在實務上，CY貨櫃運輸是利用貨櫃車以整櫃式的方式，把貨櫃往來運送於貨主倉庫與貨櫃集散站之間。近年由於客製化服務興起，加上台北港建設完成，降低北部貨櫃從高雄港進出口的需求，貨櫃運輸的模式在島內逐漸由長程的南北轉運，轉變成短程的CY貨櫃運輸。然而，CY貨櫃運輸的貨櫃車在行駛路徑規劃上複雜，還必須因應不同的需求，提供客製化的服務。現行的貨櫃車派遣是以人力為主，但隨著CY貨櫃運輸的需求上升，再加上其運輸路徑的複雜，已成為一個棘手的問題。
本研究把CY貨櫃運輸模式建構為多場站且考量時窗限制的多旅行銷售員問題(Multi-Depot m-TSPTW)。在過去文獻中，主要是以貨櫃車總空車行駛時間最小化為目標進行求解，本研究考量了實務上的狀況，加入了最小化駕駛工作差異的目標，欲減少駕駛之間的工作負荷差異。為求解此雙目標問題，本研究先使用文獻上常用的禁忌演算法與基因演算法，求解以總空車行駛時間最小化為目標的CY貨櫃運輸問題，以期能從這兩種啟發式演算法當中，選出適合實務的貨櫃車路徑規劃演算法。根據上述兩種演算法的比較結果，本研究採用以基因演算法為架構的NSGAII發展多目標啟發式演算法，求解最小化總空車行駛時間與最小化駕駛工作差異的雙目標問題。最後再利用實務上的資料，分析此演算法在不同資料量下的求解情形。
分析結果發現，本研究發展的多目標啟發式演算法在滿足時窗限制、駕駛工時限制……等條件下，隨著資料數量的增加，演算法的求解情形依然穩定，足證此演算法能夠求解雙目標的CY貨櫃運輸問題，並適用於各種不同規模的問題。

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