Multi-disruption resilient hub location–allocation network design for less-than-truckload logistics

Transportation Research Part A Policy and Practice, Journal Year: 2024, Volume and Issue: 190, P. 104260 - 104260

Published: Sept. 19, 2024

Language: Английский

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The Application of the SubChain Salp Swarm Algorithm in the Less-Than-Truckload Freight Matching Problem

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4436 - 4436

Published: April 17, 2025

The less-than-truckload (LTL) freight problem is a general pain point in logistics applications. Its challenge resides the fact that these loads cannot be shipped timely manner due to their relatively small volumes. Traditional LTL matching methods are challenged by delays updating logistic information and higher distribution costs. In order solve challenges, we developed novel SubChain Salp Swarm Algorithm (SSSA) improving traditional with utilization of operation. Our method aims find optimal strategy for maintaining balance between lower operating costs customer satisfaction. SSSA combines multiple disconnected points separate individual chains local optima obtain better convergence results final decision. We have compared our mainstream metaheuristic algorithms using datasets from road company Hangzhou, demonstrate converges faster than other has variance. solves limitation observed optimization improves service relation transportation issue.

Language: Английский

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Multi-Objective Optimization of Short-Inverted Transport Scheduling Strategy Based on Road–Railway Intermodal Transport

Sustainability, Journal Year: 2024, Volume and Issue: 16(15), P. 6310 - 6310

Published: July 24, 2024

This study focuses on the ‘short-inverted transportation’ scenario of intermodal transport. It proposes a vehicle unloading reservation mechanism to optimize point-of-demand scheduling system for inefficiency transport due complexity and uncertainty strategy. paper establishes strategy optimization model minimize cost short backhaul obtain shortest delivery time window designs hybrid NSGWO algorithm suitable multi-objective solve problem. The incorporates Non-dominated Sorting Genetic Algorithm II (NSGA-II) based Grey Wolf Optimizer (GWO) algorithm, compensating single algorithm’s premature convergence. experiment selects logistics carrier’s actual road–rail short-inverted data compares verifies above data. results show that scheme obtained by this can save 41.01% shorten total 46.94% compared with original scheme, which effectively protect enterprise’s economic benefits while achieving timely delivery. At same time, optimized plan resulted in lower number vehicles, positively impacted sustainability green logistics.

Language: Английский

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Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 166, P. 112190 - 112190

Published: Sept. 1, 2024

Language: Английский

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Optimization of Truck–Cargo Matching for the LTL Logistics Hub Based on Three-Dimensional Pallet Loading

Mathematics, Journal Year: 2024, Volume and Issue: 12(21), P. 3336 - 3336

Published: Oct. 24, 2024

This study investigates the truck–cargo matching problem in less-than-truckload (LTL) logistics hubs, focusing on optimizing three-dimensional loading of goods onto standardized pallets and assigning these loaded to a fleet heterogeneous vehicles. A two-stage hybrid heuristic algorithm is proposed solve this complex challenge. In first stage, tree search based residual space developed determine optimal layout for 3D cargo pallets. second online introduced allocate trucks while number used minimizing transportation costs. The operates within rolling time horizon, allowing it dynamically handle real-time order arrivals window constraints. Numerical experiments demonstrate that method achieves high pallet efficiency (close 90%), near-optimal truck utilization (nearly 95%), significant cost reductions, making practical solution real-world LTL operations.

Language: Английский

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Research on the Method of Bilateral Vehicle-Cargo Matching Strategy Based on Bayesian Estimation

Operations Research and Fuzziology, Journal Year: 2024, Volume and Issue: 14(06), P. 101 - 115

Published: Jan. 1, 2024

Language: Английский

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