Community logistics for dynamic vehicle dispatching: The effects of community departure “time” and “space”

Ouyang, Zhiyuan; Leung, Eric Ka Ho; Huang, George Q.

doi:10.1016/j.tre.2022.102842

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…There are also several relevant studies considering the scheduling of logistics vehicles within a restricted area, such as community logistics and in-plant logistics. Ouyang et al [11] proposed a novel postponement prioritized route optional approach, formulated two dynamic policies to manage temporal and spatial uncertainties, and evaluated the policies through simulation experiments on large-scale instances. Diefenbach et al [12] proposed a scheduling problem of electric vehicles in an in-plant logistics setting in which the actual battery charging function is considered and proposed an exact approach based on branch-and-check for minimizing the fleet size.…”

Section: Research On Logistics Vehicle Scheduling 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 Logistics Vehicle Scheduling Problemmentioning

confidence: 99%