隨著人口日益增加，造成都市化的普及，人們可能會因自己的事業而需將整個家庭遷往他處，在搬遷的過程中，因個人能力有限，貨物的搬運會是個重要的議題，貨物不僅有大有小，有多有少，數量太多時，往往造成許多人自行搬運時需要來回數趟，造成許多的不便。因此大多數的人會請搬家業者進行搬運之服務，節省時間。搬家業者可能會因為當天接洽之貨物太多，而一次性地派遣大量貨車前往服務，貨車回程後會造成大量的空車情況，使得貨車之空間使用變得沒有效率。為有效地幫助搬家業者求得一系統化之最佳解，本研究構建一符合現況之多起迄需求貨物服務及排程規劃模式，以期幫助搬家業者規劃一良好之服務對象，進而提升營運績效，使得負利潤極小化。注意，雖然本研究構建之模式已可有效幫助搬家業者規劃良好的服務對象。然由於此等模式的問題規模極為龐大，無法運用現有數學規劃軟體求解。因此發展一有效率之求解演算法幫助搬家業者進行貨物服務及排程規劃模式，以幫助搬家業者能夠有效地進行求解。

本研究發展一系統化之最佳化模式。此模式可定式為一整數多重網路流動問題，屬NP-hard問題，當問題規模變大時，可能難以在有限的時間內利用數學規劃軟體求得一最佳解。緣此，本研究針對此模式發展一啟發式求解演算法，以求解貨車與貨物之配對問題。最後本研究以台中市一搬家公司之營運資料及大型社群論壇之客戶需求資料進行測試範例與分析，結果甚佳，顯示本研究所構建之模式與求解之演算法，可為未來搬家業者進行實務貨物選擇及排程之參考。

Due to the affection of urbanization, the demand in delivering commodities is greatly increased. To save time, most people will ask a moving company to help deliver their commodities. In general, the moving company performs many delivery tasks at the same time and these task assignments are done mainly based on the personal experience of the decision maker. Therefore, the moving company needs to assign a lot of vans to finish these tasks. Therefore, the situation in which most of vans is not laden when their return trips to the moving company could occur. This means that these task assignments are inefficiency. This study proposes a fleet assignment model where the actual commodity delivery constraints are taken into consideration and the objective is to maximize the profit of the moving company. Since the problem size is expected to be huge, a solution algorithm is thus developed to efficiently solve the problem.

The model is formulated as an integer multiple commodity network flow problem, which is characterized as NP-hard and cannot be optimally solved in a reasonable time for large-scale problems. To efficiently solve large-scale problems that occur in the real world, a solution algorithm is developed. To evaluate the performance of the proposed model and solution algorithm, a case study for a fleet assignment operation associated with a moving company in Taichung is performed. The test results are good, showing that the model and the algorithm could be useful for the moving company to formulate the fleet scheduling in future.

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