Number Of Automated Guided Vehicles Research Articles

A decentralized path planning model based on deep reinforcement learning

The escalating usage of automated guided vehicles (AGVs) on a grand scale accentuates the scalability limitations inherent in traditional, centrally-controlled path planning algorithms. Addressing this challenge, we introduce a pioneering decentralized path planning model, DPPDRL, built upon the foundations of deep reinforcement learning. Drawing inspiration from human path planning and dynamic coupling techniques, DPPDRL imbues AGVs with the autonomy to independently dictate and dynamically adapt their paths in real time. Notably, DPPDRL champions the adoption of diverse policies, thereby facilitating efficient collision avoidance and exploration of potential routes. Simulation-based evidence substantiates critical findings, including remarkable improvements in training speed and scalability, heightened adaptability to environmental variations and agent number changes, and applicability across any number of AGVs, provided a reasonable task distribution. However, DPPDRL's performance becomes limited when tasks are densely concentrated. Addressing this issue through task distribution and hierarchical reinforcement learning is a promising direction for future research. These findings underscore the transformative potential of DPPDRL in managing extensive, dynamic AGV operations.

Design of experiment and simulation approach for analyzing automated guided vehicle performance indicators in a production line

Several manufacturing industries try to reduce transportation waste using automated material handling systems, which can enhance the transportation of raw materials from one location to another in the production line of a manufacturing area. The issue with transportation and job flow is a critical factor in a production line because some production stations need to wait for the work-in-progress to be delivered. Automated guided vehicle (AGV) transportation needs a setup of traffic control over a factory’s physical infrastructure and simulation. Doing so can help showcase and evaluate possible deficiencies that can be improved in the real job flow scenario of the production line. The design of experiment plays a huge role in finding and explaining variations of information under conditions that are regularly put as a hypothesis to reflect or describe the variation. A simulation model is implemented by adopting simplified AGV parameters. The model development follows the structure of system specification → machine specification → AGV specification → discrete-event simulation model → experimental design → analysis of performance indicators (PIs). To precisely reflect an alternative for evaluating aforementioned issues, this study proposes the model stated above and an analysis that is based on the PIs. Analysis of variance (ANOVA) results are chosen to analyze different factors affecting the PIs. Using the factorial ANOVA test results, this study uses one-way and two-way interactions to compare the relationship between job flow time, AGVs, AGV utilization, number of AGVs, and average waiting time.

Design of the System for the Analysis of Disinfection in Automated Guided Vehicle Utilisation

The article’s main goal is to describe the system design for the analysis of disinfection automated guided vehicle (AGV) utilisation so that the AGV’s optimal number can be determined. The simulation was used as the system’s main tool, allowing a relatively objective approach to imitate real system behaviour. With the proposed system, it is possible to determine the utilisation of AGVs and the number of necessary AGVs that carry out disinfection of the premises through the superstructure platforms. In the simulation model, two main modes of disinfection of ground AGV were tested. A regular circuit is carried out at specific intervals as well as a dynamic evaluation of the area and its possible contamination. When the area reaches a certain threshold, the instruction to disinfect the area is triggered. Experiments were carried out for a different number of AGVs, with the possible restriction of entry in the presence of the patient, and for a combination of specialised AGVs. Based on the results, we can conclude that the use of only surface-disinfecting AGVs is limited by the movement of patients and does not bring the same results as the use of a combination of surface- and air-disinfecting specialised AGVs.

A centroid opposition-based coral reefs algorithm for solving an automated guided vehicle routing problem with a recharging constraint

Automated guided vehicles (AGVs) have been utilized extensively in material handling systems. In this paper, a synchronization problem of task scheduling and AGV routing is studied on a shop floor with reconfigurable machine tools. The objective is to minimize the maximum completion time (i.e., makespan) of all products. Studying scheduling and routing problems simultaneously, we propose a centroid opposition-based coral reefs optimization (COCRO) algorithm combined with a heuristic. The developed heuristic uses the Dijkstra algorithm to extract collision-free paths. To test the performance of the proposed algorithm, its results are compared to the results of the algorithm used in the literature recently. The results indicate the superiority of the COCRO algorithm. Finally, the performance of the system under different circumstances is analyzed. Based on the results, it can be claimed that reconfigurable machines reduce production time by up to 35%. Furthermore, the capacity of AGVs has a more significant role than the number of AGVs in the production time. Also, comparing four widely used types of batteries (i.e., sealed GEL, AGM pure-lead, Lithium, and flooded lead-acid), the result confirms the superiority of Lithium batteries.

Integrated Production and Transportation Scheduling Method in Hybrid Flow Shop

The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem (IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem (HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles (AGVs) is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation (including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.

Simulation-optimization of automated material handling systems in a healthcare facility

Automated material handling systems are used in healthcare facilities to optimize material flow, minimize workforce requirements, reduce risk of contamination, and reduce work injuries. This paper develops a case study using data from Greenville Memorial Hospital (GMH) in South Carolina, USA. The case study is focused on the delivery of surgical case carts to operating rooms at GMH via Automated Guided Vehicles (AGVs). This study proposes a framework that integrates data analysis with system simulation and optimization. The study addresses the following research questions: (1) Redesign of the pathways: Do performance measures, such as travel time and task completion time, improve after a redesign of AGV pathways? (2) Operational fleet sizing: Do performance measures, such as travel time and task completion time, improve when the number of AGVs used daily is controlled by the volume of surgical cases? If this is true, then how many AGVs should be used daily? To address research question (1), we compare two AGV pathway designs via an extensive sensitivity analysis. To address research question (2), we use a simulation-optimization model to evaluate the performance of the system for different fleet sizes. Finally, we conduct a pilot study at GMH to validate the results of our analysis. This study indicates that the proposed solution, which uses a smaller fleet of AGVs than currently used at GMH, leads to significant reductions in congestion and travel times, and increased utilization of AGVs.

Expected distances and alternative design configurations for automated guided vehicle-based order picking systems

Automated Guided Vehicle (AGV)-based order picking (OP) systems, also known as Robotic Mobile Fulfilment Systems, continues to receive attention in industry and academia since their introduction as Kiva systems. A key component of AGV-based OP systems is the ‘robots’ (or AGVs) that pick up the ‘pods’ and transport them to the appropriate pick station (PS), where a picker picks the items ordered by customers. The performance of such systems depends on the shape of the forward area (FA) and the number of AGVs, which in turn depends on the time it takes an AGV to retrieve a pod. To aid system designers, we explore alternative shapes for the FA and we derive closed-form expressions for the expected AGV travel distances under two possible order assignment rules. Under the random assignment rule, an order is assigned to any PS with equal probability. Under the closest assignment rule, the order is assigned to the closest PS. We also examine the impact of alternative PS configurations for different shapes of the FA. The results offer valuable insights concerning expected travel distances under alternative design configurations. The results would also be useful when building design and performance evaluation models for AGV-based OP systems.

Scheduling automated guided vehicles in very narrow aisle warehouses

In this paper, we study the scheduling of storage and retrieval of unit loads from very narrow aisles using automated guided vehicles (AGVs). As AGVs cannot pass each other in the aisles, sequencing the aisle access is essential. We propose two access policies, present multiple complexity results and formulate MIP models. We then present a large neighborhood search that produces solutions within less than 2.5% of the optimum solution on average in a short amount of time for instances with hundreds of jobs. We use our heuristic to derive insights into the best access policy, number of AGVs, as well as the optimal layout of very narrow aisle warehouses.

Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm.

Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Finding optimal dwell points for automated guided vehicles in general guide-path layouts

The dwell points for idle vehicles in an automated guided vehicle (AGV) system determine the response times for pick-up requests and thus affect the performance of automated manufacturing systems. In this paper, we address the problem of optimally locating dwell points for a given number of AGVs in a general guide-path layout. Based on an optimality property, we propose new mixed integer linear programming (MILP) formulations for three versions of the problem: (i) minimizing the mean response time in the system, (ii) minimizing the maximum response time in the system, and (iii) minimizing the maximum response time in the system considering time restrictions on vehicle availability. Given that the computational time required to solve the MILP models significantly increases with the size of the guide-path network and number of available AGVs, we also develop a generic genetic algorithm (GA) that can be applied to all three versions of the problem. A computational study is carried out on the single-loop layout and two special cases of two-dimensional grid networks with the objectives of minimizing mean response time and minimizing the maximum response time. The results show that the proposed GA procedure can yield optimal or near optimal solutions in reasonable time.

Flow Path Network Design for Robust AGV Systems Against Tasks Using Competitive Coevolution

An effective and robust flow path network is desired in Automated Guided Vehicle (AGV) systems. A design process to obtain the desired flow path network in AGV systems is proposed in this paper. Our proposed method can make flow path networks robust against tasks, which include pick-up point, drop-off point and throughput and number of AGVs . It is important for this robust flow path network that the kinds of tasks be of various and non-linear to the system effectiveness. The problem is solved by the design method of various kinds of tasks that are difficult for AGV systems using Genetic Algorithm (GA). An effective flow path network is designed with GA simultaneously because the difficult tasks and number of AGVs depend on the flow path networks. Competitive coevolution is applied to the simultaneous design. AGV systems can be effective with uni/bi-directional combined flow path networks which utilize just simple routings. Results of the design are shown through simulations, and the designed flow path network makes it possible to complete various tasks with various numbers of AGVs.

Improved design methodology for an existing automated transportation system with automated guided vehicles in a seaport container terminal

In this paper, we propose an improved design methodology to meet the changing demands of an existing automated container transportation system in which automated guided vehicles (AGVs) are used. This system is called an AGV transportation system. To achieve an improved design, it is essential to detect and correct any occurring bottlenecks. For this purpose, we exhaustively enumerate design proposals by constructing a logic tree. As a case study to verify the proposed methodology, we apply the methodology to an existing AGV transportation system. From the enumerated design proposals, we suggest design policies by considering the actual constraints of the transportation system. Finally, we redesign the transportation system as rapidly as possible. On the other hand, we keep system balancing into account; then, we derive a suitable demand and input number of AGVs under given specifications for a transportation system.

Automated guided vehicle system for two container yard layouts

The explosive growth in the freight volumes has put a lot of pressure on seaport authorities to find better ways of doing daily operations in order to improve the performance and to cope with avalanches of containers processing at container terminals. Advanced technologies, and in particular automated guided vehicle systems (AGVS), have been recently proposed as possible candidates for improving the terminal’s efficiency not only due to their abilities of significantly improving the performance but also to the repetitive nature of operations in container terminals. The deployment of AGVS may not be as effective as expected if the container terminal suffers from a poor layout. In this paper, simulation models are developed and used to demonstrate the impact of automation and terminal layout on terminal performance. In particular, two terminals with different but commonly used yard configurations are considered for automation using AGVS. A multi attribute decision making (MADM) method is used to assess the performance of the two terminals and determine the optimal number of deployed automated guided vehicles (AGVs) in each terminal. The simulation results demonstrate that substantial performance can be gained using AGVS. Furthermore, the yard layout has an effect on the number of AGVs used and on performance.

Non-network Type Artificial Immune System and its Application to Automated Guided Vehicle (AGV) System

This paper describes Artificial Immune-Based Control System that is applied to the automatic transportation control system of AGV (Automated Guided Vehicle) operated in a restricted area such as a container terminal and an ironworks. The authors propose the non-network type artificial immune system that is functioning in a unit level of antibodies instead of in a network of antigens and antibodies. AGVs equipped by the proposed method can avoid the collision and/or the deadlock and move autonomously from the source points to the destinations. The transportation system using AGVs has been expected because AGVs have the flexibility and efficiency. However the control system for AGVs is so flexible that the design has a big margin. Especially, the design for the control system is a hard work when the number of AGV becomes to be large. Only a few study of this problem is found in a past references. This paper focuses on the automatic transportation plan of AGVs in a real time scheduling using the non-network type artificial immune system. The comparison of the proposed method: the non-network type artificial immune system and the network type artificial immune system are also discussed and the computer simulation verifies the effectiveness of the proposed method.

A simulation study of automated guided vehicle dispatching

This paper reports a simulation study to investigate the relative performance of five automated guided vehicle (AGV) dispatching rules. The study found that distance-based rules are most effective while AGV attribute-based rules are least effective. Both AGV speed and the number of AGVs in use are factors critically affecting the performance of a flexible manufacturing system. This paper demonstrates that computer simulation is a viable tool for assisting FMS design and evaluation.