An Iterative Approach for Collision Feee Routing and Scheduling in Multirobot Stations

This work is inspired by the problem of planning sequences of operations, as welding, in car manufacturing stations where multiple industrial robots cooperate. The goal is to minimize the station cycle time, \emph{i.e.} the time it takes for the last robot to finish its cycle. This is done by dispatching the tasks among the robots, and by routing and scheduling the robots in a collision-free way, such that they perform all predefined tasks. We propose an iterative and decoupled approach in order to cope with the high complexity of the problem. First, collisions among robots are neglected, leading to a min-max Multiple Generalized Traveling Salesman Problem (MGTSP). Then, when the sets of robot loads have been obtained and fixed, we sequence and schedule their tasks, with the aim to avoid conflicts. The first problem (min-max MGTSP) is solved by an exact branch and bound method, where different lower bounds are presented by combining the solutions of a min-max set partitioning problem and of a Generalized Traveling Salesman Problem (GTSP). The second problem is approached by assuming that robots move synchronously: a novel transformation of this synchronous problem into a GTSP is presented. Eventually, in order to provide complete robot solutions, we include path planning functionalities, allowing the robots to avoid collisions with the static environment and among themselves. These steps are iterated until a satisfying solution is obtained. Experimental results are shown for both problems and for their combination. We even show the results of the iterative method, applied to an industrial test case adapted from a stud welding station in a car manufacturing line

Conflict-free dynamic route multi-AGV using dijkstra floyd-warshall hybrid algorithm with time windows

Autonomous Guided Vehicle is a mobile robot that can move autonomously on a route or lane in an indoor or outdoor environment while performing a series of tasks. Determination of the shortest route on an autonomous guided vehicle is one of the optimization problems in handling conflict-free routes that have an influence on the distribution of goods in the manufacturing industry's warehouse. Pickup and delivery processes in the distribution on AGV goods such as scheduling, shipping, and determining the route of vehicle with short mileage characteristics, is very possible to do simulations with three AGV units. There is a windows time limit on workstations that limits shipping. The problem of determining the route in this study is considered necessary as a multi-vehicle route problem with a time window. This study aims to describe the combination of algorithms written based on dynamic programming to overcome the problem of conflict-free AGV routes using time windows. The combined approach of the Dijkstra and Floyd-Warshall algorithm results in the optimization of the closest distance in overcoming conflict-free routes

Development of an Industrial Internet of Things (IIoT) based Smart Robotic Warehouse Management System

According to data of Census and Statistics Department, freight transport and storage services contributed to 90% of the employment of logistics sector in the period from 2010 to 2014. Traditional warehouse operations in Hong Kong are labor-intensive without much automation. With the rapid increasing transaction volume through multi-channel, the preference for next-day delivery service has been increasing. As a result, 3rd party logistics providers have realized the importance of operational efficiency. With the advent of Industry 4.0 emerging technologies including Autonomous Robots, Industrial Internet of Things (IIoT), Cloud Computing, etc., a smart robotic warehouse management system is proposed as it redefines the warehouse put-away and picking operations from man-to-goods to goods-to-man using autonomous mobile robots. This paper aims to develop and implement an IIoT-based smart robotic warehouse system for managing goods and autonomous robots, as well as to make use of the autonomous mobile robots to deliver the goods automatically for put-away and picking operations. The significance of the paper is to leverage the Industry 4.0 emerging technologies to implement the concept of smart warehousing for better utilization of floor space and labor force so as to improve logistics operational efficiency

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Simulation in Automated Guided Vehicle System Design

The intense global competition that manufacturing companies face today results in an increase of product variety and shorter product life cycles. One response to this threat is agile manufacturing concepts. This requires materials handling systems that are agile and capable of reconfiguration. As competition in the world marketplace becomes increasingly customer-driven, manufacturing environments must be highly reconfigurable and responsive to accommodate product and process changes, with rigid, static automation systems giving way to more flexible types. Automated Guided Vehicle Systems (AGVS) have such capabilities and AGV functionality has been developed to improve flexibility and diminish the traditional disadvantages of AGV-systems. The AGV-system design is however a multi-faceted problem with a large number of design factors of which many are correlating and interdependent. Available methods and techniques exhibit problems in supporting the whole design process. A research review of the work reported on AGVS development in combination with simulation revealed that of 39 papers only four were industrially related. Most work was on the conceptual design phase, but little has been reported on the detailed simulation of AGVS. Semi-autonomous vehicles (SA V) are an innovative concept to overcome the problems of inflexible -systems and to improve materials handling functionality. The SA V concept introduces a higher degree of autonomy in industrial AGV -systems with the man-in-the-Ioop. The introduction of autonomy in industrial applications is approached by explicitly controlling the level of autonomy at different occasions. The SA V s are easy to program and easily reconfigurable regarding navigation systems and material handling equipment. Novel approaches to materials handling like the SA V -concept place new requirements on the AGVS development and the use of simulation as a part of the process. Traditional AGV -system simulation approaches do not fully meet these requirements and the improved functionality of AGVs is not used to its full power. There is a considerflble potential in shortening the AGV -system design-cycle, and thus the manufacturing system design-cycle, and still achieve more accurate solutions well suited for MRS tasks. Recent developments in simulation tools for manufacturing have improved production engineering development and the tools are being adopted more widely in industry. For the development of AGV -systems this has not fully been exploited. Previous research has focused on the conceptual part of the design process and many simulation approaches to AGV -system design lack in validity. In this thesis a methodology is proposed for the structured development of AGV -systems using simulation. Elements of this methodology address the development of novel functionality. The objective of the first research case of this research study was to identify factors for industrial AGV -system simulation. The second research case focuses on simulation in the design of Semi-autonomous vehicles, and the third case evaluates a simulation based design framework. This research study has advanced development by offering a framework for developing testing and evaluating AGV -systems, based on concurrent development using a virtual environment. The ability to exploit unique or novel features of AGVs based on a virtual environment improves the potential of AGV-systems considerably.University of Skovde. European Commission for funding the INCO/COPERNICUS Projec

Hybrid multiobjective genetic algorithm for integrated dynamic scheduling and routing of jobs and automated guided vehicles in flexible manufacturing systems

The dynamic continues trend of adoption and improvement inventive automated technologies is one of the main competing strategies of many manufacturing industries. Effective integrated operations management of Automated Guided Vehicle (AGV) system in Flexible Manufacturing System (FMS) environment results in the overall system performance. Routing AGVs was proved to be NP-Complete and scheduling of jobs was also proved to be NP hard problems. The running time of any deterministic algorithms solving these types of problems increases very rapidly with the size of the problem, which can be many years with any computational resources available presently. Solving AGVs conflict free routing, dispatching and simultaneous scheduling of the jobs and AGVs in FMS in an integrated manner is identified as the only means of safeguarding the feasibility of the solution to each sub-problem. Genetic algorithm has recorded of huge success in solving NP-Complete optimization problems with similar nature to this problem. The objectives of this research are to develop an algorithm for integrated scheduling and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm, ensure the algorithm validity and improvement on the performance of the developed algorithm. The algorithm generates an integrated scheduling and detail paths route while optimizing makespan, AGV travel time, mean flow time and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The integrated algorithms use two genetic representations for the individual solution entire sub-chromosomes. The first three sub-chromosomes use random keys to represent jobs sequencing, operations allocation on machines and AGV dispatching, while the remaining sub-chromosomes are representing particular routing paths to be used by each dispatched AGV. The multiobjective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance history. The algorithm used weight mapping crossover (WMX) and Insertion Mutation (IM) as genetic operators for sub-chromosomes represented with priority-based representation. Parameterized uniform crossover (PUX) and migration are used as genetic operators for sub-chromosomes represented using random-key based encoding. Computational experiments were conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. First scenario uses static consideration, the second scenario uses dynamic consideration with machine failure recovery. Sensitivity analysis and convergence analysis was also conducted. The results show the effectiveness of the proposed algorithm in generating the integrated scheduling, AGVs dispatching and conflict-free routing. The comparison of the result of the developed integrated algorithm using two benchmark FMS scheduling algorithms datasets is conducted. The comparison shows the improvement of 1.1% and 16% in makespan of the first and the second benchmark production dataset respectively. The major novelty of the algorithm is an integrated approach to the individual sub-problems which ensures the legality, and feasibility of all solutions generated for various sub-problems which in the literature are considered separately

Enhancing the performance of automated guided vehicles through reliability, operation and maintenance assessment

Automated guided vehicles (AGVs), a type of unmanned moving robots that move along fixed routes or are directed by laser navigation systems, are increasingly used in modern society to improve efficiency and lower the cost of production. A fleet of AGVs operate together to form a fully automatic transport system, which is known as an AGV system. To date, their added value in efficiency improvement and cost reduction has been sufficiently explored via conducting in-depth research on route optimisation, system layout configuration, and traffic control. However, their safe application has not received sufficient attention although the failure of AGVs may significantly impact the operation and efficiency of the entire system. This issue becomes more markable today particularly in the light of the fact that the size of AGV systems is becoming much larger and their operating environment is becoming more complex than ever before. This motivates the research into AGV reliability, availability and maintenance issues in this thesis, which aims to answer the following four fundamental questions: (1) How could AGVs fail? (2) How is the reliability of individual AGVs in the system assessed? (3) How does a failed AGV affect the operation of the other AGVs and the performance of the whole system? (4) How can an optimal maintenance strategy for AGV systems be achieved? In order to answer these questions, the method for identifying the critical subsystems and actions of AGVs is studied first in this thesis. Then based on the research results, mathematical models are developed in Python to simulate AGV systems and assess their performance in different scenarios. In the research of this thesis, Failure Mode, Effects and Criticality Analysis (FMECA) was adopted first to analyse the failure modes and effects of individual AGV subsystems. The interactions of these subsystems were studied via performing Fault Tree Analysis (FTA). Then, a mathematical model was developed to simulate the operation of a single AGV with the aid of Petri Nets (PNs). Since most existing AGV systems in modern industries and warehouses consist of multiple AGVs that operate synchronously to perform specific tasks, it is necessary to investigate the interactions between different AGVs in the same system. To facilitate the research of multi-AGV systems, the model of a three-AGV system with unidirectional paths was considered. In the model, an advanced concept PN, namely Coloured Petri Net (CPN), was creatively used to describe the movements of the AGVs. Attributing to the application of CPN, not only the movements of the AGVs but also the various operation and maintenance activities of the AGV systems (for example, item delivery, corrective maintenance, periodic maintenance, etc.) can be readily simulated. Such a unique technique provides us with an effective tool to investigate larger-scale AGV systems. To investigate the reliability, efficiency and maintenance of dynamic AGV systems which consist of multiple single-load and multi-load AGVs traveling along different bidirectional routes in different missions, an AGV system consisting of 9 stations was simulated using the CPN methods. Moreover, the automatic recycling of failed AGVs is studied as well in order to further reduce human participation in the operation of AGV systems. Finally, the simulation results were used to optimise the design, operation and maintenance of multi-AGV systems with the consideration of the throughputs and corresponding costs of them.The research reported in this thesis contributes to the design, reliability, operation, and maintenance of large-scale AGV systems in the modern and rapidly changing world.</div

Contributions to the deadlock problem in multithreaded software applications observed as Resource Allocation Systems

Desde el punto de vista de la competencia por recursos compartidos sucesivamente reutilizables, se dice que un sistema concurrente compuesto por procesos secuenciales está en situación de bloqueo si existe en él un conjunto de procesos que están indefinidamente esperando la liberación de ciertos recursos retenidos por miembros del mismo conjunto de procesos. En sistemas razonablemente complejos o distribuidos, establecer una política de asignación de recursos que sea libre de bloqueos puede ser un problema muy difícil de resolver de forma eficiente. En este sentido, los modelos formales, y particularmente las redes de Petri, se han ido afianzando como herramientas fructíferas que permiten abstraer el problema de asignación de recursos en este tipo de sistemas, con el fin de abordarlo analíticamente y proveer métodos eficientes para la correcta construcción o corrección de estos sistemas. En particular, la teoría estructural de redes de Petri se postula como un potente aliado para lidiar con el problema de la explosión de estados inherente a aquéllos. En este fértil contexto han florecido una serie de trabajos que defienden una propuesta metodológica de diseño orientada al estudio estructural y la correspondiente corrección física del problema de asignación de recursos en familias de sistemas muy significativas en determinados contextos de aplicación, como el de los Sistemas de Fabricación Flexible. Las clases de modelos de redes de Petri resultantes asumen ciertas restricciones, con significado físico en el contexto de aplicación para el que están destinadas, que alivian en buena medida la complejidad del problema. En la presente tesis, se intenta acercar ese tipo de aproximación metodológica al diseño de aplicaciones software multihilo libres de bloqueos. A tal efecto, se pone de manifiesto cómo aquellas restricciones procedentes del mundo de los Sistemas de Fabricación Flexible se muestran demasiado severas para aprehender la versatilidad inherente a los sistemas software en lo que respecta a la interacción de los procesos con los recursos compartidos. En particular, se han de resaltar dos necesidades de modelado fundamentales que obstaculizan la mera adopción de antiguas aproximaciones surgidas bajo el prisma de otros dominios: (1) la necesidad de soportar el anidamiento de bucles no desplegables en el interior de los procesos, y (2) la posible compartición de recursos no disponibles en el arranque del sistema pero que son creados o declarados por un proceso en ejecución. A resultas, se identifica una serie de requerimientos básicos para la definición de un tipo de modelos orientado al estudio de sistemas software multihilo y se presenta una clase de redes de Petri, llamada PC2R, que cumple dicha lista de requerimientos, manteniéndose a su vez respetuosa con la filosofía de diseño de anteriores subclases enfocadas a otros contextos de aplicación. Junto con la revisión e integración de anteriores resultados en el nuevo marco conceptual, se aborda el estudio de propiedades inherentes a los sistemas resultantes y su relación profunda con otros tipos de modelos, la confección de resultados y algoritmos eficientes para el análisis estructural de vivacidad en la nueva clase, así como la revisión y propuesta de métodos de resolución de los problemas de bloqueo adaptadas a las particularidades físicas del dominio de aplicación. Asimismo, se estudia la complejidad computacional de ciertas vertientes relacionadas con el problema de asignación de recursos en el nuevo contexto, así como la traslación de los resultados anteriormente mencionados sobre el dominio de la ingeniería de software multihilo, donde la nueva clase de redes permite afrontar problemas inabordables considerando el marco teórico y las herramientas suministradas para subclases anteriormente explotadas