Modeling and Scheduling a Constrained Flowshop in Distributed Manufacturing Environments

Journal of Manufacturing Systems, Год журнала: 2024, Номер 72, С. 519 - 535

Опубликована: Янв. 9, 2024

Язык: Английский

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Understanding electric vehicle charging network resilience: Resilience curves and interpretable machine learning

Transportation Research Part D Transport and Environment, Год журнала: 2025, Номер 142, С. 104709 - 104709

Опубликована: Март 21, 2025

Язык: Английский

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A new EDA algorithm combined with Q-learning for semiconductor final testing scheduling problem

Computers & Industrial Engineering, Год журнала: 2024, Номер 193, С. 110259 - 110259

Опубликована: Май 31, 2024

Язык: Английский

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Incentive-based customer-oriented rebalancing strategy for one-way shared electric vehicles in sustainable urban governance

Journal of Cleaner Production, Год журнала: 2024, Номер 469, С. 143192 - 143192

Опубликована: Июль 17, 2024

Язык: Английский

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Stochastic Modeling of Electric Vehicle Infrastructure Using Queueing-Theoretical Approach

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104149 - 104149

Опубликована: Янв. 1, 2025

Язык: Английский

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Research on real-time prediction of completion time based on AE-CNN-LSTM

Computers & Industrial Engineering, Год журнала: 2023, Номер 185, С. 109677 - 109677

Опубликована: Окт. 11, 2023

Язык: Английский

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Remanufacturing system scheduling of batch products with the consideration of dynamic changes in machine efficiency using an improved artificial bee colony algorithm

Computers & Industrial Engineering, Год журнала: 2023, Номер 187, С. 109817 - 109817

Опубликована: Дек. 7, 2023

Язык: Английский

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Predicting and Forecasting of Vehicle Charging Station Using ECNN with DHFO Algorithm

Energies, Год журнала: 2024, Номер 17(17), С. 4308 - 4308

Опубликована: Авг. 28, 2024

The forecast of the optimal placement a charging station (CS) according to real-time consumption electric vehicles is subject urgency in this new era. demand an area based on trend can be predicted by means interpolation and extrapolation historical data using linear function prediction model. system was performed with distance relevancy methods. An adaptive learning model proposed enhance performance for management represent pattern vehicles’ travelling directions. uses Distributional Homogeneity Feature Optimization (DHFO) artificial intelligence (AI) categorize from database. improved more than conventional classification filtering apt features all vehicular attributes Enhanced Cladistic Neural Network (ECNN) used improve increase accuracy. By comparing statistical parameters other state-of-the-art methodologies, suggested model’s overall findings were verified.

Язык: Английский

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Rolling Time Domain Charging Allocation of Electric Vehicles Under Time Varying Demand

IEEE Access, Год журнала: 2024, Номер 12, С. 14411 - 14422

Опубликована: Янв. 1, 2024

To study the impact of traffic conditions urban road networks and distribution potential demand charging users on in region. In this paper, long short-time memory (LSTM) neural network is used to learn historical user behavior data predict real-time. An integer programming model for allocation electric vehicles rolling time domain under time-varying established by taking multiple stations region as research objects. Aiming at highest comprehensive return, rejects some with a residence before allocated peak carries out charging. When high during hours, early rejection long-staying real-time appointments can increase revenue utilization rate piles. case that pile be saturated times, based LSTM 20.45% average piles 2.35% throughout day. During station increased 36.63% average, 13.54% average.

Язык: Английский

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The optimal operation decision of gas station considering charging pile installation – conflict and coordination

Kybernetes, Год журнала: 2024, Номер unknown

Опубликована: Июнь 6, 2024

Purpose In the construction of charging piles, traditional gas stations possess significant advantages in terms regional and financial resources. The transformation into “refueling+charging” integrated relies on pile manufacturers government, involving coordination issues with them. This paper aims to propose a joint contract based principles cost-sharing revenue-sharing. objective is achieve systemic among stations, manufacturers, optimizing planning quantity piles prices. Design/methodology/approach We have constructed an operational system model Stackelberg game between government. analyzed optimal prices under impact government subsidy policies both decentralized centralized operation scenarios. Additionally, we proposed revenue-sharing coordinate this tripartite system. Findings study reveals that, simple cooperative contracts, decision does not yield maximum profits for due “double-marginal effect”. However, contract, which combines as paper, will consider manufacturer’s costs subsidies when determining price. only achieves but also results Pareto improvement benefits all members by adjusting parameters. Originality/value value research lies its insights strategies electric vehicles. By analyzing decisions different arrangements, provides guidance relevant stakeholders, enabling greater efficiency realize more extensive improvements. Furthermore, it extends application theory context operations.

Язык: Английский

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