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Keywords = heterogeneous multitype fleet

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22 pages, 6469 KiB  
Article
A Collaborative Optimization Approach for Configuring Energy Storage Systems and Scheduling Multi-Type Electric Vehicles Using an Improved Multi-Objective Particle Swarm Optimization Algorithm
by Yirun Liu and Xiaolong Wu
Processes 2025, 13(5), 1343; https://doi.org/10.3390/pr13051343 - 27 Apr 2025
Viewed by 544
Abstract
Energy storage systems (ESS) and electric vehicles (EVs) play a crucial role in facilitating the grid integration of variable wind and solar power. Despite their potential, achieving coordinated operational optimization between ESS and heterogeneous EV fleets to maintain grid stability under high renewable [...] Read more.
Energy storage systems (ESS) and electric vehicles (EVs) play a crucial role in facilitating the grid integration of variable wind and solar power. Despite their potential, achieving coordinated operational optimization between ESS and heterogeneous EV fleets to maintain grid stability under high renewable penetration poses a complex technical challenge. To address this, this study develops an integrated optimization framework combining ESS capacity planning with multi-type EV scheduling strategies. For ESS deployment, a tri-objective model balances cost, wind–solar integration, and electricity deficit. A Monte Carlo simulation algorithm is used to simulate different probabilistic models of charging loads for multiple types of EVs, and a bi-objective optimization approach is used for their orderly scheduling. An improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed to resolve the coupled optimization problem. Case studies reveal that the framework achieves annual cost reductions, enhances the wind–solar integration rate, and minimizes the power deficit in the system. Full article
(This article belongs to the Section Energy Systems)
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25 pages, 2238 KiB  
Article
Heterogeneous Multitype Fleet Green Vehicle Path Planning of Automated Guided Vehicle with Time Windows in Flexible Manufacturing System
by Jia Gao, Xiaojun Zheng, Feng Gao, Xiaoying Tong and Qiaomei Han
Machines 2022, 10(3), 197; https://doi.org/10.3390/machines10030197 - 9 Mar 2022
Cited by 22 | Viewed by 4093
Abstract
In this study, we present and discuss a variant of the classical vehicle routing problem (VRP), namely the heterogeneous multitype fleet green automated guided vehicle (AGV) routing problem with time windows (HFGVRPTW) applied in the workshops of flexible manufacturing systems (FMS). Specifically, based [...] Read more.
In this study, we present and discuss a variant of the classical vehicle routing problem (VRP), namely the heterogeneous multitype fleet green automated guided vehicle (AGV) routing problem with time windows (HFGVRPTW) applied in the workshops of flexible manufacturing systems (FMS). Specifically, based on the analysis of AGV body structure and motion state, transport distance and energy consumption are selected as two optimization objectives. According to the characteristics and application context of the problem, this paper designs a hybrid genetic algorithm with large neighborhood search (GA-LNS) considering the farthest insertion heuristic. GA-LNS is improved by increasing the local search ability of genetic algorithm to enhance the solution optimal quality. Extensive computational experiments which are generated from Solomon’s benchmark instances and a real case of FMS are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results reveal that compared with using the traditional homogeneous fleet, the heterogeneous multitype AGV fleet transportation mode has a huge energy-saving potential in workshop intralogistics. Full article
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