當每日貨物按照順序從發送站運送至接收站，且客戶對交付時間有特定要求時，妥善的配送路線規劃變得格外重要。有效的規劃不僅能在既有的時間窗口限制下實現最佳配送，還能在最大化運載量的同時，設計出最有效率的車輛路線，進而降低配送成本。因此，本研究聚焦於在時間與重量雙重限制下的物流路徑規劃問題。研究所面臨的挑戰包括：(1)在固定車輛數量下，最大化貨物載運量並最小化行駛總里程；(2)在進行車輛路線規劃時，除須考量車輛容量外，還需納入客戶指定的交付時間範圍；(3)問題本身屬於NP-Hard，具高度計算複雜性；(4)所有貨車需自中央配送站出發並最終返回，路線須構成單一封閉迴圈，嚴禁出現子路線。針對貨物裝載分配問題，本研究採用Next Fit演算法進行處理；而在路線規劃方面，經比較後選擇A*演算法，並結合整數線性規劃(Integer Linear Programming, ILP)以施加限制並優化解法。最後，參考Solomon分類基準問題驗證本研究成果的效能。

When goods are delivered sequentially each day from the dispatch station to the receiving station, and customers have specific delivery time requirements, effective route planning becomes essential. Proper planning not only enables optimal delivery within the given time window constraints, but also maximizes cargo loading while designing the most efficient vehicle routes, thereby reducing transportation costs. This research focuses on solving the logistics routing problem under both time and weight constraints. The key challenges include: (1) maximizing cargo loading capacity and minimizing total travel distance within a fixed number of vehicles; (2) considering customer-specified delivery time windows in addition to vehicle capacity during route planning; (3) dealing with the NP-Hard nature of the problem, which involves high computational complexity; and (4) ensuring that all vehicles depart from and return to a central distribution station, forming a single closed-loop route with no sub-routes allowed. To address the cargo allocation problem, this study adopts the Next Fit algorithm. For route planning, the A* algorithm is selected after comparative analysis and is further integrated with Integer Linear Programming (ILP) to impose constraints and optimize the solution. Finally, the effectiveness of the proposed approach is validated using Solomon’s benchmark problems for vehicle routing with time windows.