• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• Journal of Veterinary Clinics
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• v.17 no.1
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• pp.102-108
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• 2000

This study was carried out to investigate the epidemiological prevalence of diseases from birth to weaning in 66 dairy calves which were delivered from three stock farm in Chonbuk area. We examined body weight gain, incidence rate of diseases and population mortality rate in relation to age, season, environmental temperature and rearing management conditions for one year. The results of this experiment were as follows: Birth weight of dairy calves born of primiparae was lower than those of multiparae. But no significant difference in body weight gain was observable between dairy calves born of primiparae and those of multiparae. Body weight gain of diseased calves was lower than normal calves. Of 66 delivered calves, 34 calves(51.5%) were affected with gastronitestinal and/or respiratory diseases. The prevalence of the diseases were gastrointestinal disease(28.7%), respiratory disease(18.2%), and gastronitestinal and respiratory disease(4.6%). The gastronitestinal disease was occurred contrinually regardless of the season. Whereas all of the respiratory disease were occurred in winter and a change of season(December to April). 68.4% of the gastronitestinal disease, and all of the respiratory disease were occurred at atmospheric temperatures below 1$0^{\circ}C$. 89.5% of the gastronitestinal disease were occurred within 3 weeks old, whereas 50% of the respiratory disease were occurred intensively between 3 weeks and 4 weeks old. Of 66 delivered dairy claves, 2 calves were died(3%) with respriratory disease.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.4
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• pp.382-392
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• 2006

Vendor-managed inventory policy(VMIP) is a supply-chain initiative where the supplier is authorized to manage inventories of items at retail locations. In VMIP, the supplier monitors sales and stock information at retail locations and makes decisions of inventory replenishment and transportation simultaneously. VMIP has been known as an effective supply chain strategy that can realize many of benefits obtainable only in a fully integrated supply chain. However, VMIP does not always lead to lower the supply chain cost. It sometimes generates the total supply chain cost higher than the traditional retailer-managed inventory policy (RMIP). In this paper, we perform a comparison study on RMIP and VMIP in the retail supply chain which consists of a single supplier and a number of retailers. We formulate mixed integer programming models for both RMIP and VMIP with vehicle routing problems and perform computational experiments on various test problems. Furthermore, we derive the conditions which guarantee the dominant position for VMIP with respect to total supply chain cost in the simple retail supply chain.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.3
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• pp.259-266
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• 2015

Among many energy resources, natural gas has recently received a remarkable amount of attention, particularly from the electrical generation industry. This is in part due to increasing shale gas production, providing an environment-friendly fossil fuel, and high risk of nuclear power. Because South Korea, the world's second largest LNG importing nation after Japan, has no international natural gas pipelines and relies on imports in the form of LNG, the natural gas has been traditionally procured by long term LNG contracts at relatively high price. Thus, there is a need of developing an Asian LNG trading hub, where LNG can be traded at more competitive spot prices. In a natural gas spot market, the amount of natural gas to be bought should be carefully determined considering a limited storage capacity and future pricing dynamics. In this work, the problem to find the optimal amount of natural gas in a spot market is formulated as a Markov decision process (MDP) in risk neutral environment and the optimal base stock policy which depends on a stage and price is established. Taking into account price and demand uncertainties, the basestock target levels are simply approximated from dynamic programming. The simulation results show that the basestock policy can be one of effective ways for procurement of LNG in a spot market.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.2
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• pp.132-143
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• 2011

This paper deals with a coal supply planning problem for power plants. We propose a mathematical optimization model to make decisions for coal pile sections, movement of reclaimers, and operation time of conveyor belts. The objective of the proposed model is to minimize the total operation time of conveyor belts and total movement time of reclaimers. The algorithm firstly selects a pile section by considering both the location of reclaimers and the stock amount on that pile section. And then the shortest path from the selected pile section has to be put into the operation schedule and check whether the total operation time is satisfied. Then finally the new schedule is updated. To this end, we have tested the proposed algorithm comparing with the general standard optimization package for the simplified problem SCSPP. From the numerous test runs for comparing with the existing coal supply scheduling methods, We see that the proposed model may improve the coal supply operation by reducing significant coal supply costs.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.105-113
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• 1999

최근 데이터와 데이터베이스의 폭발적 증가에 따라 무한한 데이터 속에서 정보나 지식을 찾고자하는 지식채굴과정(Knowledge discovery process)에 대한 관심이 높아지고 있다. 특히 기업 내외부 데이터베이스 뿐만 아니라 데이터웨어하우스(data warehouse)를 기반으로 하는 OLAP 환경에서의 데이터와 인터넷을 통한 웹(web)에서의 정보 등 정보원의 다양화와 첨단화에 따라 다양한 환경 하에서의 지식 채굴과정이 요구되고 있다. 본 연구에서는 인터넷 상의 지식을 효과적으로 채굴하기 위한 지식채굴과정을 제안한다. 제안된 지식채굴과정은 명시지(explicit knowledge)외에 암묵지(tacit knowledge)를 지식채굴과정에 반영하기 위해 선행지식베이스(prior knowledge base)와 선행지식관리시스템(prior knowledge management system)을 이용한다. 선행지식관리시스템은 퍼지인식도(fuzzy cognitive map)를 이용하여 선행지식베이스를 구축하여 이를 통해 웹에서 찾고자 하는 유용한 정보를 정의하고 추출된 정보를 지식변환시스템(knowledge transformation system)을 통해 통합적인 추론과정에 사용할 수 있는 형태로 변환한다. 제안된 연구모형의 유용성을 검증하기 위하여 재무자료에 선행지식을 제외한 자료와 선행지식을 포함한 자료를 사례기반추론 (case-based reasoning)을 이용하여 실험한 결과, 제안된 지식채굴과정이 유용한 것으로 나타났다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2783-2796
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• 2000

In electronic commerce systems, for the purchase of package goods to be processed in realtime, the stock of their components has to be

• The Journal of the Korea Contents Association
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• v.7 no.7
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• pp.161-170
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• 2007

E-Business has, nowadays, become a common commerce transaction. In the beginning, e-Biz has known as Electronic Commerce and has expanded its territory to department store's shopping mall, travel, finance, stock, and even luxury goods as car sales market. Considering these trends, this paper researched the environment of korean e-Biz market and suggested the picture of the matured and sound e-Biz market in Korea. We surveyed matured level of Critical Success Factors of e-Biz in terms of management. We also surveyed time based Critical Success Factors to analyze level of the Korean e-Biz market. These study's may provide us the knowledge about the prediction and preparation for changes in e-Biz market in the future.

• Journal of Distribution Science
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• v.11 no.2
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• pp.5-10
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• 2013

Purpose - The current research examines the effect of life cycle stages on capital structure of listed companies in Tehran Stock Exchange. Research design, data, methodology - By aid of 685 year-company data, which collected from financial statements of companies during 2006-2012, first, the companies, are classified into three groups including companies in growth, maturity and decline stages. After removing the companies, which were not in accordance with life cycle model, 86 companies were selected to test two main hypotheses of the research. Results - The results show that the capital structure of the sample companies is different in various life cycle stages. More investigation by LSD test also revealed that the total debt to total assets ratio means of the companies in growth stages were significantly different from those companies in maturity stages and those in growth stages had high level of debt to assets ratio. Conclusions - The result showed the average amount of the working capital for companies in three stages are significantly different and due to high level of operation of the companies in maturity and decline stages, these companies held high amount of working capital than those in the growth stages.