Split-demand multi-trip vehicle routing problem with simultaneous pickup and delivery in airport baggage transit

Zhang, Zhenzhen; Che, Yuxin; Liang, Zhe

doi:10.1016/j.ejor.2023.07.028

Cited by 13 publications

(2 citation statements)

References 27 publications

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“…Constraints ( 14)- (16) indicate that the number of de-icing vehicles matches the flight type. As for constraints (17) and (18), they denote that the unmanned de-icing vehicles put into service depart from the garage and eventually return to the garage. Finally, constraint (19) denotes that the flow of vehicles in and out of the de-icing position is conserved.…”

Section: Lower Level Modeling-fsm-mdvrp For Unmanned De-icing Vehiclesmentioning

confidence: 99%

“…Research related to this topic is relatively infrequent; however, insights can be drawn from the scheduling of other specialized vehicles within the airport. For instance, for baggage transfer vehicles within the airport, Zhang et al [17] considered real-world conditions, including split demands, multiple trips, and concurrent pickups and deliveries. They employed topological sorting to address these intricate dependencies and define the start time for each service in order to efficiently schedule vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

Liu,

Huang,

Han

et al. 2024

Biomimetics

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Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach’s efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved.

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Section: Lower Level Modeling-fsm-mdvrp For Unmanned De-icing Vehiclesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

Liu,

Huang,

Han

et al. 2024

Biomimetics

View full text Add to dashboard Cite

show abstract

The third party logistics provider freight management problem: a framework and deep reinforcement learning approach

Abbasi-Pooya,

Lash

2024

Ann Oper Res

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Column generation based hybrid optimization method for last-mile delivery service with autonomous vehicles

Hu,

Qin,

et al. 2024

Advanced Engineering Informatics

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Split-demand multi-trip vehicle routing problem with simultaneous pickup and delivery in airport baggage transit

Cited by 13 publications

References 27 publications

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem

The third party logistics provider freight management problem: a framework and deep reinforcement learning approach

Column generation based hybrid optimization method for last-mile delivery service with autonomous vehicles

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