Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets

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

Background, Systematic Review, Challenges and Outlook

Publisher Copyright: © 2013 IEEE. This research is supported by the Digital Manufacturing and Design Training Network (DiManD) project funded by the European Union through the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) under grant agreement no. 814078The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.publishersversionpublishe

Towards a Reference Architecture for Swarm Intelligence-based Internet of Things

International audienceThe Internet of Things (IoT) represents the global network which interconnects digital and physical entities. It aims at providing objects with intelligence that allows them to perceive, decide and cooperate with other objects, machines, systems and even humans to enable a whole new class of applications and services. Agent-Based Computing paradigm has been exploited to deal with the IoT system development. Many research works focus on making objects able to think by themselves thus imitating human brain. Swarm Intelligence studies the collective behavior of systems composed of many individuals who interact locally with each other and with their environment using decentralized and self-organized control to achieve complex tasks. Swarm intelligence-based systems provide decentralized, self-organized and robust systems with consideration of coordination frameworks. We explore in this paper the exploitation of swarm intelligence-based features in IoT-based systems. Therefore, we present a reference swarm-based architectural model that enables cooperation among devices in IoT systems

Application of Reinforcement Learning to Multi-Agent Production Scheduling

Reinforcement learning (RL) has received attention in recent years from agent-based researchers because it can be applied to problems where autonomous agents learn to select proper actions for achieving their goals based on interactions with their environment. Each time an agent performs an action, the environment¡Šs response, as indicated by its new state, is used by the agent to reward or penalize its action. The agent¡Šs goal is to maximize the total amount of reward it receives over the long run. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored. The objective of this research is to develop a set of guidelines for applying the Q-learning algorithm to enable an individual agent to develop a decision making policy for use in agent-based production scheduling applications such as dispatching rule selection and job routing. For the dispatching rule selection problem, a single machine agent employs the Q-learning algorithm to develop a decision-making policy on selecting the appropriate dispatching rule from among three given dispatching rules. In the job routing problem, a simulated job shop system is used for examining the implementation of the Q-learning algorithm for use by job agents when making routing decisions in such an environment. Two factorial experiment designs for studying the settings used to apply Q-learning to the single machine dispatching rule selection problem and the job routing problem are carried out. This study not only investigates the main effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling

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

Recent developments and future trends of industrial agents

The agent technology provides a new way to design and engineer control solutions based on the decentralization of control over distributed structures, addressing the current requirements for modern control systems in industrial domains. This paper presents the current situation of the development and deployment of agent technology, discussing the initiatives and the current trends faced for a wider dissemination and industrial adoption, based on the work that is being carried out by the IEEE IES Technical Committee on Industrial Agents

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems