프로그램레벨 다수 대학시설물 유지보수 일정계획 지원 모델 (Program-level Maintenance Scheduling Support Model for Multiple University Facilities)
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- 한국산학기술학회논문지
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- 제19권12호
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- pp.303-312
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- 2018
대학 시설은 다수의 건물들로 구성되어 많은 유지보수항목이 있고 한정된 기간 내에 처리해야 하는 관리상의 제약사항이 있다. 대부분의 유지보수 작업은 규모가 작으며 방수공사 등 다수 공종이 동시에 진행되는 특성을 가진다. 시설관리자는 유지보수업체와 연간단가계약을 맺고 공사를 추진하지만 기존 유지보수공사는 일정 및 인력투입계획 없이 공사가 진행되어 공사지연과 다수의 재작업이 발생하고 있다. 본 연구는 시설관리자가 다수의 대학 시설물 유지보수공사들을 프로그램 레벨로 관리할 수 있도록 지원하는 일정계획 모델을 제안하였다. 모델은 순차적으로 진행되는 3단계로 구성된다. 대상 분석단계에서는 수량산출서를 바탕으로 유지보수항목들의 세부공종을 분석하고 수량을 산출한다. 자원 분석단계에서는 효과적인 전문기능공의 투입을 위해 세부공종별 전문기능공 및 공사기간을 도출한다. 일정계획 수립단계에서는 공종별 우선순위와 최적 투입인력이 도출되고 적합도에 따라 일정계획 최적안을 선정한다. 사례적용결과 프로그램 레벨에서 다수 대학시설물 유지보수공사 간의 효과적인 인력투입이 가능하며, 노무비가 감소되어 모델 적용 전 보다 많은 수의 공사를 완료할 수 있었다. 실무자 면담을 통해 모델의 적용성 및 효과성을 평가한 결과 관리자의 유지관리공사 일정관리를 지원할 수 있는 효과적인 도구로 평가되었다.
Securities and investment services have and use large data. Investors started to invest through their own analysis methods. There are 22 major securities and investment companies in Korea and only 6 companies support open API. Python is effective for requesting and receiving, analyzing text data from open API. Daishin Securities Co. is the only open API that officially supports Python, and eBest Investment & Securities Co. unofficially supports Python. There are two important differences between CYBOS plus of Daishin Securities Co. and xingAPI of eBest Investment & Securities Co. First, we must log in to CYBOS plus to access the server of Daishin Securities Co. And the python program does not require a logon. However, to receive data using xingAPI, users log on in an individual Python program. Second, CYBOS plus receives data in a Request/Reply method, and zingAPI receives data through events. It can be thought that these points will show a difference in response time. Response time is important to users who use open APIs. Data were measured from August 5, 2021, to February 3, 2022. For each measurement, 15 repeated measurements were taken to obtain 420 measurements. To increase the accuracy of the study, both APIs were measured alternately under same conditions. A paired t-test was performed to test the hypothesis that the null hypothesis is there was no difference in means. The p-value is 0.2961, we do not reject null hypothesis. Therefore, we can see that there is no significant difference between means. From the boxplot, we can see that the distribution of the response time of eBest is more spread out than that of Cybos, and the position of the center is slightly lower. CYBOS plus has no restrictions on Python programming, but xingAPI has some limits because it indirectly supports Python programming. For example, there is a limit to receiving more than one current price.
Purpose - Despite the importance of price, many companies do not implement pricing policies smoothly, because typical price management strategies insufficiently consider logistics efficiency and an increase in logistics costs due to logistics waste. This study attempts to examine the effect of product line pricing, which corresponds to product mix pricing, on logistics efficiency in the case of manufacturer A, and analyzes how logistics performance changes in response to these variables. Research design, data, and methodology - This study, based on the case of manufacturer A, involved research through understanding the current status, analyses, and then proposing improvement measures. Among all the products of manufacturer A, product group B was selected as the research object, and its distribution channel and line pricing were examined. As a result of simulation, for products with low loading efficiency, improvement measures such as changing the number of bags in the box were suggested, and a quantitative analysis was conducted on how these measures influence logistics costs. The TOPS program was used for the Pallet loading efficiency simulation tool in this study. To prevent products from protruding out of the pallet, the maximum measurement was set as 0.0mm, and loading efficiency was based on the pallet area, and not volume. In other words, its size (length x width) was focused upon, following the purpose of this study and, then, the results were obtained. Results - As a result of the loading efficiency simulation, when the number of bags in the box was changed for 36 products with low average loading efficiency of 73.7%, as shown in