• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computers & operations research >A trilevel r-interdiction selective multi-depot vehicle routing problem with depot protection
【24h】

A trilevel r-interdiction selective multi-depot vehicle routing problem with depot protection

机译：具有库保护的三维r-Interdiction选择多仓库车辆路由问题

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The determination of critical facilities in supply chain networks has been attracting the interest of the Operations Research community. Critical facilities refer to structures including bridges, railways, train/ metro stations, medical facilities, roads, warehouses, and power stations among others, which are vital to the functioning of the network. In this study we address a trilevel optimization problem for the protection of depots of utmost importance in a routing network against an intelligent adversary. We formulate the problem as a defender-attacker-defender game and refer to it as the trilevel r-interdiction selective multi-depot vehicle routing problem (3LRI-SMDVRP). The defender is the decision maker in the upper level problem (ULP) who picks u depots to protect among m existing ones. In the middle level problem (MLP), the attacker destroys r depots among the (m-u) unprotected ones to bring about the biggest disruption. Finally, in the lower level problem (LLP), the decision maker is again the defender who optimizes the vehicle routes and thereby selects which customers to visit and serve in the wake of the attack. All three levels have an identical objective function which is comprised of three components. (i) Operating or acquisition cost of the vehicles. (ii) Traveling cost incurred by the vehicles. (iii) Outsourcing cost due to unvisited customers. The defender aspires to minimize this objective function while the attacker tries to maximize it. As a solution approach to this trilevel discrete optimization problem, we resort to a smart exhaustive enumeration in the ULP and MLP. For the LLP we design a metah-euristic algorithm that hybridizes Variable Neighborhood Descent and Tabu Search techniques adapted to the Selective MDVRP (SMDVRP). The performance of this algorithm is demonstrated on 33 MDVRP benchmark instances existing in the literature and 41 SMDVRP instances generated from them. Numerical experiments on a large number of 3LRI-SMDVRP instances attest that our comprehensive method is effective in dealing with the defender-attacker-defender game on multi-depot routing networks. (C) 2020 Elsevier Ltd. All rights reserved.
机译：确定供应链网络中的关键设施一直吸引了运营研究界的兴趣。关键设施是指桥梁，铁路，火车/地铁站，医疗设施，道路，仓库和电站等结构，对网络的运作至关重要。在这项研究中，我们解决了在对智能对手的路由网络中最重要的仓库保护的三维优化问题。我们将问题作为防御者攻击者 - 后卫游戏，并将其称为TriveLibel R-Interdiction选择性多仓库车辆路由问题（3LRI-SMDVRP）。该辩护人是上层问题的决策者（ULP），他们挑选你的仓库以保护在现有的问题中。在中间问题（MLP）中，攻击者在（M-U）未受保护的人中摧毁了R仓库，以带来最大的中断。最后，在较低级别的问题（LLP）中，决策者再次是优化车辆路线的后卫，从而在攻击后选择哪些客户访问和服务。所有三个级别都具有相同的客观函数，由三个组件组成。 （i）车辆的运营或收购成本。 （ii）车辆产生的旅行费用。 （iii）由于客户不受理的情况而导致的外包费用。后卫渴望尽量减少这个目标函数，而攻击者试图最大化它。作为这种Trivervel离散优化问题的解决方案方法，我们在ULP和MLP中度过了智能详尽的枚举。对于LLP，我们设计了一种Metah-Eurativer算法，其使变量邻域下降和禁忌搜索技术混合，适用于选择性MDVRP（SMDVRP）。在文献中存在的33个MDVRP基准实例和41个SMDVRP实例中的33 MDVRP基准实例上演示了该算法的性能。大量3Lri-SMDVRP实例的数值实验证明了我们的综合方法在处理多仓库路由网络上的防御者攻击者 - 防御者游戏方面是有效的。 （c）2020 elestvier有限公司保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号