유전알고리즘을 활용한 자원평준화 방법론

다국어 초록 (Multilingual Abstract)

Resource leveling minimizes resource fluctuations by deferring the earliest start times (ESTs) of non-critical activities within their corresponding total float. The intentional float-consumption for resource leveling purpose reduces the schedule delay contingency. This paper presents a method called Genetic Algorithm based Resource Leveling (GARL) that minimizes resource fluctuations and float-consumption impact over project duration. It identifies activities that are less sensitive to float-consumption and performs resource leveling using those activities. The study is of value to project scheduler because GARL identifies the set of activities to be deferred and the number of shift day(s) of each and every activities in the set within its total float expeditiously. It contributes to establish a baseline schedule which implements an optimal resource leveling plan. A case study is presented to verify the validity and usability of the method. It was confirmed that GARL satisfies the project duration constraint by considering resource fluctuations and float-consumption over project duration.

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 문헌고찰
• 3. 자원요동 최소화 및 프로젝트 조달확률 최대화를 고려하는 자원평준화 시스템 (GARL)
• 4. 사례적용 및 분석
• 5. 결론
• REFERENCES

참고문헌 (Reference)

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