Abstract Inland waterway disruptions may interrupt barge navigation, resulting in significant economic and environmental consequences. Disruption response reroutes disrupted barges to accessible terminals to offload cargo water to land transportation. We investigate how to redirect disrupted barges and prioritize offloading at terminals to minimize the total cargo value loss during inland waterway disruption response. This problem is known in the literature as the cargo prioritization and terminal allocation problem (CPTAP). Previous studies formulated the CPTAP as a non-linear integer programming (NLIP) model, which was solved with a genetic algorithm (NLIPGA) and a tabu search (NLIPTS) approach. In this article, we formulate CPTAP as a mixed integer linear programming (MILP) model and improve its performance through the addition of valid inequalities, which we refer to as MILP’. Due to problem complexity, the NLIPGA and NLIPTS results were validated for small size instances. We fill this gap by using the lower bounds of MILP’ model to validate the quality of NLIPGA and NLIPTS solutions, and we compare the MILP’ with the NLIPGA and the NLIPTS solutions for multiple scenarios. The MILP’ formulation is found to outperform the NLIPGA and NLIPTS approaches by reducing the total cargo value loss.

中文翻译：

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货物优先级和码头分配问题模型的计算比较

摘要 内河航道中断可能会中断驳船航行，从而造成重大的经济和环境后果。中断响应将中断的驳船重新路由到可访问的码头，将货水卸载到陆路运输。我们研究了如何重定向被中断的驳船并优先在码头卸货，以最大限度地减少内河航道中断响应期间的总货物价值损失。这个问题在文献中被称为货物优先级和终端分配问题 (CPTAP)。先前的研究将 CPTAP 制定为非线性整数规划 (NLIP) 模型，该模型通过遗传算法 (NLIPGA) 和禁忌搜索 (NLIPTS) 方法解决。在本文中，我们将 CPTAP 制定为混合整数线性规划 (MILP) 模型，并通过添加有效的不等式来提高其性能，我们将其称为 MILP'。由于问题的复杂性，NLIPGA 和 NLIPTS 结果针对小规模实例进行了验证。我们通过使用 MILP' 模型的下界来验证 NLIPGA 和 NLIPTS 解决方案的质量来填补这一空白，并且我们将 MILP' 与 NLIPGA 和 NLIPTS 解决方案在多个场景下进行了比较。通过减少总货物价值损失，发现 MILP' 公式优于 NLIPGA 和 NLIPTS 方法。我们将 MILP' 与 NLIPGA 和 NLIPTS 解决方案在多个场景下进行比较。通过减少总货物价值损失，发现 MILP' 公式优于 NLIPGA 和 NLIPTS 方法。我们将 MILP' 与 NLIPGA 和 NLIPTS 解决方案在多个场景下进行比较。通过减少总货物价值损失，发现 MILP' 公式优于 NLIPGA 和 NLIPTS 方法。