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A cost-beneficial area-partition-involved collaborative patrolling game in a large-scale chemical cluster
Process Safety and Environmental Protection ( IF 7.8 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.psep.2020.07.010 Feiran Chen , Bin Chen , Zhengqiu Zhu , Laobing Zhang , Xiaogang Qiu , Yiduo Wang , Yong Zhao
Process Safety and Environmental Protection ( IF 7.8 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.psep.2020.07.010 Feiran Chen , Bin Chen , Zhengqiu Zhu , Laobing Zhang , Xiaogang Qiu , Yiduo Wang , Yong Zhao
Abstract Terrorists often take the chemical clusters as the attacking target because of the adverse impacts of a chemical accident on society and the environment. In addition to some fixed countermeasures, previous studies have verified the feasibility of a patrol in addressing adversarial attacks. However, the previous patrolling practices fail to tackle the terrorist attacking problems in a large-scale area cost-effectively. To further tackle the protection issue with cost-beneficial solutions in a large-scale scenario, i.e., in a chemical cluster, we propose an area-partition-involved collaborative patrolling (APCP) game. We first leverage the proposed greedy deployment algorithm to determine the initial deployment of defenders (patrollers), including the quantity and position of patrol vehicles. Then, the large-scale area is partitioned into multiple smaller areas by using the collaborative idea of static partitioning. In the meantime, corresponding patrolling graphs are constructed based on graphic modeling methods. Finally, the APCP game is built between patrol vehicles (namely defender) and potential terrorists (namely attacker), in which patrol vehicles aim at detecting attack behaviors of terrorists by intelligently scheduling the patrolling routes. After formalizing the problem into a sequential game, we compute the Stackelberg equilibrium through the MultiLPs algorithm. Through case studies of three practical chemical cluster scenarios, the results explicitly show the superiority of our proposed APCP game by saving up to 25.48 % patrolling costs in a one-shot game compared to the results before partition. As for the collaborative patrolling problem in a large-scale area, the methods and models proposed in this paper can facilitate the management department of chemical clusters with intelligently scheduled patrolling routes, which can effectively reduce the cost of patrollers, and better protect the chemical cluster.
中文翻译:
大规模化学集群中的成本效益区域分区协同巡逻博弈
摘要 由于化学事故对社会和环境的不利影响,恐怖分子往往将化学集群作为袭击目标。除了一些固定的对策外,之前的研究已经验证了巡逻在应对对抗性攻击方面的可行性。然而,以往的巡逻做法无法经济有效地解决大面积区域的恐怖袭击问题。为了在大规模场景中,即在化学集群中,通过成本效益的解决方案进一步解决保护问题,我们提出了一个涉及区域分区的协作巡逻(APCP)游戏。我们首先利用所提出的贪婪部署算法来确定防御者(巡逻者)的初始部署,包括巡逻车的数量和位置。然后,利用静态分区的协同思想,将大范围的区域划分为多个较小的区域。同时,基于图形建模方法构建相应的巡逻图。最后,在巡逻车(即防御者)和潜在恐怖分子(即攻击者)之间建立APCP博弈,其中巡逻车旨在通过智能调度巡逻路线来检测恐怖分子的攻击行为。在将问题形式化为顺序博弈后,我们通过 MultiLPs 算法计算 Stackelberg 均衡。通过三个实际化学集群场景的案例研究,结果明确显示了我们提出的 APCP 游戏的优越性,与分区前的结果相比,在一次性游戏中节省了高达 25.48% 的巡逻成本。
更新日期:2021-01-01
中文翻译:
大规模化学集群中的成本效益区域分区协同巡逻博弈
摘要 由于化学事故对社会和环境的不利影响,恐怖分子往往将化学集群作为袭击目标。除了一些固定的对策外,之前的研究已经验证了巡逻在应对对抗性攻击方面的可行性。然而,以往的巡逻做法无法经济有效地解决大面积区域的恐怖袭击问题。为了在大规模场景中,即在化学集群中,通过成本效益的解决方案进一步解决保护问题,我们提出了一个涉及区域分区的协作巡逻(APCP)游戏。我们首先利用所提出的贪婪部署算法来确定防御者(巡逻者)的初始部署,包括巡逻车的数量和位置。然后,利用静态分区的协同思想,将大范围的区域划分为多个较小的区域。同时,基于图形建模方法构建相应的巡逻图。最后,在巡逻车(即防御者)和潜在恐怖分子(即攻击者)之间建立APCP博弈,其中巡逻车旨在通过智能调度巡逻路线来检测恐怖分子的攻击行为。在将问题形式化为顺序博弈后,我们通过 MultiLPs 算法计算 Stackelberg 均衡。通过三个实际化学集群场景的案例研究,结果明确显示了我们提出的 APCP 游戏的优越性,与分区前的结果相比,在一次性游戏中节省了高达 25.48% 的巡逻成本。