A Hybrid Dynamic Method for Conflict-Free Integrated Schedule Optimization in U-Shaped Automated Container Terminals

Xu, Baoyun; Jie, Depei; Li, Junjun; Zhou, Yifeng; Wang, Hailin; Fan, Huiyao

doi:10.3390/jmse10091187

Cited by 11 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stage 5: The yard crane spreader vertically lowers container j to predetermined position. The time, distance, and speed of spreader running are 𝑡 𝑗 15 , 𝑑 𝑗 15 , 𝑣 𝑗 15 respectively.…”

Section: The Time Model Of Low Operation Platform For U-shaped Actmentioning

confidence: 99%

See 1 more Smart Citation

The Influence of High Operation Platform Mode on U-Shaped Automated Container Terminal Efficiency

Li,

Zhou,

Zhu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Improving operational efficiency and reducing energy consumption are common objectives for all ports.In previous research by authors, a new high operation platform mode was proposed. It had been proven to reduce energy consumption and be economically feasible. On this basis, this paper studied the influence of high operation platform mode on automated container terminal (ACT) efficiency. This paper took U-shaped ACT as the research object, put forward the operation characteristics of U-shaped ACT under high operation platform mode, and constructed the operation time model of high and low operation platform mode respectively. This paper took 30 containers at the same bay as an example to calculate and verify. The computational results shows that when 𝑉 𝑣 1 /𝑉 ℎ 1 is above the 𝑉 𝑣 1 /𝑉 ℎ 1 − 𝛼 curve, the operation time of high platform mode is shorter, and the operation efficiency of ACT is higher (𝑉 𝑣 1 represents the speed of spreader with container vertically moving; 𝑉 ℎ 1 represents the speed of spreader with container horizontally moving.); when 𝑉 𝑣 1 /𝑉 ℎ 1 is under the 𝑉 𝑣 1 /𝑉 ℎ 1 − 𝛼 curve, the shortening time of quay cranes operation is greater than the increasing time of yard cranes operation, so the overall operational efficiency of ACT is still improved. Therefore, high operation platform mode can improve efficiency of ACT.

show abstract

Section: The Time Model Of Low Operation Platform For U-shaped Actmentioning

confidence: 99%

“…The results indicated that U-shaped layout design had best performance. [15] provided a hybrid programming model for conflict-free integrated scheduling of quay cranes, AGVs, and double-cantilever rail cranes. The results showed the model could optimize multi-equipment scheduling and improve ACT efficiency.…”

Section: Introductionmentioning

confidence: 99%

The Influence of High Operation Platform Mode on U-Shaped Automated Container Terminal Efficiency

Li,

Zhou,

Zhu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The Problem Model Algorithm [29] Dynamic routing -Cluster reschedule [30] Railway scheduling with dynamic passenger demand MILP ALNS [31] Dynamic large-scale urban transportation Network Simulation [32] Dynamic job shop scheduling MIP GA [7] Dynamic routing with congestion Set Covering Batch assignment [33] Dynamic workload management for cranes -DSTP [34] Railway crew scheduling ILP CG + LR [35] Airline crew scheduling QP CG [36] A review of dynamic vehicle routing problems -- [37] Metro train scheduling dynamic passenger demand MILP LR [6] Crane scheduling DP Heuristic + Exact [8] Dynamic routing -B and P + CG [9] Dynamic job shop scheduling MILP PSO [38] Dynamic bus scheduling MIQP RO [39] Dynamic vehicle allocation in ridesharing MIQP ADP + LR [40] Dynamic job shop scheduling MDP DQN [41] Dynamic routing MILP DDD + LR [42] Dynamic routing MDP - [43] Dynamic job shop scheduling DRL PPO [44] Dynamic job shop scheduling MDP DQN [10] Dynamic routing MILP Heuristic…”

Section: Literaturementioning

confidence: 99%

“…Moreover, the re-optimized scheme takes effect as soon as possible to ensure continuous operation. In the literature, heuristic algorithms are widely used for solving dynamic scheduling problems, e.g., [6][7][8][9][10].Among them, the batch-based task assignment and the rolling-horizon are usually developed to cope with uncertainty and large-scale operations at terminals. Inspired by these approaches, we divide the task to be handled within a planning period into several batches.…”

Section: Introductionmentioning

confidence: 99%

An Iterative Re-Optimization Framework for the Dynamic Scheduling of Crossover Yard Cranes with Uncertain Delivery Sequences

Wang

2023

JMSE

View full text Add to dashboard Cite

In yard-crane scheduling problems, as loading operations take priority over unloading, the delivery sequence of unloading from the quaysides to the yard is uncertain. The delivery sequence changes may make crane scheduling more difficult. As a result, the crane operations schedules developed statically become suboptimal or even infeasible. In this paper, we propose a dynamic scheduling problem considering uncertain delivery sequences. A mixed-integer linear program is developed to assign tasks to cranes and minimize the makespan of crane operations. We propose an iterative solution framework in which the schedules are re-optimized whenever the delivery sequence change is revealed. A genetic algorithm is proposed to solve the problem, and a greedy algorithm is designed to re-optimize and update the solution. To make the updated solution take effect as soon as possible, regarding batch-based task assignment, the tasks in the scheduling period are divided into several batches. In this case, the instant requests arising from the delivery sequence change are added to corresponding batch tasks and re-optimized together with the tasks of this batch. In addition, a relaxation model is formulated to derive a lower bound for demonstrating the performance of the proposed algorithm. Experimental results show that the average gap between the algorithm and the lower bound does not exceed 5%. The greedy insertion algorithm can re-optimize the instant requests in time. Therefore, the proposed iterative re-optimization framework is feasible and has the advantages (necessity) of batch-based task assignment.

show abstract

“…In the U-Automated Container Terminal, the interaction positions between internal trucks and yard cranes have changed. Scholars [11][12][13] , taking…”

Section: Introductionmentioning

confidence: 99%

Allocation model and algorithm for multiple container areas at U-automated container terminal

Ye,

2024

Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023)

View full text Add to dashboard Cite

In order to address the problem of container allocation at the container terminal of a U-shaped terminal layout, taking into account the actual conditions of multi-container area storage at the terminal, a model for allocating multiple container areas for outbound containers is established with the objective of minimizing the rehandling time and IGV transport time during container loading and unloading operations. A variable neighborhood search algorithm, considering storage rules, is designed to solve the problem of container allocation in the multi-container area. The effectiveness of the model and algorithm is verified through numerical experiments, and a sensitivity analysis of the balance of operations between container areas is conducted. The experimental results demonstrate that considering the balance of operations between container areas can effectively reduce the rehandling time during container loading and unloading operations, then improving the efficiency of the container terminal.

show abstract

A Hybrid Dynamic Method for Conflict-Free Integrated Schedule Optimization in U-Shaped Automated Container Terminals

Cited by 11 publications

References 30 publications

The Influence of High Operation Platform Mode on U-Shaped Automated Container Terminal Efficiency

The Influence of High Operation Platform Mode on U-Shaped Automated Container Terminal Efficiency

An Iterative Re-Optimization Framework for the Dynamic Scheduling of Crossover Yard Cranes with Uncertain Delivery Sequences

Allocation model and algorithm for multiple container areas at U-automated container terminal

Contact Info

Product

Resources

About