Project/Area Number |
20K01897
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 07080:Business administration-related
|
Research Institution | Doshisha University |
Principal Investigator |
殷 勇 同志社大学, ビジネス研究科, 教授 (50344776)
|
Co-Investigator(Kenkyū-buntansha) |
伊藤 嘉浩 長岡技術科学大学, 工学研究科, 教授 (60436235)
野田 英雄 東京理科大学, 経営学部ビジネスエコノミクス学科, 教授 (90347724)
|
Project Period (FY) |
2020-04-01 – 2024-03-31
|
Project Status |
Granted (Fiscal Year 2022)
|
Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
Keywords | seru production / smart manufacturing / セル生産 / Smart factory / Industry 4.0 / seru production system / smart factory / robots / IoT / Production Systems |
Outline of Research at the Start |
A hot term about tomorrow’s production is smart manufacturing, which is one of the cores to realize the next industrial revolution, for example, Society 5.0 by Japanese government. Many Japanese factories are in the process of adopting smart factories based on their current production system. Seru Production Systems (セル生産システム, hereafter SPS) are one of the most applied systems in Japan’s industries. This research proposal studies how to realize a smart factory with SPS.
|
Outline of Annual Research Achievements |
In accordance with our research proposal, we have successfully generated an array of problems related to seru formation and loading by varying different input elements. These problem sets were used to analyze computational complexities, identifying whether each problem was NP-hard or polynomial and determining its convexity. As anticipated, the majority of the seru problems were NP-hard and non-convex. For the NP-hard problems, we found achievable approximation ratios and subsequently developed polynomial algorithms to obtain solutions with optimal approximation ratios. For non-convex problems, we designed algorithms that can converge to local optimal results.
The process of constructing mathematical models necessitated the identification of key elements relevant to smart manufacturing. These elements were derived from various sources, such as industrial conferences like the Annual Conference of Japan Institute of Industrial Engineering, studying industrial cases in Japanese and English journals and magazines, and engaging in discussions with our MBA students who have direct factory experience.
In conclusion, the work undertaken over the past year represents significant strides in analyzing and modeling seru formation and loading problems. We've adopted a systematic approach that leverages a broad array of input variables to generate detailed mathematical models. We remain committed to the ongoing exploration of this subject, and we look forward to the breakthroughs that the coming year will bring.
|
Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Just as planned.
|
Strategy for Future Research Activity |
This year is the final year. Several papers will be submitted to journals.
|