Swarm intelligence-based green optimization framework for sustainable transportation

Nguyen, Tri-Hai; Jung, Jason J.

doi:10.1016/j.scs.2021.102947

Cited by 41 publications

(14 citation statements)

References 40 publications

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“…By adopting the data sources of connected autonomous vehicles, Gokasar et al [27] put forward an algorithm with the modifed standard normal deviation and validated its effectiveness in the trafc simulation software. In the simulation of urban mobility platform, Nguyen and Jung [28] applied swarm intelligence to green transportation and demonstrated that the average fuel consumption and the average trip duration could be both reduced by the framework. Gokasar et al [29] used the shockwave speed to control the connected autonomous vehicles in sustainable transportation.…”

Section: Research Methods In the Sustainable Transportationmentioning

confidence: 99%

Product Line Optimization for Car-Sharing Platforms in the Sustainable Transportation

Zhang

Wang

2023

Journal of Advanced Transportation

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As the sharing economy develops in sustainable transportation, car-sharing platforms improve the allocation efficiency of idle cars to customers. However, as the travel demand increases, and the vehicle type becomes various, the sharing market faces a new challenge to satisfy the customers’ diverse needs for quality-differentiated cars. Each customer would like to choose the right car type, while the manufacturing firm and the e-commerce platform should set the right car price for profit maximization. How to match the platform’s product line with the customer’s choice behavior becomes a problem for all the stakeholders in the sharing economy. From the perspective of customer heterogeneity, we establish a game-theoretic framework to study product line optimization in business-to-consumer (B2C) and consumer-to-consumer (C2C) sharing. We solve the optimal quality decision and the optimal pricing strategy for the manufacturing firm and the e-commerce platform in the sharing economy. In numerical experiments, we investigate how the sensitivity of the quality and the cost can jointly influence the profits of sustainable transportation. Our findings show that within the product line, the firm’s selling profit in the C2C sharing is negatively influenced by the sharer’s valuation, while the platform’s sharing profit in the B2C sharing is independent of either buyer’s valuation. We give some policy implications for scholars, practitioners, and policymakers in the sharing economy and point out some limitations and recommendations for the product line design.

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Section: Research Methods In the Sustainable Transportationmentioning

confidence: 99%

Product Line Optimization for Car-Sharing Platforms in the Sustainable Transportation

Zhang

Wang

2023

Journal of Advanced Transportation

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“…In [12], presented approach to quality of the dynamically varying VANETs called hybrid-fuzzy logic guided genetic algorithm. Through this method multi-objective resource optimization issues are reduced that reduced the latency.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Bio-Inspired Optimization Scheme for RSU Distribution in Vehicular Ad-Hoc Network

Hassan¹,

Al-obaidi²,

Alani³

et al. 2023

IJETAE

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—Vehicular Ad-Hoc Network (VANETs) is a trending modelthat plays maximum role inall the application of Intelligent Transport Systems (ITS). In general, VANETs are based on two communication types such as among vehicles and vehicles to infrastructure Roadside Units(RSU) based communication. In this paper, HACOGO approach is developedbased on ahybrid bio inspired optimization with the combination of Ant Colony Optimization (ACO) with Grasshopper Optimization Algorithm (GOA). The HACOGO is used to perform stable RSU distribution in VANETs. The ACO algorithm is used to help the vehicle to select the optimal path toward the destination and GOA algorithm highly magnificent vehicle is chosen as RSU. The Performance analysis of HACOGO is done by calculating the common parameters such as packet delivery ratio, end-to-end delay, packet loss and routing overhead. To analysis the effectiveness of the proposed model its results are compared with the earlier works. From the outcome it is proven using the HACOGO approach the end-to-end delay, packet loss and routing overhead is reduced as well as the packet delivery ratio of the network is increased than others. Keywords—Vehicular Ad-Hoc Network,Roadside Units, Ant Colony Optimization.

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“…Hence, other collective-intelligence features such as collaboration and self-management are yet to be fully realized. The dominant collective-intelligence methodology that has been explored so far in the literature is swarm intelligence, and algorithms such as ant colony optimization (ACO) and particle swarm optimization (PSO) have proven to be reasonably effective in traffic routing optimization when the vehicles are connected [164]- [167]. The authors in [164] and [166] have extensively surveyed swarm optimization techniques applied to intelligent traffic management.…”

Section: A Road Traffic Controlmentioning

confidence: 99%

Collective Intelligence Using 5G: Concepts, Applications, and Challenges in Sociotechnical Environments

et al. 2022

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Swarm intelligence-based green optimization framework for sustainable transportation

Cited by 41 publications

References 40 publications

Product Line Optimization for Car-Sharing Platforms in the Sustainable Transportation

Product Line Optimization for Car-Sharing Platforms in the Sustainable Transportation

A Hybrid Bio-Inspired Optimization Scheme for RSU Distribution in Vehicular Ad-Hoc Network

Collective Intelligence Using 5G: Concepts, Applications, and Challenges in Sociotechnical Environments

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