本研究試圖提出兩種新式的演算法: RMAPSO+NNS 及 RMAPSO+PSRNNS來求解二階層位置路徑規劃問題上運用動態衛星之問題 (Two-echelon location routing problem, 2E-LRPDS). 此兩種方法均運用隨機多點啟動粒子群最佳化作為求解的架構。為了決定第二階城巿貨車（city freighter, CF）的路徑，RMAPSO+NNS結合了最鄰近搜尋法（Nearest Neighbor Search, NNS）來找尋離城巿貨車最近的運送地點。為了進一步平衝多個城巿貨車的總運送距離，RMAPSO+PSRNNS進一步將NNS的結果進行交錯佈置。此兩者方法均在Prodhon設計之二階層位置路徑規劃基準問題集之最佳解進行比較。實驗結果顯示本研究所提出的方法在此動態衛星架構下大量顧客問題中可以有效降低整體運輸成本。其中，在較大的問題中，RMAPSO+NNS可節省約16%的運輸成本。

In this thesis, two new methods are proposed, named RMAPSO+NNS and RMAPSO+PSRNNS, to solve the two-echelon location routing problem with dynamic satellites (2E-LRP-DS). These two methods commonly consist of a novel approach, where a new and modified particle swarm optimization algorithm, called “Random Multi-Start Adaptive Particle Swarm Optimization algorithm” (RMAPSO), is implemented to solve 2E-LRP-DS problem. For determining the local route of city freighter vehicle (CF), the second echelon, RMAPSO+NNS simply utilizes Nearest Neighbor Search (NNS) to search closer customers for delivering. In order to balance the traveling distances among CFs, RMAPSO+PSRNNS further interlaces the routing results among CFs. Experiment tests were carried on all of the benchmark Prodhon instances. Results show that the proposed approaches improve and outperform the most of benchmark results. Particularly for instances with larger problems, the proposed RMAPSO+NNS can reduce around 16% in traveling cost.

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