Optimization Approach for Yard Crane Scheduling Problem with Uncertain Parameters in Container Terminals

As the core operational issue in container terminals, yard crane scheduling problem directly affects the overall operation efficiency of port connecting highway or railway transportation and sea transportation. In practice, the scheduling of yard cranes is subject to many uncertain factors, so the scheme may be inapplicable and needs to be adjusted. From the perspective of proactive strategy, considering fluctuations in arrival time of external trucks as well as varied handling volume of yard cranes, a stochastic programming model is established in this paper to obtain a fixed scheme with the minimum expected value of yard crane makespan and total task waiting time over all the scenarios. The scheme does not require rescheduling when facing different situations. Subsequently, two algorithms based on certain rules are proposed to obtain the yard crane operation scheme in the deterministic environment, which are taken as the basic solution in the uncertain conditions, and then a tailored genetic algorithm is adopted to find the optimal solution with good adaptability to the uncertain scenarios. Finally, we use small-scale examples to compare the performance of algorithms in the deterministic and uncertain environment and then analyze the relationship between different yard crane configurations and the number of tasks. Large-scale experiments are performed to study the operation efficiency of the storage yard with different handling volumes assigned to each yard crane.

Yard Crane Scheduling in the Ground Trolley-Based Automated Container Terminal

The yard crane (YC) scheduling problem is studied for a new terminal design, known as the Ground Trolley-based Automated Container Terminal (GT-ACT). At the operational level, the YC scheduling problem considering GT could be formalized as job sequencing. The cooperative operation of the twin YCs, GT and the transport vehicles is complicated, a small change in the operation sequence may cause a chain reaction, which could overthrow the partial sequence preferred previously. A hybrid algorithm based on the nested partition (NP) framework is proposed. The ant colony (AC) algorithm is combined in the method for solution construction, while genetic algorithm (GA) is used for the perturbation and backtrack purposes. Experiments show the hybrid method is effective for practical problems under different scales. The AC and GA algorithms play an important role in improving the optimization.

MILP Model and a Rolling Horizon Algorithm for Crane Scheduling in a Hybrid Storage Container Terminal

This paper investigates the yard crane scheduling problem of a hybrid storage container terminal whose import containers and export containers are stored together in each block. The combination of containers improves the space utilization of a container terminal while it also creates new challenges for the yard crane scheduling. To formulate this problem, we propose a mixed integer linear programming (MILP) model, which jointly optimizes trucks’ waiting costs and penalty costs caused by exceeding waiting time thresholds. Considering the NP-completeness of this scheduling problem, we develop an efficient rolling horizon algorithm based on some heuristics to reduce the computation time. Finally, computational studies are carried out to evaluate the performance of our method and the solutions obtained by CPLEX solver are used for benchmarking purposes.