Integrated scheduling of part, tool and automated guided vehicles in a flexible manufacturing system using modified genetic algorithm

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.

This paper addresses machines, automated guided vehicles (AGVs), tool transporter (TT) and tools simultaneous scheduling in a multi-machine flexible manufacturing system (FMS) to minimize makespan (MSN). The transfer times of tools and jobs are considered. Only one copy of each type of tool is made available due to economic restrictions. The tools are stored in a central tool magazine (CTM) that shares with and serves for several machines. Jobs and tools between machines are transferred by AGVs and TT, respectively. This simultaneous scheduling problem is highly complex in nature as it involves job-operations sequencing on machines, assigning AGVs and tools to job-operations and associated trip operations including the times of empty trip and loaded trip of both AGVs and TT. This paper presents nonlinear-mixed integer programming (MIP) formulation to model and symbiotic organisms search (SOS) algorithm based on the symbiotic interaction strategies that organisms use to survive in the ecosystem to solve this simultaneous scheduling problem. The results have been tabulated and analyzed. An industrial problem in a manufacturing company is used for verification and the result obtained by SOS algorithm is compared with other algorithm.

Material handling in manufacturing systems is becoming easier as the automated machine technology is improved. Nowadays, most of the research aims at increasing the flexibility and improving the performance of the automatic guided vehicle (AGV). In this paper, designed and made AGV in the Industrial Control Laboratory in Royce Lab at the University of Manchester Institute of Science and Technology will be presented. For controlling the navigation of the AGV, a newly developed controller integrated fuzzy logic with programmable logic controller will be used. By using integrated fuzzy logic controller with programmable logic controller (IFLPLC), the flexibility of AGV will be increased and we achieved great advantages, which can be used in future. Since this AGV uses programmable logic controller and fuzzy logic controllers together, then it will be useful for factories which implement flexible manufacturing system (FMS). Online maintenance and sending the commands to other machines from AGV and so on are the advantages that can be used in FMS. The aim of this paper is to propose the practical example of IFLPLC. The designed AGV is able to reach the target. Further researches are required for collision avoidance and selecting the best way in FMS

Modern automated manufacturing processes employ Automated Guided Vehicle (AGV) for material handling, which serve several machine centres (MC) in a factory. Waiting time for resources such as AGVs are the longest elements that make up the Manufacturing Lead Time (MLT). Hence, optimal scheduling of AGVs can significantly help to increase the efficiency of the manufacturing process by minimizing the idle time of MCs waiting for the raw materials. In this paper, we will analyse the requirements for an optimal schedule and then provide a mathematical framework for an efficient schedule of material delivery by an AGV. The optimal schedule depends on several factors, such as the processing speeds of MCs, the speed and the material carrying capacity of the AGV, and system dependent overheads (such as loading and unloading time, machine set-up time, distance travelled, etc). A mathematical model is developed and then a strategy for optimal material distribution of the available raw material to the MCs is derived. With this model, the optimal number of MCs to be utilized will also be determined. Finally, the material delivery schedule employing multiple journeys to the MCs by the AGV will be carried out. Through rigorous analysis and simulation experiments, we shall show that such a delivery strategy will optimize the overall performance.

In today’s intelligent warehouses, automated guided vehicles (AGVs) are widely used, and their scheduling efficiency is crucial to the overall performance of warehouse business. However, AGV scheduling is a complex problem, especially when there are a large number of tasks to be undertaken by multiple AGVs in a large warehouse. In this paper, we present a problem of scheduling multiple AGVs for order picking in intelligent warehouse, the aim of which is to minimize the latest completion time of all orders. After testing a variety of algorithms, we propose a hybrid water wave optimization (WWO) and tabu search (TS) algorithm for efficiently solving the problem. We test the algorithm on a set of problem instances with different sizes, and the results show that the proposed algorithm exhibits significant performance advantages over a number of popular intelligent optimization algorithms.KeywordsAutomated Guided Vehicle (AGV)Intelligent schedulingWater Wave Optimization (WWO)Tabu Search (TS)Routing

Flexible Manufacturing Systems (FMS) based on Automated Guided Vehicles (AGV) have emerged as highly competitive manufacturing technologies of the last decade. However, their design and control are complex tasks since the decision problems related to the different system parameters such as sizing and scheduling are usually shown as NP-hard. Nowadays, computer simulation is one of the most commonly used methods for solving these problems. Now, we present a complete simulation tool which allows the design, analysis and control of FMS based on AGV. This tool is composed of three modules: (1) A simulation module which allows the estimation of the user introduced configuration. (2) An optimization module which is based on a meta-heuristic: the simulated annealing. This module is coupled with the simulation one in order to obtain an Executive Information System that is able to generate an optimal configuration. (3) A reactive control system which is based on the concept of real-time simulation and allows dynamic dispatching and routing of different systems inside the global FMS. The different developed modules have been validated by using a typical automated manufacturing system.

Pick up‐dispatching problem together with delivery‐dispatching problem of a multiple‐load automated guided vehicle (AGV) system have been studied. By mixing different pick up‐dispatching rules, several control strategies (alternatives) have been generated and the best control strategy has been determined considering some important criteria such as System Throughput (ST), Mean Flow Time of Parts (MFTP), Mean Tardiness of Parts (MFTP), AGV Idle Time (AGVIT), AGV Travel Full (AGVTF), AGV Travel Empty (AGVTE), AGV Load Time (AGVLT), AGV Unload Time (AGVUT), Mean Queue Length (MQL) and Mean Queue Waiting (MQW). For ranking the control strategies, a new framework based on MADM methods including fuzzy MADM and TOPSIS method were developed. Then several simulation experiments which had been based on a flow path layout to find the results were conducted. Finally, by using TOPSIS method, the control strategies were ranked. Furthermore, a similar approach was used for determining the optimal fleet size. The main contribution of this paper is developing a new approach combining the top managers′ views in selecting the best control strategy for AGV systems while trying to optimize the fleet size at the mean time by combining MADM, MCDM and simulation methods.

Automated guided vehicles (AGVs) have long been identified as a potential driver to improve system efficiency and lower labour costs in material handling systems. Accordingly, the reliability and availability of AGV systems is crucial to assure the stability and efficiency of these systems. However, the reliability issues and maintenance strategies of AGVs have not previously been studied sufficiently. This is even more marked in the case of multi-AGV systems that consist of fleets of AGVs. To fill this knowledge gap, research is conducted considering a multi-AGV system, consisting of three AGVs, in order to develop a scientific methodology for optimising the layout design, operation and maintenance of a multi-AGV system. Once an AGV is failed, it will be towed to the maintenance site for repair by a recycle vehicle to prevent deadlock and conflict. The efficiency of the recycling process of failed AGVs in a multi-AGV system, with respect to the change of location of the maintenance site, is analysed by the approach of coloured Petri nets (CPNs). A CPN model simulating the corrective and periodic preventive maintenance processes of failed AGVs is also developed in order to investigate the impact of different AGV maintenance strategies on the operation efficiency of the multi-AGV system. The simulation results obtained clearly show that the location of maintenance sites and maintenance strategies do have significant influence on the performance of a multi-AGV system, where corrective maintenance is an effective measure to maintain the long-term reliability and stability of the system.

Automated guided vehicles (AGVs) in the U-shaped automated container terminal travel longer and more complex paths. The conflicts among AGVs are trickier. The scheduling strategy of the traditional automated container terminal is difficult to be applied to the U-shaped automated container terminal. In order to minimize the handling time of all tasks and avoid AGV conflicts simultaneously in the U-shaped automated container terminal, this paper establishes a hybrid programming model for conflict-free integrated scheduling of quay cranes, AGVs, and double-cantilever rail cranes in the unloading process. It consists of a discrete event dynamic model and a continuous time dynamic model. An improved genetic seagull optimization algorithm (GSOA) is designed. A series of numerical experiments are conducted to verify the effectiveness and the efficiency of the model and the algorithm. The results show that the proposed method can simultaneously realize the AGVs collision avoidance and multi-equipment integrated scheduling optimization in the U-shaped automated container terminal.

Agent technology has been considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. The conflict resolution of automated guided vehicles (AGVs) in a flexible manufacturing systems (FMS) environment is one such problem that comes under this category. This paper describe a multi-agent approach to the operational control of AGVs by integration of path generation, enumerating time-windows, searching interruptions, adjusting waiting time and taking decisions on the selection of routes. It presents an efficient algorithm and rules for finding a conflict-free shortest-time path for AGVs, which is applicable to a bi- and unidirectional flow path network. The concept of loop formation in a flow path network is introduced to deal with the parking of idle vehicles, without obstructing the path of moveable AGVs. The concept of loop formation at nodes reduces the timing-taking task of finding the dynamic positioning of idle AGVs in the network.

Containers have been used in past decades increasingly as one of the most important transportation tools. Containers have revolutionized cargo shipping and thus changed the world trade systematically. Container terminals as the transhipment facility play a valuable role in performance of this transportation system. Improvement of this facility has been widely considered in literatures. Automated container terminals (ACTs) have been introduced to pursue this purpose. In ACTs various transport vehicles are automated and integrated to each other. Automated guided vehicles (AGVs) are used in ACTs to handle containers between quay cranes and storage yards. Usually scheduling of the AGVs is known as the key factor to improve the performance of ACTs. This paper proposed a heuristic algorithm to schedule the AGVs concurrently with quay cranes. A genetic algorithm is proposed to optimize the simultaneous scheduling of AGVs and QCs. The results showed that proposed genetic algorithm can be used in practical implications while its running time is reasonably low.

In Flexible Manufacturing Systems (FMS), automated guided vehicles (AGVs) are used for transportation of the processed materials between various pickup and delivery points. The assignment of an AGV to a workcentre from a set of workcentres simultaneously requesting the service for transport of a part is often solved in real-time with simple dispatching rules. This paper proposes an intelligent dispatching approach for the AGVs based on multi-criteria fuzzy logic controller, which simultaneously takes into account multiple aspects in every dispatching decision. The controller operates in two stages in which the second stage is constructed as a conflict resolving tool between two equally ranked AGVs for a particular workcentre. The control system is being implemented using MATLAB and its Fuzzy Inference Engine. Sample runs have been provided to illustrate the controller implementation.

This paper attempts to model the Automated Guided Vehicle (AGV) system of a flexible manufacturing system (FMS) by using timed petri net and Activity Cycle Diagrams (ACDs) as basic tools. A timed petri net used for planning of AGV system in FMSs ensures conflict-free and deadlock free operations of the system. To understand the system as it is observed in practice for delineating the behaviour of entities, the ACDhas been used. Mean utilization and the utilization index have been taken as performance measures to evaluate the system and they have been compared with previous work.

The application of Analytic Process of Resource in an AGV scheduling

With the recent progress in flexible manufacturing systems (FMS) in industry, increasing attention has been given to Automatic Guided Vehicle (AGV) systems. An AGV is a self-powered unit for transporting materials between stations without needing to be controlled by an operator. Such a system has several sensors to recognize the external state, and it is designed to travel between stations without external assistance. To manage each device quickly and independently it requires a distributed controller with a main computer as the host, as well as a number of micro-controllers. In this study, an AGV system with dual motor drive was constructed. A Pentium 4 personal computer was set up as the main host for the distributed control, and this communicated with other micro-controllers in the management of the motor. The speed of each motor was also controlled by a micro-controller.