon emissions in green manufacturing. Furthermore, the results identify the trade-off between different energy sources and the total cost under different carbon emissions limits. Generally, as the carbon emissions limit increases, our proposed model tends to replace natural gas with coal to minimize the total cost. Also, we perform sensitivity analyses with respect to the energy price of different types of energy sources combined with nuclear and renewable energy sources. We find that coal provides more stability than natural gas in terms of the total cost, particularly when there are fluctuations in the price for coal and natural gas. Additionally, we determine the optimal combination of energy sources for various carbon emissions limits, aiming to minimize the total cost while simultaneously satisfying all production-related and environmental constraints. [ABSTRACT FROM AUTHOR]

Members of the armed forces rely on having an effective and efficient medical evacuation (MEDEVAC) process for evacuating casualties from the battlefield to medical treatment facilities during combat operations. Determining which MEDEVAC units to task to respond to incoming requests for service, known as the MEDEVAC dispatching problem, is a natural sequential decision making problem and is the focus of this paper. Unlike previous research in this area, this paper incorporates triage classification errors and the possibility of having blood transfusion kits on board select MEDEVAC units. A discounted, infinite-horizon continuous-time Markov decision process model is formulated to examine the MEDEVAC dispatching problem and to compare generated dispatching policies to the currently practiced United States military policy. We utilize an approximate dynamic programming (ADP) technique that leverages a random forest value function approximation within an approximate policy iteration algorithmic framework to develop high-quality policies for both small-scale and large-scale problem instances. A representative planning scenario involving joint combat operations in South Korea is developed and utilized to investigate the differences between the various policies. Results from the analysis indicate that applying ADP techniques can improve current practices by as much as 29% with regard to a life-saving performance metric. This research is of particular interest to the military medical community and can inform the procedures of future military MEDEVAC operations. [ABSTRACT FROM AUTHOR]

• Developed a Neural Network foreign exchange trading system using CCS-IRS basis. • The NN foreign exchange trading model outperforms XGBoost and LR model in USDKRW. • The NN trading model outperforms a buy-n-hold and sell-n-hold strategies in USDKRW. • The CCS-IRS basis replaces the CDS premium as a new leading indicator in USDKRW. This study aims to develop a foreign exchange trading model with a new leading indicator based on cross-currency swap (CCS) and interest rate swap (IRS) rates. We derive the correlation dimension from the chaos analysis, use Neural Networks (NNs) to optimize foreign exchange trading, and then compare the performance of the NNs model with XGBoost and Logistic Regression model (LR). The CCS-IRS basis is the spread between the cross-currency swap (CCS) rate and interest rate swap (IRS) rate and is often used as an indicator for foreign exchange soundness along with the CDS premium in Korea. Based on the phenomenon in which the USDKRW exchange rate rises (falls) when the CCS-IRS basis widens (tightens), this study proposes to use the CCS-IRS basis to build a foreign exchange trading model of the USDKRW exchange rate. The experimental results show that our NNs trading model of the USDKRW exchange rate outperforms XGBoost and LR model, generating an annual return of 4.888%∼8.464%, whereas the buy-and-hold and sell-and-hold strategies in the USDKRW exchange rate market produces an annual return of 2.2% and −1.15%, respectively. The foreign exchange trading model established in this paper not only enables foreign exchange traders to obtain sustainable economic profits, but also contributes to the efficiency of the financial market, thereby contributing to sustainable economic growth. [ABSTRACT FROM AUTHOR]

• We suggest a method for automatically finding potential business partners. • Deep learning is applied to capture transaction patterns between companies. • The proposed model is effective to identify potential business partners. • Recommendation performances of the proposed model superior to conventional ones. Potential business partner (BP) recommendation is one of the most important issues for companies to increase sales opportunities by discovering new candidate buyers. Recommendation at a low cost and automation is especially essential for small and medium businesses. However, identifying potential BPs has been regarded as a challenging task since not only an analysis of business characteristics for each candidate company by human experts is required but also an investigation of all the possible combinations of their matchings is necessary. Therefore, in this paper, we propose novel BP recommendation models, called deep business partner recommendation (DBR) models, that aim to automatically suggest potential BPs. Specifically, deep learning technique is applied to understand hidden transaction patterns between companies with various industrial sectors and product properties through the two-phases involving i) BP relation representation phase and ii) training and testing phase. In the former, for each company, its features including the industrial sector, product property, relative transaction volume, and geographical distance are embedded into a vector for utilizing as the input of the proposed models. In the latter, the proposed DBR models repeatedly use the input values to capture the hidden transaction patterns between companies in the training. In the testing phase, the extensive experiments conducted to evaluate the BP recommendation performances of the proposed DBR models using a real-world dataset consisting of transaction records among companies in South Korea. The experiment results show that the suggested DBR models significantly outperform the conventional models, in terms of the accuracy for the BP recommendation tasks. [ABSTRACT FROM AUTHOR]

The experience of Singapore and South Korea makes it clear that under certain circumstances massive testing is an effective way for containing the advance of the COVID-19. In this paper, we propose a modified SEIR model which takes into account tracing and massive testing, proving theoretically that more tracing and testing implies a reduction of the total number of infected people in the long run. We apply this model to the spreading of the first wave of the disease in Spain, obtaining numerical results. After that, we introduce a heuristic approach in order to minimize the COVID-19 spreading by planning effective test distributions among the populations of a region over a period of time. As an application, the impact of distributing tests among the counties of New York according to this method is computed in terms of the number of saved infected individuals. • A SEIR model is introduced to analyse the efficiency of test distributions. • It is proved that massive testing helps reducing the number of infected people. • A heuristic method is developed to increase the number of saved infections. • Extensive computational experience quantifies the number of saved infections. [ABSTRACT FROM AUTHOR]