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Marginal cost pricing and multi-criteria routing in a distributed swarm-intelligence approach for online vehicle guidance

机译:分布式群体智能方法中的边际成本定价和多准则路由,用于在线车辆导航

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In this paper we investigate the effects of marginal cost pricing and multi-criteria routing as an addition of our distributed real-time vehicle guidance protocol BeeJamA. BeeJamA is is honey bee-inspired swarm intelligence approach for minimizing individual travel times based on a vehicle-to-infrastructure architecture. We propose changes to the swarm behavior allowing for efficient dissemination of marginal costs and multi-criteria routing information. In literature, marginal cost pricing is known as a possibility for reducing global travel times, potentially to the disadvantage of individual travel times. Although we can confirm an improvement over the plain BeeJamA protocol for a microscopic simulation setup, marginal cost pricing could not outperform a path reservation variant of BeeJamA. Distributed routing protocols for computer networks and particularly for vehicle routing often do not provide for multi-criteria decisions and BeeJamA was no exception so far. So, as a second contribution of this paper, the protocol is complemented by multi-criteria routing concepts.
机译:在本文中,我们研究了边际成本定价和多准则路由的影响,以此作为我们分布式实时车辆导航协议BeeJamA的补充。 BeeJamA是一种受蜜蜂启发的群体智能方法,用于基于车辆到基础结构的体系结构以最大程度地减少个人出行时间。我们建议对群体行为进行更改,以便有效传播边际成本和多准则路由信息。在文献中,边际成本定价被认为是减少全球旅行时间的一种可能性,这有可能不利于个人旅行时间。尽管我们可以证实对于微观仿真设置而言,与普通的BeeJamA协议相比有所改进,但边际成本定价不能超过BeeJamA的路径保留变量。用于计算机网络的分布式路由协议,尤其是用于车辆路由的分布式路由协议,通常无法提供多标准决策,因此,BeeJamA也不例外。因此,作为本文的第二个贡献,该协议还辅以多准则路由概念。

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