A two-stage hybrid heuristic solution for the container drayage problem with trailer reposition

Cui, Haipeng; Chen, Shukai; Chen, Rui; Meng, Qiang

doi:10.1016/j.ejor.2021.06.040

Cited by 17 publications

(3 citation statements)

References 37 publications

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“…Yang et al [6] extended the CDP with multiple depots by considering a heterogeneous fleet. Unlike the above studies, Cui et al [2] and Jia et al [43] studied container drayage operations with tractor-trailer distinction and trailer repositioning, but they ignored empty container constraints, empty container reallocation, and trucking operation modes.…”

Section: The Cdp With Multiple Depotsmentioning

confidence: 99%

“…Hence, short-haulage by trucks, which is also called container drayage, becomes an essential link in container intermodal transport [1]. However, according to statistics, although the distance is relatively short compared to maritime and rail transport, container drayage accounts for 25% to 40% of the total container transportation costs [2]. Furthermore, it also significantly contributes to shipment delays, road congestion, and increased carbon emissions [3].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Optimization of Container Drayage Problem with Empty Container Constraints across Multiple Inland Depots

Yu,

Feng,

et al. 2024

Sustainability

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Container drayage involves the transportation of containers by trucks. Although the distance is relatively short compared to maritime and rail transport, container drayage accounts for 25% to 40% of the total container transportation costs and significantly contributes to increased fuel consumption and carbon emissions. Thus, the modeling of the container drayage problem (CDP) has received a lot of attention in the last two decades. However, the three fundamental modeling factors, including the combination of trucking operation modes and empty container relocation strategies, as well as empty container constraints and multiple inland depots, have not been simultaneously investigated. Hence, this study addressed a comprehensive CDP that simultaneously incorporates the three modeling factors. The problem was formulated as a novel mixed integer linear programming (MILP) model based on the DAOV graph. Given the complexity of this problem, it was not realistic to find an exact solution for large instances. Therefore, an improved genetic algorithm (GA) was designed by integrating the “sequential insertion” method and “solution re-optimization” operation. The performance of Gurobi and GA was validated and evaluated through randomly generated instances. The results indicate that (1) the proposed algorithm can provide near-optimal solutions for large-scale instances within a reasonable running time, (2) the greatest cost savings from combining trucking operation modes and empty container relocation strategies range from 10.45% to 31.86%, and (3) the three modeling factors significantly influence the fuel consumption and carbon emissions, which can provide managerial insights for sustainable container drayage practices.

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Section: The Cdp With Multiple Depotsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Container Drayage Problem with Empty Container Constraints across Multiple Inland Depots

Yu,

Feng,

et al. 2024

Sustainability

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“…They proposed an approach based on an integer programming heuristic that determines pickup and delivery sequences for daily drayage operations while ensuring a minimum transportation cost. In the same context, the authors in [16] demonstrated that drayage operations could be considered as a multi-stop VRPB. More details regarding VRPB and VRPPD can be found in two comprehensive overviews provided in [17,18], respectively.…”

mentioning

confidence: 99%

Mixed Integer Linear Programming Models to Solve a Real-Life Vehicle Routing Problem with Pickup and Delivery

et al. 2021

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This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

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A variable neighborhood search algorithm for the location problem of platoon formation center

Xue,

Peng,

Cui

2023

Flex Serv Manuf J

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A two-stage hybrid heuristic solution for the container drayage problem with trailer reposition

Cited by 17 publications

References 37 publications

Modeling and Optimization of Container Drayage Problem with Empty Container Constraints across Multiple Inland Depots

Modeling and Optimization of Container Drayage Problem with Empty Container Constraints across Multiple Inland Depots

Mixed Integer Linear Programming Models to Solve a Real-Life Vehicle Routing Problem with Pickup and Delivery

A variable neighborhood search algorithm for the location problem of platoon formation center

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