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The secure time-dependent vehicle routing problem with uncertain demands

机译:不确定需求的安全时间依赖车辆路由问题

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This paper addresses the transportation of valuable goods in which security carriers are interested to optimize the operating costs and the vehicle routes' security. In real operations, route planning and scheduling are normally performed manually based on individual experience. Hereupon, an optimizing approach should be developed to target the robbery risk reduction and the distribution network' operational costs. Facing the dangerous nature of operations, some considerations play an important role, including demand fluctuations and traffic congestion of the urban environment. To handle these issues, we respectively benefit from the robust optimization theory and considering time-dependent travel speeds with satisfying the ``first-in-first-out" property. This paper proposes a rich vehicle routing problem denoted by the secure time-dependent vehicle routing problem with time windows including pickup and delivery with uncertain demands (S-TD-VRPTWPD-UD). A mathematical formulation and an efficient solution approach combining the greedy randomized adaptive search procedure (GRASP) and the iterated local search (ILS) are developed to minimize the predictability of route plans using an integrated risk index, besides the travel costs. Extensive computational experiments on this problem are performed to analyze the impact of the demand uncertainty and the speed time-dependency and to show the efficiency of the GRASP x ILS implementation. The results show the significant improvements due to the time-dependency as well as the extra cost of protecting the model against the worst-case scenario of demand requests by deriving the robust counterpart of the S-TD-VRPTWPD-UD. (C) 2021 Elsevier Ltd. All rights reserved.
机译:本文地址的贵重物品在安全运营商有兴趣,优化运营成本和车辆路线的安全运输。在实际操作中,路线规划和调度通常是基于手动个人经验进行。须指出的是,一个优化的方法应该是发展为目标的抢劫减少风险和分销网络的运营成本。面对操作的危险性,一些因素发挥着重要的作用,包括需求波动和城市环境的交通拥堵。为了处理这些问题,我们分别从鲁棒优化理论,并考虑到满足时间相关的行驶速度有利于``先入先出”属性。本文提出了一种丰富的车辆路线由安全依赖于时间的表示问题车辆路径问题有时间窗包括拾取和递送具有不确定需求(S-TD-VRPTWPD-UD)。一个数学公式和高效的解决方案的方法相结合的贪婪随机自适应搜索过程(GRASP)和迭代局部搜索(ILS)是开发利用的综合风险指数,除了出行成本最小化的路径规划的可预测性,广泛的计算实验对这个问题进行分析的需求不确定性的影响,速度随时间的依赖,以显示GRASP x的效率ILS实施,结果显示显著的改善,由于时间的关系,以及保护模型agains的额外费用通过推导S-TD-VRPTWPD-UD的强劲对手牛逼的需求请求的最坏情况。 (c)2021 elestvier有限公司保留所有权利。

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