自從全球化經濟時代的來臨，政府產業民營化、國際化政策的發動下，油品市場的競爭日益激烈，各個煉油業者莫不在尋找降低生產成本並提高產能的最佳方案，因此，儲油槽利用率的優化已成為流程管理上的重要議題。煉油業者在安排儲油槽維修時程時，所欲追求的目標不只是滿足消費者對石油量的需求，還要降低維修時所必須付出的成本，因此，在本研究中將多目標的概念同時納入問題中，期望可以同時滿足維修排程問題中多個目標的需求。然而，由於問題過高的複雜度，傳統上由專家依其經驗對維修任務進行排程可能已不適用，所以希望能藉由資訊科技的技術完成維修任務的排程問題。
　　維修排程問題的本質為NP-Hard問題，近來螞蟻族群最佳化演算法(Ant Colony Optimization, ACO)已經被成功地應用來解決各式NP-Hard問題，且其績效不比傳統的啟發式演算法差，但還具有某些可以改善的地方。因此，本研究將先探討螞蟻演算法的限制與缺陷，針對螞蟻演算法本身的缺失進行架構上的改良，並與原本的螞蟻族群最佳化演算法做績效上的比較，以證實改良式螞蟻演算法確實有較佳的表現。最後，將之應用於求解多目標儲油槽維修排程問題而獲得一非劣解集合，此一解集合則可幫助決策制訂者能於短時間內從龐大的可行解集合中制訂出最理想的排程規劃。

　　With the coming of globalization and privatization, the oil market in Taiwan becomes more and more competitive today than before. The best solution to maximize productivity and to minimize operating costs is explored by each petroleum corporation for keeping competitiveness. Therefore, the utilization ratio of oil tanks has already been an important issue and a suitable maintenance schedule of oil tanks has become the source of enterprise's profit and competitiveness. However, the objective of maintenance scheduling is not only to maximize the oil supply level, but also to minimize the maintenance costs. The complex conditions make domain experts difficult to arrange maintenance tasks properly. So the information techniques are proposed to support maintenance scheduling problem in this research.
　　Maintenance problem is essentially a NP-Hard problem. Ant Colony Optimization (ACO) algorithm has already succeeded in applying to solve various kinds of NP-Hard problems and is proved better performance than other classical heuristic algorithms. However, there are some drawbacks with ACO algorithm. In this thesis, we try to enhance the ACO algorithm after literature reviewing and then apply it to solve the multiple-objective maintenance scheduling problem of oil tanks to find out a Pareto solution set. This non-dominanced solution set will make decision makers easy to arrange the maintenance scheduling in the large number of solutions.

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