Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Dynamic Scheduling and Path Planning of Automated Guided Vehicles in Automatic Container Terminal

The uninterrupted operation of the quay crane(QC)ensures that the large container ship can depart port within laytime,which effectively reduces the handling cost for the container terminal and ship owners.The QC waiting caused by automated guided vehicles(AGVs)delay in the uncertain environment can be alleviated by dynamic scheduling optimization.A dynamic scheduling process is introduced in this paper to solve the AGV scheduling and path planning problems,in which the scheduling scheme determines the starting and ending nodes of paths,and the choice of paths between nodes affects the scheduling of subsequent AGVs.This work proposes a two-stage mixed integer optimization model to minimize the transportation cost of AGVs under the constraint of laytime.A dynamic optimization algorithm,including the improved rule-based heuristic algorithm and the integration of the Dijkstra algorithm and the Q-Learning algorithm,is designed to solve the optimal AGV scheduling and path schemes.A new conflict avoidance strategy based on graph theory is also proposed to reduce the probability of path conflicts between AGVs.Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm over existing methods.

Energy-aware Integrated Scheduling for Container Terminals with Conflict-free AGVs

For automated container terminals,the effective integrated scheduling of different kinds of equipment such as quay cranes(QCs),automated guided vehicles(AGVs),and yard cranes(YCs)is of great significance in reducing energy consumption and achieving sustainable development.Aiming at the joint scheduling of AGVs and YCs with consideration of conflict-free path planning for AGVs as well as capacity constraints on AGV-mate which is also called buffer bracket in blocks,a mixed integer programming model is established to minimize the energy consumption of AGVs and YCs for the given loading/unloading task.A solution method based on a novel bi-level genetic algorithm(BGA),in which the outer and the inner layer search the optimal dispatching strategy for QCs and YCs,respectively,is designed.The validity of the model and the algorithm is verified by simulation experiments,which take the Port of Qingdao as an example and the performance under different conflicting resolution strategies is compared.The results show that,for the given task,the proposed solution to conflict-free path and the schedule provided by the algorithm can complete the task with minimum energy consumption without loss of AGVs utilization,and the number of AGV-mates should be adjusted according to the task rather than keeping unchanged.Comparison results indicate that our proposed approach could efficiently find solutions within 6%optimality gaps.Energy consumption is dropped by an average of 15%.