This paper investigates arc interdiction location-routing problem (AI-LRP), a new variant of the classical LRP. There are two decision-makers with dissimilar perceptions of the problem, making efforts to achieve their contradictory yet interconnected targets. While the interdictor develops a plan to disrupt products flow in a distribution network, the distributor strives to mitigate the effects of disruption and to deliver goods to customers at minimal risk and cost in the interdicted network. The impacts they have on one another are formulated as a bi-level programming model, with the interdictor taking decisions at the upper level. This problem has wide applications in reality, including distribution of particular goods such as money, precious metals, hazardous materials, and even prisoners that may need security measures. To solve the problem, an efficient memetic algorithm (EMA) with a dynamic local search is proposed. The efficiency of the developed EMA is demonstrated by comparing its performance with a few LRP algorithms published in the literature as well as with a commercial solver. A cost–benefit analysis, along with a case study in maritime transportation, is conducted to provide managerial insights. The results from numerical experiments show that when more budgets are allocated to interdiction and the distributor estimates the interdictor's parameters with less accuracy, AI-LRP is capable of formulating close-to-real-life cases under information asymmetry more effectively.

本文研究了弧线遮断位置路由问题（AI-LRP），这是经典LRP的新变体。有两个决策者对问题的看法不同，他们正在努力实现他们相互矛盾但又相互联系的目标。拦截者制定了破坏分销网络中产品流的计划时，分销商努力减轻干扰的影响，并以最小的风险和成本在拦截的网络中向客户交付商品。他们相互之间的影响被表述为双层编程模型，拦截者在上一级做出决定。这个问题在现实中得到了广泛的应用，包括分配某些商品，例如金钱，贵金属，危险材料，甚至是可能需要采取安全措施的囚犯。为了解决这个问题 提出了一种具有动态局部搜索的有效模因算法（EMA）。通过将其性能与文献中公布的一些LRP算法以及商用求解器进行比较，可以证明已开发EMA的效率。进行了成本效益分析以及海上运输的案例研究，以提供管理方面的见解。数值实验的结果表明，当更多的预算用于拦截时，分发者以较低的准确性估计拦截者的参数时，AI-LRP能够在信息不对称下更有效地制定接近现实的案例。通过将其性能与文献中公布的一些LRP算法以及商用求解器进行比较，可以证明已开发EMA的效率。进行了成本效益分析以及海上运输的案例研究，以提供管理方面的见解。数值实验的结果表明，当更多的预算用于拦截时，分发者以较低的准确性估计拦截者的参数时，AI-LRP能够在信息不对称下更有效地制定接近现实的案例。通过将其性能与文献中公布的一些LRP算法以及商用求解器进行比较，可以证明已开发EMA的效率。进行了成本效益分析以及海上运输的案例研究，以提供管理方面的见解。数值实验的结果表明，当更多的预算用于拦截时，分发者以较低的准确性估计拦截者的参数时，AI-LRP能够在信息不对称下更有效地制定接近现实的案例。