Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(2)

Published: Jan. 8, 2025

Language: Английский

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A low-carbon transportation network: Collaborative effects of a rail freight subsidy and carbon trading mechanism

Transportation Research Part A Policy and Practice, Journal Year: 2024, Volume and Issue: 184, P. 104066 - 104066

Published: April 26, 2024

Language: Английский

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Snake Optimization Algorithm Augmented by Adaptive t-Distribution Mixed Mutation and Its Application in Energy Storage System Capacity Optimization

Biomimetics, Journal Year: 2025, Volume and Issue: 10(4), P. 244 - 244

Published: April 16, 2025

To address the drawbacks of traditional snake optimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation strategy is proposed. Initially, Tent-based chaotic mapping quasi-reverse learning approach are utilized to enhance quality initial solution initialization process original method. During evolution stage, novel foraging introduced substitute stage This perturbs mutates at optimal position generate new solutions, thereby improving algorithm’s ability escape local optima. The mating mode in replaced with opposite-sex attraction mechanism, providing algorithm more opportunities for global exploration exploitation. improved method accelerates improves accuracy while balancing exploitation capabilities. experimental results demonstrate that outperforms other methods, including standard technique, terms robustness accuracy. Additionally, each improvement technique complements amplifies effects others.

Language: Английский

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A novel multi-strategy combined whale optimization algorithm for cascade reservoir operation of complex engineering optimization.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112917 - 112917

Published: Feb. 1, 2025

Language: Английский

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Deep reinforcement learning based low energy consumption scheduling approach design for urban electric logistics vehicle networks

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 15, 2025

The rapid increase in carbon emissions from the logistics transportation industry has underscored urgent need for low-carbon solutions. Electric vehicles (ELVs) are increasingly being considered as replacements traditional fuel-powered to reduce urban logistics. However, ELVs typically limited by their battery capacity and load constraints. Additionally, effective scheduling of charging management duration critical factors that must be addressed. This paper addresses low energy consumption (LECS) problem, which aims minimize total heterogeneous with varying capacities, considering availability multiple stations (CSs). Given complexity LECS this study proposes a attention model based on encoder-decoder architecture (HAMEDA) approach, employs graph network introduces novel decoding procedure enhance solution quality learning efficiency during encoding phases. Trained via deep reinforcement (DRL) an unsupervised manner, HAMEDA is adept at autonomously deriving optimal routes each ELV specific cases presented. Comprehensive simulations have verified can diminish overall utilization no less than 1.64% compared other heuristic or learning-based algorithms. excels maintaining advantageous equilibrium between execution speed solutions, rendering it exceptionally apt expansive tasks necessitate swift decision-making processes.

Language: Английский

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Optimization and Implementation Strategy for Low-Carbon Multimodal Transport Routes: A Collaborative Approach between Government and Transport Enterprises

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 145341 - 145341

Published: March 1, 2025

Language: Английский

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Stochastic optimization for on-time delivery in high-speed railway meal services: balancing earliness and tardiness costs

Industrial Management & Data Systems, Journal Year: 2025, Volume and Issue: unknown

Published: March 26, 2025

Purpose This study explores optimizing high-speed railway (HSR) meal services, a unique logistical challenge requiring precise alignment with train departure times. Unlike standard delivery systems, HSR services demand strict on-time delivery, balancing the conflicting costs of earliness and tardiness while accounting for stochastic nature preparation processes. Design/methodology/approach A single-machine scheduling model is developed to minimize expected in delivery. The problem formulated as two-stage mixed-binary program, incorporating uncertainties intermodal coordination. surrogate algorithm proposed enhance computational efficiency, particularly large sizes. Extensive numerical experiments based on real-world scenarios are conducted validate algorithm. Findings significantly improves efficiency maintaining high solution accuracy. It outperforms commercial solvers sample sizes highlights importance uncertainties. Particularly, size increases, this can even match optimal (i.e. 0% performance gap) 63.594% reduction computation time. Originality/value bridges gap integrating synchromodal logistics principles into services. provides innovative methodologies synchronizing operations across transport modes, addressing both cost objectives system findings offer actionable insights time-sensitive, industry beyond.

Language: Английский

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Modelling the Determinants to Develop a Sustainable Supply Chain in Taiwan’s Semiconductor Manufacturing Industry

Published: Jan. 1, 2025

Language: Английский

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Navigating uncertainties using type Ⅰ sensitivity analysis for transportation problems: A mid-width technique with crisp and fuzzy scenarios

Journal of Industrial and Management Optimization, Journal Year: 2025, Volume and Issue: 0(0), P. 0 - 0

Published: Jan. 1, 2025

Language: Английский

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Enhanced dung beetle optimization algorithm and its application in 3D UAV path planning

Electronic Research Archive, Journal Year: 2025, Volume and Issue: 33(4), P. 2618 - 2667

Published: Jan. 1, 2025

Language: Английский

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