• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.174.1-174.1
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• 2012

현재 인공위성 개발 프로젝트들을 계획하고 일정계획을 수립하기위해 간트차트가 널리 활용되고 있다. 간트차트는 프로젝트의 각 작업들이 언제 시작하고 종료되는지에 대한 작업 일정을 막대 도표를 이용하여 표시하는 프로젝트 일정표로 다양한 형태로 변경하여 사용할 수 있으나 작업경로를 표시할 수 없으며 계획의 변화에 대한 적응성이 약한 단점이 있다. 또한 일목요연하게 눈으로 보여줄 수 있으나 효과적인 프로젝트 관리에 중요한 활동 사이의 어떤 관계에 대한 정보를 주지 못한다. 인공위성개발과 같은 복잡한 프로젝트에 대해 간트차트와 더불어 PERT(Program evaluation and review technique)와 CPM(critical path method)과 같은 네트워크 도(Network Diagram)와 함께 사용될 수 있도록 그 이론과 활용방안에 대해 기술하고자 한다. PERT와 CPM은 큰 프로젝트를 계획하고 조정하기 위해 폭넓게 사용되는 두 가지 기법이다. PERT와 CPM을 사용하면 프로젝트 활동에 대한 그래프를 통한 도시, 프로젝트 소요시간 추정, 프로젝트 완료시간 준수를 위해 중요한 활동의 식별, 전체 프로젝트에 대한 지연 없이 가능한 각 활동의 지연시간 추정과 같은 이점이 있다. PERT와 CPM은 독립적으로 개발되었지만, 많은 공통점이 있다. 더 나아가서 둘 사이에 원래 존재했던 차이점은 많은 부분은 서로의 특징을 도입하면서 거의 사라졌다. 실제적으로 볼 때, 두 기법은 지금 같은 기법이며, 기술된 특징과 절차는 PERT분석 뿐 아니라 CPM 분석에도 적용될 수 있을 것이다.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.1-12
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• 2020

Recently, research using machine learning have shown remarkable results in various domains, leading to the fact that leaning-based dispatchers have intrigued interest in both academia and industry. To improve the performance of the dispatcher, each dispatch decision needs to be evaluated in detail. However, existing studies on visualization techniques for manufacturing lines have mainly focused on illustrating the performance indicators or abnormal patterns. In this paper, we propose a monitoring system that displays a variety of information about the manufacturing line along with alternatives at the time of each dispatching decision being made. Furthermore, the proposed system effectively represents the cause of the idle time of resources and the change of the performance index over time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.3
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• pp.669-677
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• 2010

In this paper, we propose a novel project schedule notification and issue tracking system based on a social networking service for project management on a smartphone. The proposed system has a server subsystem and a client subsystem. The server is in charge of enabling a deadline notification and an issue tracking of the project to project participants by exploiting a legacy social networking service. The client running on a smartphone displays timelines of the project schedule using Gantt chart and let the project participant edit their schedule. The proposed system combines the mobility of smartphones and the connectivity of social networking services and apply them to schedule notification and issue tracking, which demonstrates a novel usage of social networking services.

• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.15-27
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• 2020

This study starts with the idea that the process of creating a Gantt Chart for schedule planning is similar to Tetris game with only a straight line. In Tetris games, the X axis is M machines and the Y axis is time. It is assumed that all types of orders can be worked without separation in all machines, but if the types of orders are different, setup cost will be incurred without delay. In this study, the game described above was named Gantris and the game environment was implemented. The AI-scheduling table through in-depth reinforcement learning compares the real-time scheduling table with the human-made game schedule. In the comparative study, the learning environment was studied in single order list learning environment and random order list learning environment. The two systems to be compared in this study are four machines (Machine)-two types of system (4M2T) and ten machines-six types of system (10M6T). As a performance indicator of the generated schedule, a weighted sum of setup cost, makespan and idle time in processing 100 orders were scheduled. As a result of the comparative study, in 4M2T system, regardless of the learning environment, the learned system generated schedule plan with better performance index than the experimenter. In the case of 10M6T system, the AI system generated a schedule of better performance indicators than the experimenter in a single learning environment, but showed a bad performance index than the experimenter in random learning environment. However, in comparing the number of job changes, the learning system showed better results than those of the 4M2T and 10M6T, showing excellent scheduling performance.