1,224 research outputs found
Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation
[EN] The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T), the SEPIE Erasmus+Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Do C. Martins, L.; Tordecilla, RD.; Castaneda, J.; Juan-Pérez, ÁA.; Faulin, J. (2021). Electric vehicle routing, arc routing, and team orienteering problems in sustainable transportation. Energies. 14(16):1-30. https://doi.org/10.3390/en14165131130141
Smart green charging scheme of centralized electric vehicle stations
This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of
Communications Society (OJ-COMS
Real-time bandwidth encapsulation for IP/MPLS Protection Switching
Bandwidth reservation and bandwidth allocation are needed to guarantee the protection of voice traffic during network failure. Since voice calls have a time constraint of 50 ms within which the traffic must be recovered, a real-time bandwidth management scheme is required. Such bandwidth allocation scheme that prioritizes voice traffic will ensure that the voice traffic is guaranteed the necessary bandwidth during the network failure. Additionally, a mechanism is also required to provide the bandwidth to voice traffic when the reserved bandwidth is insufficient to accommodate voice traffic. This mechanism must be able to utilise the working bandwidth or bandwidth reserved for lower priority applications and allocate it to the voice traffic when a network failure occurs
Optimizing Sustainable Transit Bus Networks in Smart Cities
Urban mobility has been facing several challenges in the recent years due to the increasing populations and private vehicles ownership, which led to several negative environmental and social impacts in big cities. In this dissertation, we investigate how decision support systems can enhance the role of transit bus networks in the transition to more sustainable and convenient urban mobility
Stochastic scheduling of autonomous mobile robots at hospitals
The outbreak of the New Coronavirus has significantly increased the
vulnerability of medical staff. This paper addresses the safety and stress
relief of medical personnel by proposing a solution to the scheduling problem
of autonomous mobile robots (AMRs) in a stochastic environment. Considering the
stochastic nature of travel and service times for AMRs affected by the
surrounding environment, the routes of AMRs are planned to minimize the daily
cost of the hospital (including the AMR fixed cost, penalty cost of violating
the time window, and transportation cost). To efficiently generate high-quality
solutions, we identify several properties and incorporate them into an improved
Tabu Search (I-TS) algorithm for problem-solving. Experimental evaluations
demonstrate that the I-TS algorithm outperforms existing methods by producing
higher-quality solutions. By leveraging the characteristics of medical request
environments, we intelligently allocate an appropriate number of AMRs to
efficiently provide services, resulting in substantial cost reductions for
hospitals and enhanced utilization of medical resources. These findings confirm
the effectiveness of the proposed stochastic programming model in determining
the optimal number of AMRs and their corresponding service routes across
various environmental settings
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