• Korean Journal of Computational Design and Engineering
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• v.12 no.3
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• pp.191-199
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• 2007

In order to construct a data warehouse of process plant equipment, a classification structure should be defined first, identifying not only the equipment categories but also attributes of an each equipment to represent the specifications of equipment. ISO 15926 Process Plants is an international standard dealing with the life-cycle data of process plant facilities. From the viewpoints of defining classification structure, Part 2 data model and Reference Data Library (RDL) of ISO 15926 are seen to respectively provide standard syntactic structure and semantic vocabulary, facilitating the exchange and sharing of plant equipment's life-cycle data. Therefore, the equipment data warehouse with an ISO 15926-based classification structure has the advantage of easy integration among different engineering systems. This paper introduces ISO 15926 and then discusses how to define a classification structure with ISO 15926 Part 2 data model and RDL. Finally, we describe the development result of an ISO 15926-based classification structure for a variety of equipment consisting in the reactor coolant system (RCS) of APR 1400 nuclear plant.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.23-31
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• 1999

Using Information Systems to process massive data, quickly and exactly, organizations have chances to enhance their performance. The limitations of IS function to support decision-making, however, have been frequently mentioned In this context, in addition to traditional mathematical model that is a kernel DSS, the needs for Data Warehouse which is a system supporting business process analysis are emerging. In this study, for those needs first we introduce issues of implementation methodology for D/W, especially various models relating development process. Then we investigate correlation between these models and key factors for success of R/W.

• Korea Trade Review
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• v.46 no.4
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• pp.149-166
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• 2021

This paper aims to reduce the frequency of dangerous goods storage accidents in China. Advocating the managers of warehousing and logistics enterprises to pay attention to the operation process of dangerous goods warehousing business. Improving the safe storage and management capabilities of dangerous goods warehouses. This article first collects official data on dangerous goods storage accidents in China and conducts a general statistical analysis of the accidents. Based on the results of accident statistics and related literature research on dangerous goods storage management, establish a dangerous goods storage safety management factor system, use the analytic hierarchy process, establish a factor importance questionnaire and implement data collection. Through statistics, this paper finds that the storage accidents of dangerous goods in China in the past ten years mainly occurred in the inbound phase of dangerous goods and the storage phase of dangerous goods warehouses. Through the results of the analytic hierarchy process, it is found that the professionalism of the dangerous goods storage practitioners, the compliance of the practitioners with safety regulations, and the awareness of operational safety are the most important.

• Journal of Korea Port Economic Association
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• v.31 no.3
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• pp.167-186
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• 2015

The purpose of this case study is to verify the usefulness of three costing models and a minimum profit management model that can support the logistics warehouse companies in the Busan Newport Distri-park. This case study investigated traditional costing, activity-based costing (ABC), and time-driven ABC (TDABC); and suggested that an appropriate minimum profit management model is contribution margin analysis. Accordingly, in order to verify the usefulness of models, this case study surveyed the actual cost management conditions of companies, applied the three costing models to the "K" warehouse company in the Busan Newport Distri-park, and undertook a comparative study of the results. This case study produced two main findings. First, TDABC was verified as the most useful and advanced of the three costing models tested. Second, contribution margin analysis was confirmed to be the most suitable model to manage minimum profits for port warehouse companies in the Busan Newport Distri-park.

• Transactions of the Korean hydrogen and new energy society
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• v.30 no.3
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• pp.282-288
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• 2019

When liquefied natural gas (LNG) is vaporized to form natural gas for industrial and household consumption, a tremendous amount of cold energy is transferred from LNG to seawater as a part of the phase-change process. This heat exchange loop is not only a waste of cold energy, but causes thermal pollution to coastal fishery areas by dumping the cold energy into the sea. This project describes an innovative new design for reclaiming cold energy for use by cold storage warehouses (operating in the 35 to $62^{\circ}C$ range). Conventionally, warehouse cooling is done by mechanical refrigeration systems that consume large amounts of electricity for the maintenance of low temperatures. Here, a closed loop LNG heat exchange system was designed (by simulator) to replace mechanical or vapor-compression refrigeration systems. The software PRO II with PROVISION V9.4 was used to simulate LNG cold energy, gas re-liquefaction, and the vaporized process under various conditions. The effects on sensible and latent heats from changes to the array type of heat exchangers have been investigated, as well as an examination of the optimum.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.1-9
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• 2019

Recently, as the importance of warehouses has increased, much efforts are being made to improve the picking process in order to cope with a small amount of high frequency and fast delivery. This study proposes an algorithm to assign orders to pickers in the situation where Single Order Picking policy is used. This algorithm utilizes five attributes related to picking such as picking processing time, elapsed time after receipt of order, inspection/packing workstation situation, picker error, customer importance. A measure of urgency is introduced so that the units of measure for each attribute are the same. The higher the urgency, the higher the allocation priority. In the proposed algorithm, the allocation policy can be flexibly adjusted according to the operational goal of the picking system by changing the weight of each attribute. Simulation experiments were performed on a hypothetical small logistics warehouse. The results showed excellent performance in terms of system throughput and flow time.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.

• Information Systems Review
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• v.2 no.2
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• pp.283-299
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• 2000

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.189-207
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• 1997

Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.8-13
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• 2003

This paper considers a continuous-review two-echelon inventory control problem with one-to-one replenishment policy incorporated and with lost sales allowed where demand arrives In a stationary Poisson process The problem Is formulated using METRIC-approximation in a combined approach of pricing and (S-1.S) Inventory policy, for which an iterative solution algorithm is derived with respect to the corresponding one-warehouse multi-retailor supply chain. Specifically, decisions on retail pricing and warehouse inventory policies are made in integration to maximize total profit in the supply chain. The objective function of the model consists of sub-functions of revenue and cost (holding cost and penalty cost). To test the effectiveness and efficiency of the proposed algorithm, numerical experiments are performed The computational results show that the proposed algorithm is efficient and derives quite good decisions