Optimal Policies for Platooning and Ride Sharing in Autonomy-Enabled Transportation

Adler, Aviv; Miculescu, David; Karaman, Sertaç

doi:10.1007/978-3-030-43089-4_54

Cited by 20 publications

(18 citation statements)

References 13 publications

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“…In doing so, we explore how decision variables related to the size of the platoons and the arrival and departure schedules combined with costs for operating trucks (idle time and driving time) affect the platooning benefits. Compared to the literature, in addition to the similarities to the work by Adler et al (2016) discussed above, the most comparable reference is that of Meisen et al (2008). However, the current paper extends Meisen et al (2008) in a few important ways.…”

Section: Problem Definitionmentioning

confidence: 93%

“…Also, no estimate of the expected profit and how this differs across inputs were provided and we therefore believe that our paper extend this work in several ways as will be detailed in Section 3 below. Adler et al (2016) considers the problem of forming platoons for trucks travelling between two hubs and also the trade-off between idle time when forming platoons and obtained energy savings from driving in platoons. Conceptually, their approach shares similarities with the approach put forward in this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Hub-based truck platooning: Potentials and profitability

Larsen

Rich

Rasmussen

2019

Transportation Research Part E: Logistics and Transportation Re

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This paper presents a model for optimising truck platoons formed at a platooning hub. Different planning and dispatching strategies, from static to dynamic, are investigated with respect to profitability and fuel savings across a range of input variables. The problem is solved using a dynamic programming based local search heuristic. As a case study, a virtual platooning hub close to the German Elb Tunnel is examined using data from a large European transport network model. It is concluded that profitability crucially depends on; i) dynamic outlook and ii) if chauffeurs are allowed to rest while driving in platoons.

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Section: Problem Definitionmentioning

confidence: 93%

Section: Literature Reviewmentioning

confidence: 99%

Hub-based truck platooning: Potentials and profitability

Larsen

Rich

Rasmussen

2019

Transportation Research Part E: Logistics and Transportation Re

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show abstract

“…In their article it had been shown that if the scheduled arrival times of the vehicles are different, the goals of saving fuel and arriving on time were conflicting [18]. A similar issue of energy-delay trade-off has been analyzed in [19]. The authors presented an algorithm that highlights the negatives of the platoon concept.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The authors presented an algorithm that highlights the negatives of the platoon concept. Even though vehicles that are in a platoon can reduce energy consumption and emissions, the accumulated waiting time for the interconnection can greatly increase the delay [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of a physical internet based supply chain using reinforcement learning

Puskás

Budai

Bohács

2020

Eur. Transp. Res. Rev.

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Physical Internet based supply chains create open, global logistics systems that enable new types of collaboration among participants. The open system allows the logistical examination of vehicle technology innovations such as the platooning concept. This article explores the multiple platoon collaboration. For the reconfiguration of two platoons a heuristic and a reinforcement learning (RL) based models have been developed. To our knowledge, this work is the first attempt to apply an RL-based decision model to solve the problem of controlling platoon cooperation. Vehicle exchange between platoons is provided by a virtual hub. Depending on the various input parameters, the efficiency of the model was examined through numerical examples in terms of the target function based on the transportation cost. Models using platoon reconfiguration are also compared to the cases where no vehicle exchange is implemented. We have found that a reinforcement learning based model provides a more efficient solution for high incoming vehicle numbers and low dispatch interval, although for low vehicle numbers heuristics model performs better.

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“…A number of papers focus on platoon planning in which trucks travel on a fixed path, thus ignoring any possible routing flexibility. These papers typically focus on a single highway lane or designated hub (Adler et al 2016, Larsen et al 2019. A number of these specifically focus on adjusting speeds to form platoons (Liang et al 2013, Sun andYin 2019).…”

Section: Related Literaturementioning

confidence: 99%