Electric vehicle routing with charging/discharging under time-variant electricity prices

Bo, Lin; Ghaddar, Bissan; Nathwani, Jatin

doi:10.1016/j.trc.2021.103285

Cited by 60 publications

(36 citation statements)

References 36 publications

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“…We construct an initial solution using the approach in Lin et al (2021). We first choose an arbitrary point and calculate the angle between the depot, this random point, and the customer.…”

Section: Solution Methods For the Master Problemmentioning

confidence: 99%

Adaptive robust electric vehicle routing under energy consumption uncertainty

Jeong¹,

Ghaddar²,

Zufferey³

et al. 2022

Preprint

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Electric vehicles (EVs) have been highly favoured as a future transportation mode in the transportation section in recent years. EVs have many advantages compared to traditional transportation, especially the environmental aspect. However, despite many EVs' benefits, operating EVs has limitations in their usage.One of the significant issues is the uncertainty in their driving range. The driving range of EVs is closely related to their energy consumption, which is highly affected by exogenous and endogenous factors. Since those factors are unpredictable, uncertainty in EVs' energy consumption should be considered for efficient operation. This paper proposes an adaptive robust optimization framework for the electric vehicle routing problem. The objective is to minimize the worst-case energy consumption while guaranteeing that services are delivered at the appointed time windows without battery level deficiency. We postulate that EVs can be recharged en route, and the charging amount can be adjusted depending on the circumstance. The proposed problem is formulated as a two-stage adaptive robust problem. A column-and-constraint generation based heuristic algorithm, which is cooperated with variable neighborhood search and alternating direction algorithm, is proposed to solve the proposed model. The computational results show the economic efficiency and robustness of the proposed model, and that there is a tradeoff between the total required energy and the risk of failing to satisfy all customers' demand.

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“…We construct an initial solution using the approach in Lin et al (2021). We first choose an arbitrary point and calculate the angle between the depot, this random point, and the customer.…”

Section: Solution Methods For the Master Problemmentioning

confidence: 99%

Adaptive robust electric vehicle routing under energy consumption uncertainty

Jeong¹,

Ghaddar²,

Zufferey³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lagrangian relaxation is a well-known relaxation method to obtain a bound on difficult optimization problems [35]. It has been widely used in vehicle routing [36], water pump scheduling [37], and more recently in orchestration of services in edge networks [38]. In our formulation, constraint (2) links the time periods and complicates the problem.…”

Section: Lagrangian Relaxationmentioning

confidence: 99%

Energy-Efficient Service Placement for Latency-Sensitive Applications in Edge Computing

Premsankar

Ghaddar

2022

IEEE Internet Things J.

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Edge computing is a promising solution to host artificial intelligence (AI) applications that enable real-time insights on user-generated and device-generated data. This requires edge computing resources (storage and compute) to be widely deployed close to end devices. Such edge deployments require a large amount of energy to run as edge resources are typically overprovisioned to flexibly meet the needs of time-varying user demand with a low latency. Moreover, AI applications rely on deep neural network (DNN) models that are increasingly larger in size to support high accuracy. These DNN models must be efficiently stored and transferred, so as to minimize their energy consumption. In this article, we model the problem of energy-efficient placement of services (namely, DNN models) for AI applications as a multiperiod optimization problem. The formulation jointly places services and schedules requests such that the overall energy consumption is minimized and latency is low. We propose a heuristic that efficiently solves the problem while taking into account the impact of placing services across time periods. We assess the quality of the proposed heuristic by comparing its solution to a lower bound of the problem, obtained by formulating and solving a Lagrangian relaxation of the original problem. Extensive simulations show that our proposed heuristic outperforms baseline approaches in achieving a low energy consumption by packing services on a minimal number of edge nodes, while at the same time keeping the average latency of served requests below a configured threshold in nearly all time periods.

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“…GNN was also used to train supervised normalized embeddings employed to rebuild neighboring matrix of TSP graph [ 40 ]. Furthermore, reinforcement learning was used to solve the problem of electric vehicle maneuvering, and a new architecture based on decentralized learning and centralized decision making was presented [ 41 ]. To build these circuits, upon transforming the online routing problem into the in-vehicle circuit generation problem, James, Yu, and Gu [ 42 ] presented a deep reinforcement learning based solution based on the network of pointers embedded in the structure diagram.…”

Section: Related Workmentioning

confidence: 99%

Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning

et al. 2022

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In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck’s capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points. In our approach, this challenge is solved using machine learning-based models. Using HPNs as our primary model allows us to gain insight and tackle more complicated node interactions, which simplified our objective to obtaining state-of-art outcomes. Our experimental results demonstrate the effectiveness of the proposed neural network, as we achieve the state-of-art results for this problem as compared with the existing models. We deal with two types of demand patterns in a single type commodity problem. In the first pattern, all demands are assumed to sum up to zero (i.e., we have an equal number of backup and drop-off items). In the second pattern, we have an unequal number of backup and drop-off items, which is close to practical application, such as bike sharing system rebalancing. Our data, models, and code are publicly available at Solving Pickup and Dropoff Problem Using Hybrid Pointer Networks with Deep Reinforcement Learning.

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Electric vehicle routing with charging/discharging under time-variant electricity prices

Cited by 60 publications

References 36 publications

Adaptive robust electric vehicle routing under energy consumption uncertainty

Adaptive robust electric vehicle routing under energy consumption uncertainty

Energy-Efficient Service Placement for Latency-Sensitive Applications in Edge Computing

Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning

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