在機場的眾多作業中，機門指派，被視為是相當重要的任務之一。它關注的是將機門指派給航班提供旅客上下機，以及分配位置處理從飛行開始到結束的各項相關作業(如：安全檢查、機械維修和旅客上下機等)。本研究的目標，旨在降低航班間發生機門衝突的機率藉此提升指派的穩健性，同時，有效的提升機場設施的使用率。故一個多目標的指派模型在本研究中被提出，希望同時極小化發生機門衝突的機率和指派到停機坪的航班數量。

在大多數的多目標問題中，需要先了解決策者對目標的偏好，給予權重後轉為單一目標進行運算和評估，提供的僅僅是單一最佳解。但在實務問題上，存在一條無法被超越的柏拉圖曲線，在不犧牲任一個目標的情況下，同時追求其他目標的改進。因此，本研究將利用以改良式簡化群體演算法(improved Simplified Swarm Optimization, iSSO) [1] 為基礎的多目標算法，非支配排序改良式簡化群體演算法(Non-dominated Sorting improved Simplified Swarm Optimization, NSiSSO)，將非支配排序的概念融於改良式簡化群體演算法中，提供決策者一條無法被超越的解集合，提出多項可替代的等效益方案。

本研究所利用的方法，將和其他多目標的進化式算法進行比較，如非支配排序基因演算法(Non-dominated Sorting Genetic Algorithm) [2]、非支配排序粒子群演算法(Non-dominated Sorting Particle Swarm Optimization) [3]。以實驗結果來看，本研究所使用的方法，不論在曲線上解集合的多樣性和品質上，都有不錯的表現。

Gate Assignment, which is considered one of the most important tasks in airport operations. It focuses on assigning gates to aircrafts while providing passengers embark and disembark, and allocating positions to process all the operations related to the flight from the start to the end. Two factors having significant impact on gate assignment are considered in this research: schedule robustness and facility utilization. A multi-objective assignment model is proposed, with the aim to decrease the conflict probability between two flights and the number of aircrafts assigned to the parking apron simultaneously.

In most of the multi-objective problems, we need to predetermined preference by the decision maker. Giving the objectives weights and then transform to a single objective problem for calculation and evaluation. Providing only one single best solution. However, in multi-objective problem, there is a Pareto set of non-dominated solutions and both objectives should be advanced simultaneously without sacrificing any of them. In this research, a multi-objective evolutionary algorithm based on improved Simplified Swarm Optimization is used, called Non-dominated sorting Simplified Swarm Optimization (NSiSSO). We integrated the concept of non-dominated sorting into the improved Simplified Swarm Optimization. Providing the decision maker a Pareto set of solutions that can’t be surpass.

Compare to other multi-objective evolutionary algorithm, Non-dominated sorting Genetic Algorithm (NSGA-II), Non-dominated sorting Particle Swarm Optimization (NSPSO). Numerical results show that the proposed algorithm can obtain a Pareto set in terms of quality and diversity compare with other existing algorithms.

摘要 I
Abstract II
目錄 1
圖目錄 3
表目錄 5
第一章、 緒論 6
1.1 研究背景與動機 6
1.2 研究目的 7
1.3 研究架構 9
第二章、 文獻回顧 11
2.1 機門指派問題 11
2.2 穩健性和多目標機門指派問題 13
2.3 多目標進化式算法 14
2.4 簡化群體演算法 16
第三章、 數學模型 18
3.1 數學符號 18
3.2 模型假設 19
3.3 目標函數和限制條件 19
第四章、 研究方法 22
4.1 粒子編碼方式 22
4.2 初始解生成方式 23
4.3 適應值函數 26
4.4 非支配排序的概念 26
4.5 改良式簡化群體演算法 28
4.6 NSiSSO更新流程 29
第五章、 實驗結果 32
5.1 實驗資料 32
5.2 多目標問題的評估指標 32
5.3 實驗設計 34
5.4 實驗結果與分析 44
5.5 統計驗證 53
第六章、 結論與未來展望 56
6.1 結論 56
6.2 未來展望 56
第七章、 參考文獻 58

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