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首页> 外文期刊>IEEE Transactions on Automatic Control >Asymptotically Optimal Algorithms for One-to-One Pickup and Delivery Problems With Applications to Transportation Systems
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Asymptotically Optimal Algorithms for One-to-One Pickup and Delivery Problems With Applications to Transportation Systems

机译:一对一收货和配送问题的渐近最优算法及其在运输系统中的应用

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摘要

Pickup and delivery problems (PDPs), in which objects or people have to be transported between specific locations, are among the most common combinatorial problems in real-world logistical operations. A widely-encountered type of PDP is the Stacker Crane Problem (SCP), where each commodity/customer is associated with a pickup location and a delivery location, and the objective is to find a minimum-length tour visiting all locations with the constraint that each pickup location and its associated delivery location are visited in immediate, consecutive order. The SCP is NP-Hard and the best known approximation algorithm only provides a 9/5 approximation ratio. In this paper, we examine an embedding of the SCP within a stochastic framework, and our objective is three-fold: First, we describe a large class of algorithms for the SCP, where every member is asymptotically optimal, i.e., it produces, almost surely, a solution approaching the optimal one as the number of pickups/deliveries goes to infinity; moreover, one can achieve computational complexity O(n2+ε) within the class, where n is the number of pickup/delivery pairs and ε is an arbitrarily small positive constant. Second, we characterize the length of the optimal SCP tour asymptotically. Finally, we study a dynamic version of the SCP, whereby pickup and delivery requests arrive according to a Poisson process, and which serves as a model for large-scale demand-responsive transport (DRT) systems. For such a dynamic counterpart of the SCP, we derive a necessary and sufficient condition for the existence of stable vehicle routing policies, which depends only on the workspace geometry, the distributions of pickup and delivery points, the arrival rate of requests, and the number of vehicles. Our results leverage a novel connection between the Euclidean Bipartite Matching Problem and the theory of random permutations, and, for the dynamic setting, exhibit novel features that are absent in tr- ditional spatially-distributed queueing systems.
机译:现实世界中的物流操作中最常见的组合问题之一就是必须在特定位置之间运输物品或人员的取货和运送问题(PDP)。 PDP的一种广泛遇到的类型是堆垛机起重机问题(SCP),其中每个商品/客户都与取货地点和交货地点相关联,目标是找到访问所有地点的最小长度游览,其约束条件是每个提货地点及其关联的交货地点均以立即连续的顺序访问。 SCP是NP-Hard,最著名的近似算法仅提供9/5的近似比率。在本文中,我们检查了SCP在随机框架中的嵌入,我们的目标是三方面的:首先,我们为SCP描述了一大类算法,其中每个成员都是渐近最优的,也就是说,它几乎产生当然,随着取件/送达数量达到无穷大,一种解决方案已接近最佳解决方案。此外,可以在该类内实现计算复杂度O(n 2 +ε),其中n是取/送对的数量,ε是任意小的正常数。其次,我们渐近地描述了最优SCP巡视的长度。最后,我们研究了SCP的动态版本,通过该流程可以根据泊松过程到达取货和交付请求,并且可以作为大规模需求响应运输(DRT)系统的模型。对于如此动态的SCP,我们为稳定的车辆路线选择策略的存在导出了充要条件,该条件仅取决于工作空间的几何形状,取货和配送点的分布,请求的到达率以及数量的车辆。我们的结果利用了欧几里得二分匹配问题和随机置换理论之间的新颖联系,并且对于动态设置,它表现出传统的空间分布排队系统所缺乏的新颖特征。

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