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New Fleet Deployment and Phase-In Operation for Container Liner
|Contributors: ||NTOU:Department of Transportation Science|
Containership liner service;Mathematical programming;Ship scheduling;Fleet deployment;Phase-in operation
|Issue Date: ||2011-08-17T07:07:33Z
|Abstract: ||摘要:定期航商進行船隊及航線之重新部署與調整時，必須面臨船舶導入 暨導出(Phase-in and phase-out)之船期安排，亦即如何完成船舶由原先服務航線轉換至新航線間之過渡性接續。該項課題之重點，乃是處理船舶在時間與空間上之替換，以延續各船舶應有之服務；其決策內容涵蓋船舶之航程調整與載貨換船之處理方式。本研究針對新船導入所產生之船期擾動問題，藉時間-運具網路(Time-vehicle network)之概念，設計船流與櫃流之網路圖形，除船舶之移動外，將各起迄對貨流分類為流入規劃期、規劃期內部與流出規劃期三層網 路，進而建立四層結構之多元商品流量問題(Multi-commodity flow problem)模式，以追求流動成本極小化。由於額外限制式之約束性質不高，本研究參考各航商之案例進行實驗設計，並利用分枝限界法( Branch-and-bound method)進行求解，即可在有限時間內解算實務所需之問題規模。此外，經以特定航商之實例進行分析比較，模式所得結果頗具可用性。而在敏感度分析方面，則以貨流供給之變化對決策之影響較大。|
Abstract:Liner carriers have to face the scheduling problem of ships' phase-in and phase-out, when they adjust the existing routes or change their fleet deployment. That means a smooth connection between the original service route and the new one for involved ships. Not only the time and place of connection within their service routes but containers transferring plan also are most concerned decision. In this paper, we focus on the scheduling problem of disturbance caused from the new ship's phase-in to design a network, which applies the concept of time-vehicle network, with four tiers to describe the movement of ships and containers. Except the ships' movement, the other three tiers are the goods flowing into the planning horizon, inner flowing, and flowing out the planning horizon. Through this network, the discussed problem can be formulated as a multi-commodity flow problem for minimizing the total flowing cost. Since For the sake of sparse side constraints in this model, we exploit the branch-and-bound method to solve the self-designed cases directly. The results show it can obtain the optimal solutions within reasonable CUP times for the scale of practical cases. The test to the case of Yang-Ming Lines shows that the results obtained from the model can be promisingly applied in practice. The sensitivity analysis also indicates that the carrying demand of containers governs the variation of decision.
|Appears in Collections:||[運輸科學系] 研究計畫|
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