本研究將考量多種產品和多種船型並存在靠港限制情境下的船舶途程規劃問題，同時也將船舶的裝卸貨與航行時間加入考量，在過去研究中很少同時將這些限制與條件加入考慮，而本研究將這些同時加入考量，且在加入考量後能更貼近實務上所面臨的問題。本研究將提出以混合型粒子群演算法求解船舶途程規劃問題會有較佳的求解品質與效率，在實驗中也顯示和傳統的基因演算法與粒子群演算法相比能有更好的求解效率與品質。
在過去文獻中，大多數船舶途程規劃問題在需求上都是確定的，並未考量顧客需求是具有隨機性的現象，故本研究也將探討隨機需求下的船舶途程規劃問題。但是顧客的需求是具有變異性的，因此必須透過多次模擬以消除隨機性所帶來的干擾，本研究將利用模擬預算最佳分配(Optimal Computing Budget Allocation, OCBA)進行模擬資源分配，有效率地分配抽樣次數以減少運算時間和模擬成本，並且可有效的提升求解品質。
到目前為止，有關船舶途程規劃的研究大多都是在已知取貨地點的情況下進行求解，極少將多廠區訂單分配問題加入考量，因此，本研究將探討將訂單分配題加入於船舶途程規劃問題進行共同求解，並利用回饋式演算法進行求解，實驗結果顯示將訂單分配問題加入考量後能夠更全面性地評估各個方案，使得績效值能夠有顯著提升。

Ship routing problem (SRP) is an important and well-known combinatorial optimization problem encountered in many transport logistics and distribution systems. The SRP has several variants depending on some restrictions such as time window, multiple vessels and so on. In this research, considering the ship routing problem with multi-product, heterogeneous vessel and having loading and port constraints The problem is to find an optimal assignment of the ship routing and loading volume of demand simultaneously in order to minimize the total cost satisfy capacity of ships.
Since SRP is an NP-hard problem, we propose a hybrid particle swarm optimization (HPSO) to solve ship routing problem. HPSO is an improved algorithm based on particle swarm optimization (PSO) incorporated with crossover and mutation operators can provide better solving quality. The performance of the proposed method is compared with genetic algorithm (GA) and particle swarm optimization (HPSO). The experimental results show that the proposed algorithm exhibits good performance and solving effectiveness for the test problem.
In the real situation, the demand of customers are not constant. It would change by temporary or seasonal demand. Therefore, we consider stochastic demands of customers. We apply optimal computing budget allocation (OCBA) to allocate simulation resource. It can allocate simulation resource efficiently and reduce solving time.

第一章 緒論
1.1 研究背景與動機
1.2 研究目的
1.3 研究範圍
1.4 研究步驟與方法
第二章 文獻回顧
2.1 船舶途程規劃問題
2.2 車輛路徑規劃問題
2.3 多廠區訂單分配問題
2.4 啟發式演算法
2.4.1 基因演算法
2.4.2 粒子群演算法
2.5 模擬最佳化
2.5.1 資源分配最佳化
第三章 船舶途程規劃問題之探討
3.1 問題定義
3.2 數學模型
3.3 啟發式演算法求解
3.3.1 基因演算法
3.3.2 粒子群演算法
3.3.3 混合型粒子群演算法
3.4 實驗分析
3.4.1 實驗一、啟發式演算法與數學解之比較
3.4.2 實驗二、參數分析
3.4.3 實驗三、不同情境下之求解比較
第四章 隨機型船舶途程規劃問題之探討
4.1 船舶途程規劃問題之隨機因子
4.2 蒙地卡羅模擬
4.3 資源分配最佳化
4.4 啟發式演算法結合
4.5 實驗分析
4.5.1 實驗一、參數分析
4.5.2 實驗二、OCBA與Equal之比較與分析
第五章 考慮訂單分配問題於船舶途程規劃問題
5.1 問題定義
5.2 數學模型
5.3 回饋式演算法求解
5.3.1 基因演算法
5.4 實驗分析
5.4.1 實驗一、參數分析
5.4.2 實驗二、考量訂單分配問題之求解比較與分析
5.4.3 實驗三、不同求解方法之比較
第六章 結論與建議
6.1 結論
6.2 建議
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