Research on Vehicle and Cargo Matching of Electric Materials Based on Weed Optimization Algorithm

Huang, Dedi; Gao, Yanlong; Zhu, Jun; Ren, Bohan

doi:10.1007/978-981-19-9968-0_46

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Tian et al [21] proposed a vehicle-cargo matching algorithm based on an improved dynamic Bayesian network. Yu et al [22] proposed a vehicle-cargo matching model based on the weed optimization algorithm and integrated various factors to establish an objective function for calculation. Wang et al [23] established a vehicle routing optimization model considering goods-vehicle matching and three-dimensional loading constraints.…”

Section: Research On Vehicle-cargo Matching Problemmentioning

confidence: 99%

Optimization of Truck–Cargo Online Matching for the Less-Than-Truck-Load Logistics Hub under Real-Time Demand

Tang,

Chen,

Lang

et al. 2024

Mathematics

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Reasonable matching of capacity resources and transported cargoes is the key to realizing intelligent scheduling of less-than-truck-load (LTL) logistics. In practice, there are many types and numbers of participating objects involved in LTL logistics, such as customers, orders, trucks, unitized implements, etc. This results in a complex and large number of matching schemes where truck assignments interact with customer order service sequencing. For the truck–cargo online matching problem under real-time demand, it is necessary to comprehensively consider the online matching process of multi-node orders and the scheduling of multi-types of trucks. Combined with the actual operation scenario, a mixed-integer nonlinear programming model is introduced, and an online matching algorithm with a double-layer nested time window is designed to solve it. By solving the model in a small numerical case using Gurobi and the online matching algorithm, the validity of the model and the effectiveness of the algorithm are verified. The results indicate that the online matching algorithm can obtain optimization results with a lower gap while outperforming in terms of computation time. Relying on the realistic large-scale case for empirical analysis, the optimization results in a significant reduction in the number of trips for smaller types of trucks, and the average truck loading efficiency has reached close to 95%. The experimental results demonstrate the general applicability and effectiveness of the algorithm. Thus, it helps to realize the on-demand allocation of capacity resources and the timely response of transportation scheduling of LTL logistics hubs.

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Section: Research On Vehicle-cargo Matching Problemmentioning

confidence: 99%

Optimization of Truck–Cargo Online Matching for the Less-Than-Truck-Load Logistics Hub under Real-Time Demand

Tang,

Chen,

Lang

et al. 2024

Mathematics

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“…The combined approach achieves a greater level of optimization accuracy and a faster rate of convergence as a result of the population diversity, which improves the algorithm's capacity for global searching which increases its capacity for local extractive activities. [20,21] Both of these factors contribute to the hybrid algorithm. The suggested approach's performance has been evaluated for several recognized standard problems in kinematics, chemistry, combustion, and medicine.…”

Section: Differential Evolution Invasive Weed Optimization (Deiwo) Al...mentioning

confidence: 99%

Optimal Location of Facts Devices in IEEE 14 Bus System Using Ga and Deiwo

CHETHAN¹,

Ravi²

2023

Preprint

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FACTS devices provide significance to enhance the functioning of both static and dynamic power systems. The type, size, and location of FACTS devices determine how well they achieve the goals of enhancing voltage stability, loadability, and loss minimization simultaneously. The aim of the function in this study includes voltage stability, line loadings, and loss minimization, which are expected to obtain the most benefits from their installation and other weights allocated to them. It has been determined how installing TCSC, and SVC will improve loadability under situations of continuously increasing load. In this study, a novel optimization technique known as differential evolution invasive weed optimization (DEIWO) is utilized to locate the optimal location for FACTS devices within the IEEE 14 bus system by considering valve point effects. DEIWO was a recently developed, dominant, and simple metaheuristic algorithm for genuine parameter optimization. DEIWO was inspired by the natural process of weed colonization and dissemination, and it is highly effective at addressing broad multi-dimensional, linear and nonlinear optimization problems. The proposed method was implemented in the IEEE 14 bus testing system. Different methods from the research papers have been used to compare and analyze the results. The results demonstrate the DEIWO algorithm's capability and show how it may be used in practice to locate FACTS devices in deregulated electricity networks in the most advantageous place. A comparison of the proposed process to existing algorithms found in the literature and implemented using traditional techniques reveals that it performs better in terms of precision and convergence

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Research on Vehicle and Cargo Matching of Electric Materials Based on Weed Optimization Algorithm

Cited by 2 publications

References 6 publications

Optimization of Truck–Cargo Online Matching for the Less-Than-Truck-Load Logistics Hub under Real-Time Demand

Optimization of Truck–Cargo Online Matching for the Less-Than-Truck-Load Logistics Hub under Real-Time Demand

Optimal Location of Facts Devices in IEEE 14 Bus System Using Ga and Deiwo

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