목적달성 문제에서 다시점 효과를 고려한 QFD 계획 모형의 확장에 관한 연구

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A Joint Optimal Decision on Shipment Size and Carbon Reduction under Direct Shipment and Peddling Distribution Strategies

Recently, much research has focused on lowering carbon emissions in logistics. This paper attempts to contribute to the literature on the joint shipment size and carbon reduction decisions by developing novel models for distribution systems under direct shipment and peddling distribution strategies. Unlike the literature that has simply investigated the effects of carbon costs on operational decisions, we address how to reduce carbon emissions and logistics costs by adjusting shipment size and making an optimal decision on carbon reduction investment. An optimal decision is made by analyzing the distribution cost including not only logistics and carbon trading costs but also the cost for adjusting carbon emission factors. No research has explicitly considered the two sources of carbon emissions, but we develop a model covering the difference in managing carbon emissions from transportation and storage. Structural analysis guides how to determine an optimal shipment size and emission factors in a closed form. Moreover, we analytically prove the possibility of reducing the distribution cost and carbon emissions at the same time. Numerical analysis follows validation of the results and demonstrates some interesting findings on carbon and distribution cost reduction

A DEA approach for evaluating the relationship between energy efficiency and financial performance for energy-intensive firms in Korea

In order to keep pace with the global trend of controlled and proper energy usage, the Korean government has enforced its energy-related regulations. However, the firms under these regulations are of the view that this approach may threaten their financial performance. In response to the growing interest of industry in Korea, this paper investigates whether energy efficiency has a positive relationship with firms' financial performance. For the purpose of accommodating various factors for measuring energy efficiency, we extended an existing two-stage network DEA (Data Envelopment Analysis) model that distinguished pure-energy efficiency and economy efficiency by developing a multi-period model with the aim to identify any change in efficiency during each period. An empirical analysis on Korean firms reveals interesting findings; First, while it is true that energy efficiency has changed over time, there is a difference in the magnitude of changes by industry. Firms in more energy-intensive industry have experienced more changes in their energy efficiency performance. Second, it is impossible to improve both categories of efficiency (i.e., pure-energy and economic efficiencies) at the same time. Thus, a firm should understand its current position in order to determine the extent and direction of efficiency improvements. Third, the energy efficiency has been found to have a significant relationship with financial performance. However, firms whose pure-energy efficiency was found to be relatively high did not always achieve better financial performance. (C) 2020 Elsevier Ltd. All rights reserved

The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers

In recent years, big data has been widely used to understand consumers' behavior and opinions. With this paper, we consider the use of big data and its effects in the problem of projecting the number of reverse mortgage subscribers in Korea. We analyzed web-news, blog post, and search traffic volumes associated with Korean reverse mortgages and integrated them into a Generalized Bass Model (GBM) as a part of the exogenous variables representing marketing effort. We particularly consider web-news volume as a proxy for marketer-generated content (MGC) and blog post and search traffic volumes as proxies for user-generated content (UGC). Empirical analysis provides some interesting findings: First, the GBM by incorporating big data is helpful for forecasting the sales of Korean reverse mortgages, and second, the UGC as an exogenous variable is more useful for predicting sales volume than the MGC. The UGC can explain consumers' interest relatively well. Additional sensitivity analysis supports that the UGC is important for increasing sales volume. Finally, prediction performance is different between blog posts and search traffic volumes

Assessing energy efficiency and the related policy implications for energy-intensive firms in Korea: DEA approach

The Korean government has given efforts to reduce green-house gas emissions. In order to avoid the monolithic system that only focuses on reducing the emission without considering operational environments of firms, the government should encourage respective firms to improve their energy efficiency which wastes less energy but produces maximum economic revenue. This paper employed a DEA (Data Envelopment Analysis) model to measure energy efficiency of energy intensive manufacturing firms in Korea. Identifying and comparing the difference in efficiency score by using the simplified single-stage DEA model is not sufficient to give governments and firms meaningful policy implications related to energy efficiency improvement. To compensate for this insufficiency, this study extended the single stage model into a two-stage model that includes two efficiency measures: pure energy efficiency which is only for energy-related process and economy efficiency which is for the pursuit of profit, to reach the improvement of total energy efficiency. By using the two-stage DEA model, it has identified that the difference from overall energy efficiency is not caused by pure energy efficiency but rather by economy efficiency. In addition, this paper statistically analyzed the effects of environmental variables such as firm size, possession of certified management systems and emission type. (C) 2017 Elsevier Ltd. All rights reserved

An Extended QFD Planning Model Considering Longitudinal Effect: High-speed Internet Service Case Example

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Effects of the move towards renewables on the power system reliability and flexibility in South Korea

The plan to shift towards renewable energy has recently become the central part of the energy policy on the power system in South Korea. The sudden shift towards renewable energy has raised questions regarding the reliability and flexibility of the power system. This paper proposes a research framework to evaluate the new policy in South Korea from various aspects using three simulation models in a series. The first optimal generation model finds the optimal electricity generation mix and provides the total generation cost and environmental impact of the given long-term capacity expansion plan. The other two simulation models assess the reliability and flexibility of power systems, respectively. Within the research framework, we introduce a new probabilistic index to quantitatively measure the flexibility. The results of applying the framework into the new policy show that the policy fails to guarantee the target reliability level and increases costs and emissions. Achieving target system reliability will require additional capacity, and system flexibility is very sensitive to the type of capacity added. If the Korean government decides to add more capacity, natural gas turbine power plants turn out to be a good option from the point of economic, environmental, and flexibility considerations. © 202

A learning-based approach for dynamic freight brokerages with transfer and territory-based assignment

The recent evolution of information technology in logistics has facilitated the digital freight brokerage, which allows shippers and trucks to match needs and services in a short-term period. In the context of freight brokerages, this paper considers a dynamic fleet assignment problem related to matching demand and supply. We particularly integrate practical operational characteristics, such as territory-based assignment and transferring, which have not been considered in the dynamic fleet assignment problem. We first formulate the problem as a Markov Decision Process (MDP) to represent uncertain and sequential decision-making procedures. Furthermore, to overcome the dimensionality and ambiguity of the MDP model, we proposed a reinforcement learning(RL) approach with function approximation for solving the MDP model. Finally, numerical experiments are carried out to illustrate the superiority of the RL method and analyze the effects of operational characteristics. A numerical analysis shows that the proposed RL-based method provides more rewards than other policies, such as myopic policy and first-come-first-served (FCFS) policy, for all test scenarios. The RL-based method is even better for a situation in which the delivery territories are highly overlapped and customer demand exceeds to supply capacity, which requires precise capacity control. In addition, we observe that significant achievement can be attained by allowing trucks to deliver with transfer

An extended QFD planning model for goal attainment considering longitudinal effect

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Efficiency of well-diversified portfolios: Evidence from data envelopment analysis

In this work, we evaluate eight exchange traded funds (ETFs) and their benchmark index (the KOSPI 200 Index), based on the Sharpe ratio and the Treynor ratio and find that the performance of these well-diversified portfolios are quite poor relative to individual stocks. Investors׳ preference to avoid the well-diversified portfolios would be related to this poor performance. However, we empirically show that ETFs and the KOSPI 200 Index are the most efficient investment instruments with respect to the new performance measure designed on the basis of the data envelopment analysis (DEA) methodology. Examining the panel data over the period between 2003 and 2014 indicates that well-diversified portfolios improve the efficiency by adjusting the input variables (σ and β). Furthermore, they do so more effectively as they mature. © 2017 Elsevier Lt