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An important issue in the design of a flexible manufacturing system (FMS) is the operational control of the materials handling subsystem, through which reparts are conveyed from one work station to the next. A popular type of materials handling device is the automatic guided vehicle (AGV), which has been widely adopted in FMS because of its versatility and speed. This paper presents a simulation study to investigate the effects of different AGV dispatching rules on the performance of an FMS under various operating conditions. The dispatching rules studied are: FAFS — select the first available AGV; MIT — select the AGV with the most idle time; STT — select the AGV with the shortest travel time; and LTT — select the AGV with the longest travel time. The performance measures adopted are: overall production rate of the system; mean work station utilisation; and mean AGV utilisation. Although the results obtained are related to the specific FMS layout studied in this paper, it is believed that the inferences drawn from them are applicable to any FMS with similar layout structure and can provide useful guidelines for AGV design.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

This study addresses the production optimization of a cellular manufacturing system (CMS) in magnetic production enterprises. Magnetic products and raw materials are more critical to transport than general products because the attraction or repulsion between magnetic poles can easily cause traffic jams. This study needs to address a method to promote the scheduling efficiency of the problem.

To address this problem, this study formulated a mixed-integer linear programming (MILP) model to describe the problem and proposed an auction and negotiation-based approach with a local search to solve it. Auction- and negotiation-based approaches can obtain feasible and high-quality solutions. A local search operator was proposed to optimize the feasible solutions using an improved conjunctive graph model.

Verification tests were performed on a series of numerical examples. The results demonstrated that the proposed auction and negotiation-based approach with a local search operator is better than existing solution methods for the problem identified. Statistical analysis of the experiment results using the Statistical Package for the Social Sciences (SPSS) software demonstrated that the proposed approach is efficient, stable and suitable for solving large-scale numerical instances.

An improved auction and negotiation-based approach was proposed; The conjunctive graph model was also improved to describe the problem of CMS with traffic jam constraint and build the local search operator; The authors’ proposed approach can get better solution than the existing algorithms by testing benchmark instances and real-world instances from enterprises.

Magnet spot is the primary method to develop the automated guided vehicle (AGV) guidance system for many automated container terminals (ACT). Aiming to improve the high flexibility of AGV operation in ACT, this paper aims to address the problem of technical stability leading to ACT production paralysis and propose a mini-terminal AGV robot for testing laser simultaneous location and mapping (SLAM)-based methods in ACT operation scenarios.

This study developed a physical simulation robot for terminal AGV operations, providing a platform to test technical solutions for applying laser navigation-related technologies in ACTs. Then, the terminal-AGV navigation system framework is designed to apply the laser-SLAM-based method in the physical simulation robot. Finally, the experiment is conducted in the terminal operation scenario to verify the feasibility of the proposed framework for lased-SLAM-based method testing and analyze the performance of the different mini-terminal AGV robots.

A series of experiments are conducted to analyze the performance of the proposed mini-terminal AGV robot for laser-SLAM-based method testing. The experimental results show the validity and effectiveness of the AGV robot and AGV navigation system framework with better local map matching, loopback and absolute positional error.

The proposed mini-terminal AGV robot and AGV navigation system framework can provide a platform for innovative laser-SLAM-based method testing in ACTs applications. Therefore, this study can effectively meet the high requirements of ACT for maturity and stability of the laser navigation technical.

In a kitting supply system, the occurrence of material-handling errors is unavoidable and will cause serious production losses to an assembly line. To minimize production losses, this paper aims to present a dynamic scheduling problem of automotive assembly line considering material-handling mistakes by integrating abnormal disturbance into the material distribution problem of mixed-model assembly lines (MMALs).

A multi-phase dynamic scheduling (MPDS) algorithm is proposed based on the characteristics and properties of the dynamic scheduling problem. In the first phase, the static material distribution scheduling problem is decomposed into three optimization sub-problems, and the dynamic programming algorithm is used to jointly optimize the sub-problems to obtain the optimal initial scheduling plan. In the second phase, a two-stage rescheduling algorithm incorporating removing rules and adding rules was designed according to the status update mechanism of material demand and multi-load AGVs.

Through comparative experiments with the periodic distribution strategy (PD) and the direct insertion method (DI), the superiority of the proposed dynamic scheduling strategy and algorithm is verified.

To the best of the authors’ knowledge, this study is the first to consider the impact of material-handling errors on the material distribution scheduling problem when using a kitting strategy. By designing an MPDS algorithm, this paper aims to maximize the absorption of the disturbance caused by material-handling errors and reduce the production losses of the assembly line as well as the total cost of the material transportation.

Process mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.

Standard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.

Results show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.

This paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

This study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.

This study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.

By conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.

This paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

This paper aims to examine the potentials of using automated guided vehicle (AGV) technology in modular integrated construction (MiC) to realise logistics automation in module manufacturing and transport.

This paper adopts a scenario approach through three phases (i.e. scenario preparation, development and transfer), with six steps performed iteratively. The scenarios were systematically developed using a six-aspect socio-technical framework. Data were collected through a comprehensive literature review, site visits and interviews with relevant stakeholders and professionals. Implications regarding strength, weakness, opportunities and challenges and future research directions are provided.

The developed scenarios of “smart manufacturing” and “last-mile delivery” demonstrated how AGVs could be used to enhance efficiency and productivity in module manufacturing and transport. The synergies between AGVs and emerging information technologies should pave a good foundation for realising logistics automation in MiC. Future research should address: how to define the tasks of AGVs, how will the use of AGVs impact MiC practices, how to design AGV-integrated module manufacturing/transport systems and how to integrate people factors into the use of AGVs in MiC.

This paper reveals the socio-technical benefits and challenges of using AGVs in MiC.

This study extends the understanding of using logistics automation in MiC as emerging research directions, with the intention of directing scholars’ and practitioners’ interest into future exploration. It is the first attempt in its kind. Its findings could be extended to constitute a comprehensive development roadmap and prospects of automation in modular construction.

This paper aims to highlight the complex nature of automated guided vehicle (AGV) simulation model building, and especially how system modelling details affect the end results. This is an important issue in all of the transportation simulation systems, since they are service‐based by their nature, and additional inefficiencies create unanticipated performance downgrading.

This paper uses a simulation approach, and simulated systems are based on a real‐life case study and on well accepted hypothetical simulation example.

Simulation system boundaries are often neglected in the model building, and especially interface to inbound (and possibly outbound) material flow should be considered carefully; based on these research results, AGV investments are seen in an entirely different light, as system boundary is enlarged to contain more realistically interacting elements. Similar system boundary issues were found from the case study: interface with overhead gantry did not provide near optimal performance. The case study also revealed that high speed of AGVs is not necessarily worth additional investment; constraints exist in safety, acceleration and ability to turn in corners.

The findings are based on the simulation work and, to see the real implications, real‐life implementations on policy level are needed.

Results of this research provide more insights for manufacturing unit investments, and especially in the scope of automated transportation system use. Also changes in manufacturing flow management issues, after investing in, for example, AGV systems, are different from in less‐automated manufacturing units.

This research work provides more insights to simulation research work, especially from the perspective of transportation systems. Also implications arising from case study are unique as being compared to previous research in the field.

The purpose of this paper is to present the development of an architecture for real‐time routing of automated guided vehicles (AGV) in a random flexible manufacturing system (FMS).

AGV routing problem is modeled using an evolutionary algorithm‐based intelligent path planning model, which handles vehicle assignments to material handling requests and makes routing decisions with the objective of maximizing the system throughput. The architecture is implemented on a 3‐layer software environment in order to evaluate the effectiveness of the proposed model.

The proposed architecture, along with the evolutionary algorithm‐based routing model, is implemented in a simulated FMS environment using hypothetical production data. In order to benchmark the performance of the path planning algorithm, the same FMS model is run by traditional dispatching rules. The analysis shows that the proposed routing model outperforms the traditional dispatching rules for real‐time routing of AGVs in many cases.

Future work includes expanding the scope of the current work by developing and implementing other routing models and benchmarking them against the proposed model on different performance measures.

The implementation of evolutionary algorithms in real‐time routing of AGVs is unique. In addition, due to its modularity, the proposed 3‐layer architecture can allow effective and efficient integration of different real‐time routing algorithms; therefore it can be used as a benchmarking platform.