Abstract
Due to the entry of Taiwan into WTO and the recently liberalized Petroleum Management Law, the oil market in Taiwan is liberalized and thus is becoming more competitive. However, the space limitation and the residents' increasing awareness of environmental protection issues in the island make international vendors unavoidably have to rent tanks from domestic oil companies. In order to help the leaseholder maximize revenue by increasing the availability of tanks, an efficient maintenance scheduling is needed. This paper introduces a tabu-based genetic algorithm (TGA) and its implementation for solving a real-world maintenance scheduling problem of oil storage tanks. TGA incorporates a tabu list to prevent inbreeding and utilizes an aspiration criterion to supply moderate selection pressure so that the selection efficiency is improved, and the population diversity is maintained. The experimental results validate that TGA outperform GA in terms of solution quality and convergence efficiency.
| Original language | English |
|---|---|
| Pages (from-to) | 209-215 |
| Number of pages | 7 |
| Journal | Proceedings of the International Conference on Tools with Artificial Intelligence |
| Publication status | Published - 2002 Dec 1 |
| Event | 14th International Conference on Tools with Artificial Intelligence - Washington, DC, United States Duration: 2002 Jun 4 → 2002 Nov 6 |
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All Science Journal Classification (ASJC) codes
- Software
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Maintenance scheduling of oil storage tanks using Tabu-based genetic algorithm. / Li, Sheng-Tun; Ting, Chuan Kang; Lee, Chungnan; Chen, Shu Ching.
In: Proceedings of the International Conference on Tools with Artificial Intelligence, 01.12.2002, p. 209-215.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Maintenance scheduling of oil storage tanks using Tabu-based genetic algorithm
AU - Li, Sheng-Tun
AU - Ting, Chuan Kang
AU - Lee, Chungnan
AU - Chen, Shu Ching
PY - 2002/12/1
Y1 - 2002/12/1
N2 - Due to the entry of Taiwan into WTO and the recently liberalized Petroleum Management Law, the oil market in Taiwan is liberalized and thus is becoming more competitive. However, the space limitation and the residents' increasing awareness of environmental protection issues in the island make international vendors unavoidably have to rent tanks from domestic oil companies. In order to help the leaseholder maximize revenue by increasing the availability of tanks, an efficient maintenance scheduling is needed. This paper introduces a tabu-based genetic algorithm (TGA) and its implementation for solving a real-world maintenance scheduling problem of oil storage tanks. TGA incorporates a tabu list to prevent inbreeding and utilizes an aspiration criterion to supply moderate selection pressure so that the selection efficiency is improved, and the population diversity is maintained. The experimental results validate that TGA outperform GA in terms of solution quality and convergence efficiency.
AB - Due to the entry of Taiwan into WTO and the recently liberalized Petroleum Management Law, the oil market in Taiwan is liberalized and thus is becoming more competitive. However, the space limitation and the residents' increasing awareness of environmental protection issues in the island make international vendors unavoidably have to rent tanks from domestic oil companies. In order to help the leaseholder maximize revenue by increasing the availability of tanks, an efficient maintenance scheduling is needed. This paper introduces a tabu-based genetic algorithm (TGA) and its implementation for solving a real-world maintenance scheduling problem of oil storage tanks. TGA incorporates a tabu list to prevent inbreeding and utilizes an aspiration criterion to supply moderate selection pressure so that the selection efficiency is improved, and the population diversity is maintained. The experimental results validate that TGA outperform GA in terms of solution quality and convergence efficiency.
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M3 - Conference article
AN - SCOPUS:0036924958
SP - 209
EP - 215
JO - Proceedings of the International Conference on Tools with Artificial Intelligence
JF - Proceedings of the International Conference on Tools with Artificial Intelligence
SN - 1063-6730
ER -