Cooperative Scheduling of AGV and ASC in Automation Container Terminal Relay Operation Mode

Abstract

The key problem of operation optimization for automated container terminal is the coordinated scheduling of automated quay crane (QC), automated guided vehicle (AGV), and automated stacking crane (ASC). In order to solve this problem, this paper proposed a new method to optimize the scheduling of ASC and AGV. In the automated container terminal, each container block is equipped with twin ASCs. At the same time, buffer zones are set at both ends of the container block to achieve the decoupling operation among ASC, AGV, and container truck. Considering the buffer capacity constraint and twin ASC operation interference, and introducing the design of handshake area, this paper developed a collaborative scheduling model of AGV and ASC in automatic terminal relay operation mode. This model is designed based on the genetic algorithm. The model aims to minimize the AGV waiting time and the ASC running time. The results indicated that the introduction of handshake area can effectively reduce the ASC operation interference by 10.56% on average. Also, it can be found that increasing buffer capacity can reduce the waiting time of AGV by about 4.25% on average, and the effect of relay operation is more obvious in large-scale operation. It was proved that buffer zone and handshake area can reduce task delay time and improve coordination between AGV and ASC.

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

“…The genetic algorithms (GAs), have adaptive and robust features which improve search diversity while avoiding premature convergence of the algorithm. For example, [15,17,20] used GAs to solve the crane scheduling problem including multiple operating devices. Table 1 lists the pioneering research on yard-crane scheduling.…”

“…The Problem Objective Function (Minimize) Model Algorithm [11] Twin-crane Interference-free -- [16] Twin-crane Makespan MILP Heuristic [21] Crane + YT Makespan MILP BD [14] AGV + ASC Makespan MCF B and B [13] Twin-crane Makespan MILP ALNS [17] Twin-ASC Makespan MILP GA [22] Multi-crane Delay time + Energy consumption MILP GA + PSO [18] Crane + YT Makespan Analytical GA + PSO [15] Twin-crane Makespan MILP GA + Greedy [3] SM + Crane Crane's movements cost + Penalty cost MIP B and B + CG [23] Crane + YT Makespan MILP DP [4] QC + AGV + Crane Berthing time CP Heuristic [24] Twin-crane Makespan MILP PSO [25] ASC + AGV Makespan MILP GA [26] Twin-crane Makespan MILP Heuristic + DP [2] Dual-ASC Makespan MILP NS [27] Twin-crane ASC and truck waiting time MILP Look-Ahead [20] Twin…”

“…Constraint (18) makes sure that the completion time of task i is strictly earlier than task j on a certain bay in the block. Constraints (19) and (20) constrain the ranges of the variables.…”

Improved Particle Swarm Approach for Dynamic Automated Guided Vehicles Dispatching

“…The authors developed a Congestion Prevention Rulebased bi-level genetic algorithm (CPR-BGA) to solve the proposed model. [12] proposed a new method for optimizing the ASC and AGV scheduling and a collaborative AGV and ASC scheduling model in automatic terminal. The proposed model is designed based on a genetic algorithm (GA) and aims to minimize the AGV waiting time and ASC running time.…”

AGVs Collision and Deadlock Handling Based on Structural Online Control Policy: A Case Study in a Square Topology

“…This method maintains a central schedule, which ensures provably conflict-free control of a large fleet of vehicles in almost any layout. Zhang et al [24] present a new method to optimize the scheduling of automated stacking cranes and automated guided vehicles for automated container terminals. It is based on the genetic algorithm.…”