• International conference on construction engineering and project management
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• 2009.05a
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• pp.1540-1547
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• 2009

Steel frame factories were surveyed in this study in order to explore the possibility of shortening the construction time and save on construction cost through overlapping at the stages of design or construction. In the survey, construction professionals were interviewed in order to collect quantitative data. Hypotheses were then formulated, and the data was thereby analyzed using the simulation technique in order to analyze the effects of the concurrent engineering method on shorter construction time and cost saving. In addition, actual cases were analyzed to determine the overlapping rates of major processes in terms of shorter construction time and cost saving and to analyze the relationship between time and cost due to overlapping.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.393-400
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• 2022

Agent-based modeling (ABM), as a relatively new simulation technique, has recently gained in popularity in the civil engineering domain due to its uniquely advantageous features. Among many civil engineering applications, ABM has been applied to facility operation and management, such as energy consumption management, as well as the enhancement of maintenance and repair processes. The former studies used ABM to manage energy consumption through simulating human energy-related behaviors and their interactions with facilities, as well as electrical, heating, and cooling systems and appliances, while the latter used ABM to enhance maintenance process through facilitating coordination, negotiation, and decision making between facility managers, service providers, and repair workers. The present study aims to provide a short qualitative review on the most recent applications of ABM in the above-mentioned areas. Based on the review and follow-up analysis, the study identifies the advantages, disadvantages, and limitations of ABM applications to facility operation and management, and offers several potential future research topics in the hope of filling the existing literature gaps.

• Advances in nano research
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• v.16 no.5
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• pp.521-529
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• 2024

The integration of nanoparticles into various industries has spurred interest in understanding their impact on logistics and transportation systems. In this study, we investigate the effect of nanoparticles on the economic aspects of railway logistics transport using a mathematical model. By incorporating factors such as transportation costs, time efficiency, and environmental considerations, we aim to assess the overall economic feasibility of integrating nanoparticles into railway logistics operations. Through mathematical modeling and analysis, we explore how the introduction of nanoparticles affects cost-benefit analyses, resource allocation, and decision-making processes within railway logistics. Our findings provide valuable insights into the economic implications of nanoparticle integration in railway transport, offering potential strategies for optimizing logistics operations and enhancing overall efficiency and sustainability.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.77-84
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• 2024

The significance of welding quality cannot be overstated in ensuring the structural integrity of steel constructions. However, welding operations are inherently intricate, influenced by numerous variables. This paper introduces a novel welding digital twin framework grounded in a dynamic knowledge base. This framework serves to visualize the welding procedures and forecast optimal welding parameters. Such insights facilitate informed decision-making by operators, thereby enhancing both the quality and efficiency of welding processes. Furthermore, the study employs the welding of H-beam steel as an example, wherein a digital twin welding model is established, based on which the overall welding quality can be improved.

• Korean Journal of Construction Engineering and Management
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• v.12 no.4
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• pp.50-58
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• 2011

Lean concept is management philosophy that defines a customer's value and eliminates wasteful and impeditive factors. Management philosophy of Lean in the construction industry is referred to as "Lean Construction". Now this concept has expanded to achieve effective productivity during the design phase. Currently the norm of the domestic design process has been Point-based Design(PBD). It involves selecting a single structurally-feasible design option early and then refining that single design as more information becomes available throughout the design process. This single design is then re-worked until a solution is found that is feasible for all parties. On the contrary, Set-based Design(SBD) is based on lean processes to eliminate waste and improve project productivity. It focuses on keeping the design space as open as long as possible, to allow "subdesign" to advance and not labeling them as secondary in importance. Preserving the maximum number of feasible designs as long as possible reduces the likelihood that rework will be necessary and allows all project participants to utilize their unique expertise to make the project successful. This study proposes that the design methodology of minimizing waste and increasing productivity through SBD of AHP, one of the decision making process so as to compare PBD with SBD and tries to find decision making process and then suggest that application methodology through performs case study of SBD process.

• Journal of the Korean Society of Systems Engineering
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• v.11 no.2
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• pp.1-11
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• 2015

During the industrial plant system life cycle, required technologies are developed and assessed to analyze their performance, risks and costs. The assessment is called technology readiness assessment (TRA) and the measure of readiness is called technology readiness level (TRL). The TRL consists of 9 levels and through the TRL assessment, the technology to be developed and its components are assigned to their appropriate TRL. TRL assessment should be performed in each life cycle stages to monitor the technology readiness and analyze the potential risks and costs. However, even though the concept of TRL has been largely adopted by numerous organizations and industry, direct and clear assignment of target TRL for each life cycle stage has been overlooked. Direct mapping/assignment of target TRL for each life cycle has benefits as follow: (1) the technical risks condition of each life cycle stage can be better understood, (2) cost incurred if the technology development is failed can be analyzed in each life cycle stage, and (3) more effective decision making because the technology readiness achievement for each life cycle stages is agreed beforehand. In this paper, we propose a steel-making plant system life cycle and TRL assignment to each of the system life cycle stage. By directly assigning target TRL for each life cycle stages, we look forward to a more coordinated (in terms of exit criteria) and highly effective (in terms of technical risks identification and eventually prevent project failure) technology development and assessment processes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.181-190
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• 2010

The purpose of this study is to propose an effective approach of making route decisions for the large-scale national project (i.e. the KTX Gyeongbu line between Daegu and Busan) by analyzing the conflicts that had occurred in the process of selecting the routes and stations. This study investigated policy making processes and conflicts during the construction of the KTX Gyeongbu line. The five contentious issues were 1) selecting the route between Daegu~Busan line, 2) developing a new route through Gyeongju areas, 3) making decisions of the first and second phases of construction, 4) setting up an extra station in Ulsan, and 5) conflicts of the Mt. Cheonseong route in Yangsan. In terms of analyzing these issues, the current study focuses on dealing with notions of technical, economical, legal, social, and practical rationality. Based on such analyses and implications, this study proposes a relatively reasonable model of line decision making especially for long-distance intercity routes. It also turns out that the large-scale project like the development of KTX Gyeongbu line should be conducted after receiving enough quantitative evaluation by reviewing relevant technical research studies and economic analyses, and having social agreements to prevent plausible conflicts.

• Journal of Information Technology Services
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• v.19 no.1
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• pp.103-112
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• 2020

Job seekers are making various efforts to find a good company and companies attempt to recruit good people. Job search activities through self-introduction essay are nowadays one of the most active processes. Companies spend time and cost to reviewing all of the numerous self-introduction essays of job seekers. Job seekers are also worried about the possibility of acceptance of their self-introduction essays by companies. This research builds a classification model and conducted an experiments to classify self-introduction essays into pass or fail using deep learning and decision tree techniques. Real world data were classified using stratified sampling to alleviate the data imbalance problem between passed self-introduction essays and failed essays. Documents were embedded using Doc2Vec method developed from existing Word2Vec, and they were classified using logistic regression analysis. The decision tree model was chosen as a benchmark model, and K-fold cross-validation was conducted for the performance evaluation. As a result of several experiments, the area under curve (AUC) value of PV-DM results better than that of other models of Doc2Vec, i.e., PV-DBOW and Concatenate. Furthmore PV-DM classifies passed essays as well as failed essays, while PV_DBOW can not classify passed essays even though it classifies well failed essays. In addition, the classification performance of the logistic regression model embedded using the PV-DM model is better than the decision tree-based classification model. The implication of the experimental results is that company can reduce the cost of recruiting good d job seekers. In addition, our suggested model can help job candidates for pre-evaluating their self-introduction essays.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.413-413
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• 2010

In semiconductor manufacturing field, all equipments have various sensors to diagnosis the situations of processes. For increasing the accuracy of diagnosis, hundreds of sensors are emplyed. As sensors provide millions of data, the process diagnosis from them are unrealistic. Besides, in some cases, the results from some data which have same conditions are different. We want to find some information, such as data and knowledge, from the data. Nowadays, fault detection and classification (FDC) has been concerned to increasing the yield. Certain faults and no-faults can be classified by various FDC tools. The uncertainty in semiconductor manufacturing, no-faulty in faulty and faulty in no-faulty, has been caused the productivity to decreased. From the uncertainty, the rough set theory is a viable approach for extraction of meaningful knowledge and making predictions. Reduction of data sets, finding hidden data patterns, and generation of decision rules contrasts other approaches such as regression analysis and neural networks. In this research, a RGB sensor was used for diagnosis plasma instead of optical emission spectroscopy (OES). RGB data has just three variables (red, green and blue), while OES data has thousands of variables. RGB data, however, is difficult to analyze by human's eyes. Same outputs in a variable show different outcomes. In other words, RGB data includes the uncertainty. In this research, by rough set theory, decision rules were generated. In decision rules, we could find the hidden data patterns from the uncertainty. RGB sensor can diagnosis the change of plasma condition as over 90% accuracy by the rough set theory. Although we only present a preliminary research result, in this paper, we will continuously develop uncertainty problem solving data mining algorithm for the application of semiconductor process diagnosis.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.93-108
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• 2023

In manufacturing productivity analysis, understanding the intricate interplay among factors like facility performance, layout design, and workforce allocation within the production line is imperative. This paper introduces a simulation-based methodology tailored to food manufacturing, progressively expanding scenarios to analyze production enhancement. The target system is a food processing plant, encompassing production processes, including warehousing, processing, subdivision, packaging, inspection, loading, and storage. First, we analyze the target system and design a simulation model according to the actual layout arrangement of equipment and workers. Then, we validate the developed model reflecting the real data obtained from the target system, such as the workers' working time and the equipment's processing time. The proposed model aims to identify optimal factor values for productivity gains through incremental scenario comparisons. To this end, three stages of simulation experiments were conducted by extending the equipment and worker models of the subdivision and packaging processes. The simulation experiments have shown that productivity depends on the placement of skilled workers and the performance of the packaging machine. The proposed method in this study will offer combinations of factors for the specific production requirements and support optimal decision-making in the real-world field.