Scheduling Of Automated Guided Vehicles Research Articles

Quantum computing for several AGV scheduling models

Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.

Research on AGV scheduling and potential conflict resolution in port scenarios: based on improved genetic algorithm

In the research of Automated Guided Vehicle (AGV) scheduling, the most critical issues are the optimization of task allocation to AGVs and the handling of conflict scenarios. To address these challenges, we propose a scheme for AGV scheduling optimization and conflict resolution. To begin with, we introduce a novel improved genetic algorithm grounded on a combination strategy that re-encodes tasks into compound groupings, effectively simplifying large-scale integer programing problems into smaller, more manageable ones. Subsequently, the simplified problem is solved using an improved genetic algorithm. Test results validate that this method not only quickens the pace of finding solutions but also significantly improves the quality of those solutions. This is particularly evident when it comes to managing larger-scale optimization challenges. Furthermore, within AGV system conflict scenarios, this paper divides them into two primary categories: navigational conflicts and task quantity changes. For navigational conflicts, three resolution approaches are designed to address four different types of conflict situations: head-on, crossing, occupation, and chasing conflicts. Considering the fluctuations in task quantity, we developed strategies for rescheduling, non-rescheduling, and insertion rescheduling. Their performances were experimentally compared across various scales of scheduling problems, providing data support and theoretical basis for the selection of scheduling strategies in practical applications.

Real-time AGV scheduling optimisation method with deep reinforcement learning for energy-efficiency in the container terminal yard

The increasing vessel size and automation level have shifted the productivity bottleneck of automated container terminals from the terminal side to the yard side. Operating an automated container terminal (ACT) yard with a big number of automated guided vehicles (AGV) is challenging due to the complexity and dynamics of the system, severely affecting the operational efficiency and energy use efficiency. In this paper, a hybrid multi-AGV scheduling algorithm is proposed to minimise the energy consumption and the total makespan of AGVs in an ACT yard. This framework first models the AGV scheduling process as a Markov decision process (MDP). Furthermore, a novel scheduling algorithm called MDAS is proposed based on multi-agent deep deterministic policy gradient (MADDPG) to facilitate online real-time scheduling decision-making. Finally, simulation experiments show that the proposed method can effectively enhance the operational efficiency and energy use performance of AGVs in ACT yards of various scales by comparing with benchmarking methods.

Multi-AGV-Driven Pallet-Picking Scheduling Optimization (MADPSO): A Method for Flexible Multi-Level Picking Systems

In the context of increasingly competitive shipbuilding, the flexible multi-level picking system, composed of high-rise shelves, Automated Guided Vehicles (AGVs), and picking stations, has been of gradual interest because of its advantages in operation efficiency, system flexibility, and system robustness. Compared with other simple-level systems, the flexible multi-level picking system has a more complex coupling temporal relationship, which makes the scheduling optimization of shipbuilding automated collaborative order picking (SACOP) extremely difficult. In order to avoid the dilemma of finding a feasible and optimal collaborative scheduling scheme under the constraints of a complex temporal relationship, this paper proposed a multi-AGV-driven pallet-picking scheduling optimization (MADPSO) method, which takes the AGV scheduling scheme as the direct solution and modifies it to a feasible solution under the reasonably designed interaction strategy of stacker, AGV, and the interaction strategy of picking station, AGV. Furthermore, taking the minimum energy consumption and operation time as the optimization objectives, a multi-objective optimization mathematical model was established to describe MADPSO, and an improved NSGA-III algorithm was designed to solve the problem. Finally, several experiments were conducted in various scenarios and verified that using MADPSO can achieve a comprehensive optimization index improvement of 52.02–75.66% compared with traditional picking methods, which has a certain reference significance for shipyards.

Solving line balancing and AGV scheduling problems for intelligent decisions using a Genetic-Artificial bee colony algorithm

Due to the rapid advancement of technology, the demand for electronic devices in various sectors such as consumer electronics, automotive, telecommunications, healthcare, and industrial applications, as well as customized printed circuit board (PCB) products, has significantly increased. Balancing the PCB assembly line is crucial for improving productivity to meet market demand, which means balancing the workload distributed among various assembly stations on the assembly line. However, the workload balancing depends on material-handling systems in production environments that facilitate the transportation of raw materials to the buffer storage of the assembly stations. In the PCB assembly system, the material handling is carried out using automated guided vehicles (AGVs). This paper studies the assembly line balancing and AGV scheduling collectively because of their dependency on each other. A mixed integer linear programming (MILP) model is formulated to balance the workload distribution among stations and schedule the AGVs. An existing intelligent platform of the real-life PCB industry is considered to solve this integrated problem with intelligent decisions. The platform includes three layers modules: physical layer, data management layer and application service layer. A novel genetic artificial bee colony (GABC) algorithm is proposed and embedded with an application service layer to optimize the current problem and give optimum solutions for the real-life physical layer. The proposed GABC algorithm incorporates the operators of the genetic algorithm (GA) i.e., crossover and mutation into the search process of the ABC algorithm. Additionally, the greedy selection feature is employed in GABC which significantly enhances the exploitation capabilities, leading to faster convergence, higher-quality solutions, and improved robustness. The performance of the proposed GABC algorithm is tested based on different parameters by comparing the results with GA, ABC and PSO algorithms. Experimental analysis indicates that the proposed GABC algorithm outperforms the compared algorithms. Therefore, the yielded solutions in terms of prompt responses help managers to expedite the production through intelligent decision-making based on customer demands.

Multi-AGV Scheduling under Limited Buffer Capacity and Battery Charging Using Simulation Techniques

In recent years, automated guided vehicles (AGVs) have been widely adopted to automate material handling procedures in manufacturing shopfloors and distribution centers. AGV scheduling is the procedure of allocating a transportation task to an AGV, which has large impacts on the efficiency of an AGV system with multiple AGVs. In order to optimize the operations of multi-AGV systems, AGV scheduling decisions should be made with consideration of practical issues such as buffer space limitations and battery charging. However, previous studies have often overlooked those issues. To fill this gap, this paper proposes a simulation-based multi-AGV scheduling procedure for practical shopfloors with limited buffer capacity and battery charging. Furthermore, we propose three kinds of rules: job selection rules, AGV selection rules, and charging station selection rules, for AGV scheduling in practical shopfloors. The performance of the rules is evaluated through multi-scenario simulation experiments. The FlexSim software v.2022 is used to develop a simulation model for the experiments, and the experimental findings indicate that the job selection rules have larger impacts on the average waiting time than the other kinds of rules.

A digital twin-based decision support approach for AGV scheduling

The dynamic and complex environment affects the operational efficiency of automated guided vehicles (AGVs) at automated container terminals, in which artificial intelligent approaches are applied to address optimization problems in dynamic systems. This paper proposes a digital twin-based decision support approach to improve the efficiency of AGV scheduling service. Accordingly, the factors that affect AGV scheduling performance, including conflicts, failures, and battery constraints, are discussed first. Then, the framework and main steps of the proposed approach are described. The physical operation space is mapped into the virtual space, and both spaces keep synchronized to support the verification of solutions. A mathematical programming model and Q-learning algorithm are used to generate the AGV scheduling plan considering battery charging. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach. Comparisons of the digital twin-based approach, genetic algorithm (GA), and particle swarm optimization (PSO) are also made with different scale experiments. It appears that the proposed digital twin-based approach is superior to GA and PSO when solving small- and large-scale cases. A sensitivity analysis is performed concerning the battery utilization rate, task, and AGV number. Experimental results show that an optimal configuration of AGV and task can improve the battery utilization rate and reduce the completion time.

Joint scheduling optimization model for yard cranes and AGVs considering running routes

The escalation in global consumer demand has prompted the continual expansion of container throughput, which drives container terminals to bolster their production capacities. Globally, ports worldwide have been adopting automated equipment and embracing digital operational environments to reinforce their logistics system efficiency and curtail transportation costs. Enhancement of the efficiency of container transportation at automated terminals depends on terminal loading and unloading operations. The efficiency of these operations directly hinges on the automated mechanical equipment deployed at terminals. Consequently, optimization of resource allocation and production scheduling of loading and unloading equipment at terminals has become an essential and effective approach. In this regard, automated yard cranes, which are responsible for rear-end handling, and automated guided vehicles (AGVs), which transport containers between the yard and the terminal quay, have been proven indispensable. This paper principally delves into the scheduling optimization of mechanical equipment in automated container terminal yards, with a unique focus on the coordinated scheduling optimization of yard cranes and AGVs while also accounting for AGV path planning. This research centers on the automated container terminal yard system and the challenge in yard crane scheduling. This work also establishes a grid-based terminal network model and validates the model and algorithm using practical examples. Optimal solutions are obtained for the joint scheduling of yard cranes and AGVs, accompanied by strategies on rational AGV path selection. The findings provide valuable references and theoretical underpinnings for operational scheduling and enhancement of the efficiency in container transport in automated container terminal yards. Furthermore, these results facilitate the overall improvement of production efficiency at automated terminals and the reduction of operational costs, thus carrying theoretical and practical significance.

Integrated optimization of automated guided vehicles and yard cranes considering charging constraints

To improve the operational efficiency of automated container terminals and the coordination between multiple operations, this article studies the integrated scheduling optimization problem of automated guided vehicles (AGVs) and yard cranes. The impact of charging constraints on AGV task allocation and scheduling is considered. With the goal of minimizing the maximum completion time of all tasks, a mixed integer programming model is proposed. A solution method based on a dynamic programming algorithm is designed, where a heuristic algorithm is used to assign tasks to the yard cranes, and the dynamic programming method is used to assign tasks to the AGVs based on the task assignment results of the yard cranes. Finally, the validity of the model and algorithm is tested by numerical experiments. Furthermore, the influence of the quantity of AGVs on the terminal operational efficiency and the impact of AGV charging strategies on AGV scheduling are analysed.

AGV dispatching and bidirectional conflict-free routing problem in automated container terminal

Automated guided vehicles (AGVs) are automated devices that replace internal trucks in container terminals for horizontal container transportation. Compared to internal trucks, AGV has the special characteristic of bidirectional transportation. This characteristic makes AGV change the transport direction without turning around, significantly saving the transport space and time taken up by vehicle transport. However, few studies focus on the bidirectional transportation mode in the AGV scheduling problem to improve the overall efficiency of the automated container terminal (ACT). This study aims at the AGV dispatching and bidirectional conflict-free routing problem. A bi-level mixed integer programming model is proposed, which fully considers equipment coordination, bidirectional conflict-free routing, and import and export container tasks. The bi-level differential evolution algorithm based on the dynamic routing approach is developed to minimize the completion time of all tasks. A series of numerical experiments are conducted to validate the performance of the proposed bidirectional transportation mode and algorithm. The result shows that the efficiency of the bidirectional transportation mode of AGVs is more suitable in the actual ACT operation scenario, and the proposed algorithm can find a better solution within a reasonable amount of time.

Research on Optimization Algorithm of AGV Scheduling for Intelligent Manufacturing Company: Taking the Machining Shop as an Example

Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence; the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree; and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop.

Bi-Objective Integrated Scheduling of Quay Cranes and Automated Guided Vehicles

Operational efficiency is one of the key performance indicators of a port’s service level. In the process of making scheduling plans for container terminals, different types of equipment are usually scheduled separately. The interaction between quay cranes (QCs) and automated guided vehicles (AGVs) is neglected, which results in low operational efficiency. This research explores the integrated scheduling problem of QCs and AGVs. Firstly, a multi-objective mixed integer programming model (MOMIP) is conducted, with the aim of minimizing the makespan of vessels and the unladen time of AGVs. Then, embedded with a new heuristic method, the non-dominated sorting genetic algorithm-II (NSGA-II) is designed for the scheduling problem. The heuristic method includes two parts: a bay-based QC allocation strategy and a container-based QC-AGV scheduling strategy. Finally, in order to test the performance of the proposed algorithm, differently sized benchmark tests are performed, and the results are compared to the multi-objective particle swarm optimization algorithm (MOPSO) and the weighted-sum method. The computational results indicate that the proposed algorithm can effectively solve the multi-objective integrated scheduling problem of QCs and AGVs. For large-scale problems, the NSGA-II algorithm has better performance and more obvious advantages compared to others. The proposed method has the capability of providing a theoretical reference for the QC and AGV scheduling of container terminals.

Blocks allocation and handling equipment scheduling in automatic container terminals

The blocks allocation and integrated scheduling of quay cranes (QCs), yard cranes(YCs), and automated guided vehicles(AGVs) between the apron, side-loading blocks and end-loading blocks for multiple berthing ships is investigated, where the inbound, outbound and transship containers from several ships berthing at the same time is considered. A multi-ship blocks-sharing strategy is proposed to increase the efficiency in double cycling operations of YCs and AGVs through centrally storing containers from different ships. A two-stage mixed integer programming model is formulated in which the first stage is to optimize the problem of blocks allocation and QCs scheduling, and the second stage is to optimize the problem of YCs and AGVs scheduling. To solve the model, adaptive variable neighbourhood immune algorithm (AVNIA) and rule-based heuristic algorithm are proposed. Compared with the lower bound compiled with Gurobi, ANVIN is highly effective based on case study layout data of the automated container terminal of Yangshan port, Shanghai. Experiments demonstrate that the proposed blocks-sharing strategy can significantly improve the utilization rate of YCs and AGVs, and effectively reduce the operation cost of automatic container terminals.

Digital-Twin-Driven AGV Scheduling and Routing in Automated Container Terminals

Automated guided vehicle (AGV) scheduling and routing are critical factors affecting the operation efficiency and transportation cost of the automated container terminal (ACT). Searching for the optimal AGV scheduling and routing plan are effective and efficient ways to improve its efficiency and reduce its cost. However, uncertainties in the physical environment of ACT can make it challenging to determine the optimal scheduling and routing plan. This paper presents the digital-twin-driven AGV scheduling and routing framework, aiming to deal with uncertainties in ACT. By introducing the digital twin, uncertain factors can be detected and handled through the interaction and fusion of physical and virtual spaces. The improved artificial fish swarm algorithm Dijkstra (IAFSA-Dijkstra) is proposed for the optimal AGV scheduling and routing solution, which will be verified in the virtual space and further fed back to the real world to guide actual AGV transport. Then, a twin-data-driven conflict prediction method is proposed to predict potential conflicts by constantly comparing the differences between physical and virtual ACT. Further, a conflict resolution method based on the Yen algorithm is explored to resolve predicted conflicts and drive the evolution of the scheme. Case study examples show that the proposed method can effectively improve efficiency and reduce the cost of AGV scheduling and routing in ACT.

An effective hybrid algorithm for joint scheduling of machines and AGVs in flexible job shop

Flexible job shops motivated by small batches and multiple orders require the collaboration of machines and automated guided vehicles (AGVs) scheduling to boost shop floor flexibility and productivity. The joint scheduling of machines and AGVs can better achieve global optimization. However, joint scheduling requires two NP hard problems to be solved simultaneously. Therefore, this paper employs a multi-AGV flexible job shop scheduling problem (MA-FJSP) with an effective hybrid algorithm. First of all, a model is established with the objectives of minimizing the makespan, the total AGV running time and the total machine load. To solve the MA-FJSP, high-quality initialization methods and improved elite strategies are designed to improve global convergence in the proposed algorithm. In addition, a problem-knowledge-based neighborhood search is integrated to improve its exploitation capability. At last, a series of comparative experimental studies were performed to exam the effectiveness of the improved algorithm. The results demonstrate that the solutions gained by the proposed algorithm perform well in respect of convergence, diversity and distribution.

An Approach to Integrated Scheduling of Flexible Job-Shop Considering Conflict-Free Routing Problems.

This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises.

A Two-stage Stochastic Programming for AGV scheduling with random tasks and battery swapping in automated container terminals

Automated guided vehicle (AGV) is one of the main equipment for horizontal transportation in automated container terminals, and the optimization of AGV scheduling has become increasingly important. Existing scheduling systems tend to make decisions based on deterministic conditions, ignoring the dynamic changes and uncertainties of the terminal environment, such as the arrival of random tasks during AGV operations. In addition, the battery swapping process is neglected in most AGV scheduling studies, yet it is crucial to ensure the operation of AGVs. In this paper, we construct a two-stage stochastic programming model for the joint scheduling problem of battery swapping and task operation with random tasks. A double-threshold constraint for battery swapping decision-making is adopted. The results show that the double-threshold strategy is better for AGV utilization than the single-threshold one. Upon the solution method, a simulation-based ant colony optimization algorithm is proposed. Sample average approximation is used to calculate the expected cost, and two local search procedures are introduced to improve the quality of the solutions. In the cases of multiple instances and several random task samples with different arrival rates, our method was compared with three practical policies under a deterministic model. Computational results show that the scheduling scheme considering random tasks in advance is more robust and stable.

Real-Time Charging Scheduling of Automated Guided Vehicles in Cyber-Physical Smart Factories Using Feature-Based Reinforcement Learning

In smart factories, a variety of automated guided vehicles (AGVs) communicate with cyber-physical systems (CPSs) to autonomously deliver raw materials and workpieces among smart production facilities. In practice, instead of acquiring more costly AGVs to cause congestion in existing working space, most factories develop rule-based and model-based approaches to improve the AGV utilization rate and further the production efficiency. However, these charging strategies require predefined rules or models for estimating the internal information of batteries, so that they lead to huge computational costs and estimation errors. As a consequence, this work creates a Markov decision process problem for real-time charging scheduling of AGVs to fulfill uncertain AGV dispatching requests from the CPS for production lines, in which four bounds for charging heterogeneous AGVs are considered from practical experiences for increasing the AGV utilization rate. This work further improves a feature-based reinforcement learning approach, in which the state and action space can be effectively reduced through approximating the state-value function by five feature functions, including the estimated revenue for improving the utilization time, the total AGV charging cost, the cost of penalizing unfulfilled dispatching requests, the priority of charging newer batteries, and the priority of charging the batteries close to be fully charged, respectively. Experimental results show that the proposed algorithm obtains better benefits than the current practical approach, and improves the AGV utilization rate.

Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study

In the Flexible Manufacturing System (FMS), where material processing is carried out in the form of tasks from one department to another, the use of Automated Guided Vehicles (AGVs) is significant. The application of multiple-load AGVs can be understood to boost FMS throughput by multiple orders of magnitude. For the transportation of materials and items inside a warehouse or manufacturing plant, an AGV, a mobile robot, offers extraordinary industrial capabilities. The technique of allocating AGVs to tasks while taking into account the cost and time of operations is known as AGV scheduling. Most research has exclusively addressed single-objective optimization, whereas multi-objective scheduling of AGVs is a complex combinatorial process without a single solution, in contrast to single-objective scheduling. This paper presents the integrated Local Search Probability-based Memetic Water Cycle (LSPM-WC) algorithm using a spinning mill as a case study. The scheduling model’s goal is to maximize machine efficiency. The scheduling of the statistical tests demonstrated the applicability of the proposed model in lowering the makespan and fitness values. The mean AGV operating efficiency was higher than the other estimated models, and the LSPM-WC surpassed the different algorithms to produce the best result.

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 lay-time, 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 subse-quentAGVs. 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.