• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.421-432
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• 1997

In general, Automated Storage/Retrieval Systems (AS/RS) have racks of equal size. But higher utilization of warehouse storage can be achieved by using AS/RS with racks of different size. Therefore those systems are adequate and efficient in current environment. In this paper, travel time models are developed for AS/RS with racks of different size under randomized storage in each zone. Each zone has its own rack size. In order to confirm the proposed travel time models, some numerical examples are given.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.2
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• pp.195-206
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• 1995

Higher utilization of warehouse space can be achieved by using automated mobile racking systems. Therefore, those systems may be employed for factories or distribution centers as a good option of increasing the storage capacity. In this paper, travel-time models are developed to estimate the average performance of the system assuming randomized storage. Expected travel times are determined for both single- and dual-command cycles. Two extreme input/output-point locations are considered. Some numerical results obtained from the models are given.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.20-27
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• 1999

This study presents an Automated Warehouse Management System (AWMS) using a bar-code system The AWMS has been designed to be associated with an Integrated Production Management System (IPMS), which basically includes the following four modules i.e the sales management, production management, material management, and data management. Now, whenever storage or retrieval of the material occurs in the warehouse, the events could be processed quickly and accurately only through reading the 13 digit bar-code including 5 digit position code and simply typing in the number of material. Consequently, the AWMS associated with IPMS could automatically coincide the item counts on document with those in the warehouse. Also, this makes it possible to identify the material quantities in real time.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.171-178
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• 2023

In this paper, we propose a smart automated warehouse to maximize space utilization. Previous elevator-type automatic warehouses were designed with a maximum payload of 100kg on trays, which has the problem of extremely limiting the number of pallets that can be loaded within the space. In this paper, we design a smart warehouse that can maximize space utilization with a maximum vertical stiffness of 300kg. As a result of the performance evaluation of the implemented warehouse, the maximum payload was 500.6kg, which satisfied the original design and requirements, the lifting speed was 0.5m/s, the operating noise of the device was 67.1dB, the receiving and forwarding time of the pallet was 36.92sec, the deflection amount was 4mm, and excellent performance was confirmed in all evaluation items. In addition, the PLC control method, which designs the control UI and control panel separately, was integrated into the PC system to improve interoperability and maintainability with various process management systems. In the future, we plan to develop it into a fully automatic smart warehouse by linking IoT sensor-based logistics robots.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.486-493
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• 2010

This paper demonstrates the design and implementation of fruit larder system that is able to monitor and control restoration environment using mobile equipment. Based on RFID/USN technology, it builds wireless sensor network to enhance its efficiency to fruit larder environment and inventory management and performs automated environment management through window-based application that works in desktop environment. Additionally, using WINC service, it provides remote control function for fruit larder using mobile equipment.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.2
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• pp.109-121
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• 1992

In this paper, we address a layout design problem for determining a K-class-based dedicated storage layout in an automated storage retrieval system. K-class-based dedicated storage employs K zones in which lots from a class of products are stored randomly. Zones form a partition of storage locations. Our objective function is to minimize the expected single command travel time, which is expressed as a set function of space requirements for zones, average demand rates from classes, and one-way travel times from the pickup/deposit station to locations. We construct a heuristic algorithm based on analytical results and a local search method, the methodology deveolped can be used with easily-available data by warehouse planners to improve the throughput capacity of a conventional warehouse as well as an AS/RS.

• Journal of the Korea Society of Computer and Information
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• v.8 no.1
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• pp.13-19
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• 2003

There are active researches on automated guided vehicles(AGV) generally used in flexible manufacturing system(FMS) or automated warehouse systems(AWS). Because existing AGV uses magnetic tapes, electric wire, RF or laser as guidelines, its installation and modification require a lot of money and time. The present study implemented AGV that detects paths marked with 50mm Yellow tape using a mono-color CCD camera. Because it uses color tape, it is easy and inexpensive to install and change lines. This study presented the structure of the developed AGV, the image Processing technique for detecting guidelines by ing the characteristics of color, and the result of operating AGV.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1991.11a
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• pp.1-13
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• 1991

This paper describes two FMS, which were developed by SAEIL Heavy Industries. One is FMS for machining of circular parts, that are automobile's axle shafts. This system consists of 13 units, including 7 CNC machine tools. The other is FMS for machining of non-circular parts namely casted or steel block within size of 1.2 X 1.2 X 1.5m. This FMS consists of 8-machining centers, 1-automated warehouse, 2-unmanned Robo Trailers, and computer control room. All systems are functioning satisfactorily and so help greately towards automation of Korean industries.

• International Journal of Reliability and Applications
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• v.3 no.4
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• pp.155-164
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• 2002

This paper focuses on how modem data mining can be integrated with real-time relational databases and commercial data warehouses to improve reliability in real-time. An important Issue for many manufacturers is the development of relational databases that link key product attributes with real-time process parameters. Helpful data for key product attributes in manufacturing may be derived from destructive reliability testing. Destructive samples are taken at periodic time intervals during manufacturing, which might create a long time-gap between key product attributes and real-time process data. A case study is briefly summarized for the medium density fiberboard (MDF) industry. MDF is a wood composite that is used extensively by the home building and furniture manufacturing industries around the world. The cost of unacceptable MDF was as large as 5% to 10% of total manufacturing costs. Prevention can result In millions of US dollars saved by using better Information systems.

• 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.