• 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.

• KDI Journal of Economic Policy
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• v.32 no.2
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• pp.144-170
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• 2010

This paper considers the effect of various types of procurement auction system on competition focusing on the rate of successful bidding. We analyze the number of bidders and the rate of successful bids using online procurement data of the Public Procurement Service. The average number of bidders is 301 and the average rate of successful bids is 87.42% while the weighted average rate is 75.13%. These numbers show that there is quite strong competition among bidders and the rate is lower as the expected price is higher. When we analyze the data of price procurement auction, the rate is also shown to be lower as the expected price is higher. Furthermore, the rate decreases as the number of bidders increases which naturally makes the competition stronger. Meanwhile, the analysis finds that the inclusion of the onsite bidding, the PQ(Pre-Qualification) result, or major-10 winning companies cannot explain the rate much in our data. In case of turnkey-alternative, the average rate of successful bidding for 484 cases record 90.20%. The average is 84.89% with 120 alternatives and 91.97% with 364 cases of turnkey. The reason why the rate of turnkey-alternative is lower than that of price procurement auction is the lack of competition as well as the systematic difference. By setting up a model, we are able to explain the difference in rate caused by the respective reason. When we suppose there are 3 bidders in case of price procurement auction for a project that exceeds 100 billion won, the rate is expected to be around 64%. This implies that difference of 26% is caused by the systemic difference and 3% by the lack of competition. Therefore, we conclude that the difference in rate between turnkey-alternative and price procurement auction is caused mainly by the systemic difference. In case of PPP(Public Private Partnership) projects, among 154 projects in total, only 40% has more than 2 bidders that compete. The average number of bidders is 1.88 which is less than 2, and the average rate of successful bids is 90%. In sum, under the price procurement auction, there is strong competition which is reflected by the rate of successful bids. However, there is room to decrease the rate by strengthening the competition under the turnkey-alternative. Also with PPP projects, we expect the rate can be steadily reduced with revived competition among bidders.