Research On Multi-Yard Cranes Scheduling Model And Algorithm For Automated Container Terminals

As the novel coronavirus is effectively controlled and the economies of various countries are in a state of rapid recovery,container ports,as important nodes of global economic and trade transactions,will face enormous pressure and challenges in the rapid development of the future economy,therefore,it is crucial to improve the operational efficiency of container ports.At present,China’s container ports are rapidly transforming to semi-automation as well as full automation,and there are more and more studies on the efficiency improvement of automated terminal equipment,while the operation efficiency of container yard and bridge as a key part of improving port operation efficiency,the study of yard crane scheduling and related integration problems is of great significance to the improvement of port efficiency.Therefore,in this paper,the integration problem of container port reshuffling and multi-yard cranes scheduling is studied,and the main work is as follows:(1)Studied the collaborative optimization of reshuffling and yard crane scheduling.To address this issue,a mathematical model is established with the goal of minimizing the waiting time for external trucks,taking into account constraints such as container reshuffling operations and the inability to collide between yard cranes.The model is solved using the Adaptive Parameters Differential Evolution Algorithm(APDE).For small-scale problems,the results of the APDE algorithm were compared with the sum of the results obtained from solving the reshuffling problem and the yard crane scheduling problem through GAMS,respectively,to demonstrate the effectiveness of the algorithm and the necessity of studying the integration problem;In order to analyze the impact of different reshuffling methods on the efficiency of yard operations,comparative experiments were conducted on various common reshuffling methods under different yard utilization rates.The results showed that the OOR(Optimization of Redistribution)reshuffling method proposed in this paper has a significant effect on reducing reshuffling time when the stack utilization rate is greater than 70%,When the utilization rate of the yard is less than 70%,there is no significant difference between NR(No Rehandle Optimization)and RO(Rehandle Optimization)methods;In addition,in order to analyze the impact of different yard crane scheduling methods on the efficiency of yard crane operations,a comparison was made between the first come,first served,and nearest first served yard crane scheduling methods for different external truck arrival frequencies,with and without partition of the yard crane service scope.The results show that container terminals with busy operations use the method of dividing the scope of yard crane operations and nearest first take scheduling,which is more efficient,while container terminals with less busy operations use the method of dividing the scope of yard crane scheduling,which is first come first take scheduling,which is more efficient.Comparative experiments before and after algorithm improvement were conducted on large-scale examples,and the DE algorithm with the addition of optimal solution properties reduced the optimization solution by 33% and the solution time by 10.8%;After adding external archiving and parameter customization strategies,the APDE algorithm reduced the optimization solution by 5.1% compared to the standard differential evolution algorithm,and the solving time was reduced by 9.5%.At the same time,adding optimal solution properties,external archiving,and parameter customization strategies,the differential evolution algorithm optimized solution decreased by 36.4%,and the solving time was reduced by 19.2%.Proved the effectiveness of the proposed algorithm improvement strategy for solving the problem of reshuffling and yard crane integration.(2)Based on the analysis of practical factors with uncertain pickup time for external container terminals,this paper studies the scheduling problem of multiple yard cranes considering access tasks simultaneously,and designs a two-stage method to solve the problem.In the first stage,the yard crane allocation and operation sequence of storage tasks with known information were planned,with the goal of minimizing the completion time of two yard cranes.A mathematical model for the operation sequence of storage tasks considering inter yard crane interference constraints was established.Using GAMS to solve the small-scale storage task problem in the first stage,comparative experiments were conducted with DE algorithm to verify the accuracy of the mathematical model and the effectiveness of the algorithm;In the second stage,a heuristic solution is adopted,which adds unknown information to extract task information and obtains the complete yard crane allocation and operation sequence of all container tasks within that time period.By incorporating an improvement strategy to preserve the optimal individual gene fragment into the DE algorithm,experimental examples of different scales were designed.Comparative experiments were conducted on the algorithm improvement in various situations where the extraction task accounted for different proportions of the total task.The results showed that the average target value before and after the algorithm improvement was reduced by 15.54%,and the average solution time was reduced by 32.33%.Proved the effectiveness of algorithm improvement on this problem.