空陸複合運輸之路徑與併裝決策模式

Abstract

航空貨運業在過去的數十年中蓬勃發展，最近相關的運量預測也顯示未來二十年的成長亦相當樂觀。同時，台灣的經濟發展相當依賴以出口為導向之高科技製造業，一個高效率之航空貨運業將非常有助於台灣產品競爭力之提升。然而，國際航空貨運業是一個作業密集的產業，其中航空貨運承攬業所扮演之角色，一方面是貨主的服務供應者，另一方面也同時是航空公司的消費需求者，於整個國際航空貨運業的流程中扮演著極重要的角色。 航空貨運計價方式較一般運輸業來得複雜，不僅有向貨主收取費用的訂價，還必須考慮交付航空公司的不同收費方式，甚至當進行出口貨物戶對戶的運送類型時，還需將陸運花費納入考量。在此研究中，航空公司會提供不同種類、重量與體積限制的盤櫃，而其有相對應的收費方式，包含使用盤櫃的固定成本以及根據盤櫃內貨物重量計價的變動成本，因此本研究以航空承攬運送業者為角度，發展了混合整數規劃 (Mixed Integer Programming – MIP) 的模式，提供散貨貨物併裝與路徑之決策模式，幫助航空承攬業者決定如何租賃盤櫃以及如何將貨物併裝在這些盤櫃內，目標為最小化其所須支付之空運及陸運費用。 然而，受限於運算的複雜度，MIP 模型對於較大規模的問題並無法在短時間內找到合理的解答，為了可以運用在實務當中，必須在有限時間內求出理想的可行解，所以本研究以拉式鬆弛法為基礎發展出一遞迴性的啟發式求解演算法。數值測試的結果發現，對於小型問題，在MIP 模型可在合理時間內提供最佳解的情況下，演算法所得的近似解接近最佳值，另外，對於中大型問題，雖然MIP 模型無法提供最佳解供比對，但演算法均可在有限的時間內求得合理之近似解，並提供參考價值之解答。

Air cargo business has been booming for the past decades, and recent forecast also shows that the growth rate is promising for next twenty years. Meanwhile, Taiwan’s economic development highly depends on high-tech manufacturing industry, an efficient air cargo business would be very helpful to promote the competition of Taiwan’s products. The airfreight forwarders play a very important role in international air cargo business. They are air service providers for shippers and the consumers for air airlines. In this problem, the air carriers can provide different types of air containers with different weight and volume limits. The problem is further complicated by the cost charged by the air carriers: this is based on a fixed cost for using the container and a variable cost that depends on the weight that the container will hold. This study developed a mixed integer programming model which is from airfreight forwarder’s perspective, to provide decision-making of cargo consolidation and path decision to help airfreight forwarder make decisions about how to rent air containers from air carriers and how to load air cargos into these containers optimally. The objective is to minimize its transport costs. As it is hard to solve large scale problems in reasonable time. To be able to use it in practice, we use Lagrangean Relaxation to develop a recursive heuristic for finding an ideal feasible solution in a limited time. Based on the performed experiments, the heuristic generates the solution very close to the optimal derived from the MIP model for the small scale problems. For larger scale problems though MIP model can’t provide optimal solution for comparison, the heuristic still terminates within a limited time and generates a reasonable solution.