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A Logic-Based Benders Decomposition Approach to Improve Coordination of Inland Vessels for Inter-Terminal Transport

机译:一种基于逻辑的Benders分解方法,可改善内河船之间的终端运输协调性

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Large seaports usually contain multiple terminals serving container vessels, railways, trucks and other modes of hinterland transportation. Every time an inland vessel enters a seaport, it visits several terminals for loading and unloading containers. A vessel rotation is the sequence in which a vessel visits the different terminals in a large seaport. Currently, in a seaport like the port of Rotterdam, around 40% of the inland vessels have to spend a longer time in the port area than originally planned, due to the low utilization of terminal quay resources and uncertainty of waiting times at different terminals. To better utilize the terminal resources in the ports, as well as to reduce the amount of time inland vessels spend in the port area, this paper first proposes a new model in which inland vessels coordinate with each other with respect to the arrival, departure time and the number of inter-terminal containers carried, besides their conventional hinterland containers, with the aim to prevent possible conflicts of their rotations. Then, a logic-based Benders' decomposition approach is proposed to minimize the total time the inland vessels spent in the port. We compare the performance of the proposed approach with the performance of a centralized approach on the aspects of the runtime, solution quality, and three logistical performance indicators. Simulation results show that the proposed approach generates both faster optimal and faster high-quality solutions than the centralized approach in both small and large problem instance.
机译:大型港口通常包含多个为集装箱船,铁路,卡车和其他腹地运输方式服务的码头。内陆船只每次进入海港时,都会访问几个码头来装卸集装箱。船只旋转是船只到达大型海港中不同码头的顺序。目前,在鹿特丹港这样的海港,由于码头码头资源利用率低以及不同码头等待时间的不确定性,约有40%的内河船在港口区域的停留时间比原计划更长。为了更好地利用港口的码头资源,并减少内陆船只在港口区域的停留时间,本文首先提出了一种新模型,其中内陆船只在到达,离开时间方面相互协调除常规的腹地集装箱外,还携带终端间集装箱的数量,目的是防止其旋转可能发生冲突。然后,提出了一种基于逻辑的Benders分解方法,以最大程度地减少内陆船只在港口停留的总时间。在运行时,解决方案质量和三个后勤性能指标方面,我们将提议的方法的性能与集中式方法的性能进行了比较。仿真结果表明,与集中式方法相比,在小问题和大问题实例中,该方法都能生成更快的最优解和更快的高质量解。

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