本論文主要研究貨運業者如何利用合作關係以達到營運最佳化，而貨運業者需要在其他潛在合作者需求未確定下決定是否與其合作；當兩者之間的合約簽定時，業者必須滿足已簽訂合約公司的需求。研究主要分為兩個目標，首先為利潤最大化， 針對一個貨運問題建構兩階段隨機規劃的數學模型；第二個目標則為排碳量最小化。由於所提出的數學模型屬於NP-hard問題，無法利用套裝軟體在有限時間內求解，因此本研究相應提出一結合蒙地卡羅限界法作為停止條件的禁忌搜尋法，作為本研究的求解演算法。本演算法首先展開原問題為一大型混合整數問題，而後利用禁忌搜尋法求出現行解，最後以蒙地卡羅限界法判定該現行解是否落入最佳解之信賴區間中。而所提出的研究方法於實證研究上可以證明，貨運業者在與其他業者合作的情況下，利潤能夠有效提升4%，而貨運的整體碳排放量也能夠降低3%至20%。

In this paper, we investigate the carrier collaboration problem in the trucking industry, and we analyze the approach a carrier employs to determine whether it should sign a contract with its collaborators and deliver their unknown delivery requests with backhaul routes. We also study carbon emission trading between freight companies and investigate a collaboration problem. The problems are formulated as a 2-stage stochastic program with recourse. The resulting stochastic multi-vehicle routing problem is difficult to solve because of its combinatorial nature and NP-hard complexity. We therefore present a novel algorithm that combines the Tabu Search method and Monte Carlo bounding techniques as an approach to this problem. In the algorithm, we first expand the stochastic model to an equivalent large-scale mixed-integer programming problem and determine the incumbent solutions using the Tabu Search method. Monte Carlo bounding techniques are then applied to evaluate the quality of the obtained incumbent solution and to provide an effective upper bound for this stochastic programming problem. The suggested method is empirically applied to real-world freight networks, and the numerical results provide evidence that the collaborative model presented in this thesis can effectively increase profit by at least 4% and reduce total emissions in a freight network by 3%–20%.

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