Holonic Manufacturing Systems Research Articles

Smart industrial products’ role in industry 4.0: from a strategic paradigm of manufacturing management towards new holonic manufacturing system

The advancement of technology and the advent of the fourth industrial revolution have significantly influenced manufacturers’ strategies when it comes to production. In parallel with practical development, terminology may need to be changed over time as new information/technology or when understanding a particular concept evolves. This allows the language to reflect the current understanding and knowledge and ensures that communication remains accurate and precise. This study uses a systematic literature review together with theoretical-conceptual research to compare Industry 4.0 and Holonic Manufacturing System features. The results presented a theoretical-deduction framework illustrating where Industry 4.0 becomes New Holonic Manufacturing System. This work contributes to theory, motivating researchers of both fields to move efforts to jointly achieve New HMS. In practice, the proposed framework gives managers a deep understanding of the pathway organisations should follow to achieve a New Holonic Manufacturing System. Finally, it was possible to conclude that in the limit of full adoption, industry 4.0 becomes a New Holonic Manufacturing System.

Facilitate The Process of A Holonic Manufacturing System Based on The Whale Optimization Algorithm with Random Forest

Facilitate The Process of A Holonic Manufacturing System Based on The Whale Optimization Algorithm with Random Forest

Facilitate The Process of A Holonic Manufacturing System Based on The Whale Optimization Algorithm with Random Forest

Facilitate The Process of A Holonic Manufacturing System Based on The Whale Optimization Algorithm with Random Forest

The Role of the Holonic Manufacturing System in Achieving Product Quality: an Exploratory Study in the General Company for the Automotive Industry of Alexandria

Purpose: The aim of this study is deals with the methods that preserve the natural products and offer them at a cost while maintaining the quality of the products, which leads to the company’s production of profits. Theoretical framework: This aspect has been prepared by referring to sources and references specialized in production management and total quality management, such as books, theses, theses, research and studies, including what is available in the library and others from the international information network, the Internet. Methodology: The project was implemented in the implementation of a set of computer programs: (SPSS V25) / (Amos V25) / (Microsoft Excel 2010). The systematic literature review was built from the content analysis of papers from the Web of Science and Scopus database. The papers were analyzed from descriptive. Findings: The results of the research bear the thinking of the departments about the most important element of production, represented by the labor force, as the success of the administrative teams Which represents the basic idea of ​​the component (Holon employees). Research, Practical & Social implications: The study is the departments must adopt scientific and systematic mechanisms and methods in consolidating the culture of modern systems and adopting them according to what is scientific, even in preliminary and steps simplified. Originality/value: A model was proposed to clarify the relationship between holographic manufacturing requirements and product quality through the use of statistical methods such as the Pearson correlation coefficient and the regression coefficient to clarify the correlation and effect relationship between these two variables.

Graphic Model for Shop Floor Simulation and Control in the Context of Industry 5.0

Industry 5.0 changes the paradigm of the current production model, with repercussions throughout the value chain, and opens up opportunities for new approaches that include reducing waste to optimize the use of the planet’s resources. This paper proposes a functional and executable model that implements a Holonic Manufacturing System (HMS) architecture inspired by the I5.0 guidelines. This architecture presents the factory floor as a service provider for the product to be built, intending to make the manufacturing process adaptable to changes. The model uses Reference nets as the modeling language, a high-level class of Petri nets, Java programming language as the annotation language, and free tool support. The model can be used to perform software-level simulations and can also be interconnected to existing physical devices using Internet of things technologies, enabling interactions between Cyber–Physical Systems (CPSs). It thus allows for the control of the shop floor and the reuse of the current machine park to make its adoption more sustainable. The model was used to generate several simulation results, which are presented and analyzed, thus demonstrating the model’s usefulness.

Enabling a Multi-Agent System for Resilient Production Flow in Modular Production Systems

Volatile markets with strongly fluctuating customer demand and uncertainties in the global supply chain challenge manufacturing companies with increasing complexity. A flexible, reconfigurable production system like the concept of Modular Production by Kern is one way to meet these challenges and at the same time to produce more efficiently. Such plants can quickly adapt to new production demands, but also come with challenging requirements on the planning and control of production programs. To fulfil these requirements, new information structures are needed. Previous approaches, as traditionally prevalent in the automotive industry, are structured rigidly hierarchical leading to monolithic systems. These are designed for static production cycles and therefore offer only limited flexibility. To reach a higher flexibility, new information structures are needed which enable centralized as well as decentralized decision making. One approach to meet these requirements are Multi-Agent Systems (MAS). In this paper a design approach for deploying a semi-heterarchical system structure is presented that puts special emphasis on a resilient production planning and execution with the help of Asset Administration Shells (AAS). The potential of AAS for the self-description of Industrie 4.0 Components and agents in Holonic Manufacturing Systems as well as their usefulness as interoperable interfaces are described and discussed in this work. This is elaborated and discussed in a simulated production environment using an example from the automotive industry.

A Holonic Control System Approach for Line-less Mobile Assembly System Operations

The Line-Less Mobile Assembly Systems (LMAS) is a paradigm to maximize responsiveness to market trends by adaptive factory configurations utilizing mobile assembly resources. Such responsive systems are characterized as Holonic Manufacturing Systems (HMS), whose Holonic Control Architectures (HCA) are portrayed as Industry 4.0-enabling approaches. Accordingly, we execute the ANEMONA methodology building the LMAS HCA. The analysis phase results in a use case specification. The design phase results in the architecture concept. The implementation phase results in a ontology, re-using elements from widespread manufacturing ontologies, and in the LMAS HCA. Validation is done by the execution of the desired use case workflow.

An architecture to facilitate the integration of human workers in Industry 4.0 environments

While Industry 4.0 research has predominantly focussed on the connection of machines and digital systems and solutions, it is important to consider the role of human workers within the envisaged Industry 4.0 environments. This paper presents the concept of a digital administration shell to support the integration of humans in Industry 4.0 environments and an architecture for the development of such an administration shell. The Biography-Attributes-Schedule-Execution (BASE) architecture builds on holonic systems principles and is guided by the requirements and responsibilities of the Resource Holon, as developed in the Holonic Manufacturing Systems paradigm. The functionality and value of the BASE architecture administration shell is demonstrated by means of a case study implementation.

Perspective on holonic manufacturing systems: PROSA becomes ARTI

Looking back at 30 years of research into holonic manufacturing systems, these explorations made a lasting scientific contribution to the overall architecture of intelligent manufacturing systems. Most notably, holonic architectures are defined in terms of their world-of-interest (Van Brussel et al., 1998). They do not have an information layer, a communication layer, etc. Instead, they have components that relate to real-world assets (e.g. machine tools) and activities (e.g. assembly). And, they mirror and track the structure of their world-of-interest, which allows them to scale and adapt accordingly.This research has wandered around, at times learning from its mistakes, and progressively carved out an invariant structure while it translated and applied scientific insights from complex-adaptive systems theory (e.g. autocatalytic sets) and from bounded rationality (e.g. holons). This paper presents and discusses the outcome of these research efforts.At the top level, the holonic structure distinguishes intelligent beings (or digital twins) from intelligent agents. These digital twins inherit the consistency from reality, which they mirror. They are intelligent beings when they reflect what exists in the world without imposing artificial limitations in this reality. Consequently, a conflict with a digital twin is a conflict with reality.In contrast, intelligent agents typically transform NP-hard challenges into computations with low-polynomial complexity. Unavoidably, this involves arbitrariness (e.g. don’t care choices). Likewise, relying on case-specific properties, to ensure an outcome in polynomial time, usually renders the validity of an agent’s choices both short-lived and situation-dependent. Here, intelligent agents create conflicts by imposing limitations of their own making in their world-of-interest.Real-world smart systems are aggregates comprising both intelligent beings and intelligent agents. They are performers. Inside these performers, digital twins may constitute the foundations, supporting walls, support beams and pillars because these intelligent beings are protected by their real-world counterpart. Further refining the top-level of this architecture, a holonic structure enables these digital twins to shadow their real-world counterpart whenever it changes, adapts and evolves.In contrast, the artificial limitations, imposed by the intelligent agents, cannot be allowed to build up inertia, which would hamper the undoing of arbitrary or case-specific limitations. To this end, performers explicitly manage the rights over their assets. Revoking such rights from a limitation-imposing agent will free the assets. This will be at the cost of reduced services from the agent. When other service providers rely on this agent, their services may be affected as well; that’s how the inertia builds up and how harmful legacy is created. Thus, the services of digital twins are to be preferred over the services of an intelligent agent by developers of holonic manufacturing systems.Finally, digital twins corresponding to the decision making in the world-of-interest (a non-physical asset) allow to mirror the world-of-interest in a predictive mode (in addition to track and trace). It allows to generate short-term forecasts while preserving the benefits of intelligent beings. These twins are the intentions of the decision-making intelligent agents. Evidently, when intentions change, the forecasts needs to be regenerated (i.e. tracking the corresponding reality by the twin). This advanced feature can be deployed in a number of configurations (cf. annex).

A practical approach for multiagent manufacturing system based on agent computing nodes

With the increasing requirement for personalized customization service, discrete manufacturing workshop, as the parts processing unit in manufacturing system, is expected for more agile and fast adaptation to environment changes, dynamically handling production tasks according to resource conditions. Simultaneously, distributed artificial intelligence system (e.g. multiagent manufacturing system and the holonic manufacturing system) has been considered as an important approach for developing industrial applications to solve the problems of complexity, uncertainty, and dynamic in the modern manufacturing environment. But the lack of universality and the difficulty in deployment have restricted the use of distributed artificial intelligence in actual industrial sites. For this issue, a new concept of agent computing node is proposed in this paper to enable the realization of multiagent manufacturing system. Adaptation layer, information development layer, and intelligent analysis layer are investigated for standardizing the configuration mode of agent computing node. Cooperating agent computing node with the radio frequency identification-based dynamic recognition technology for workpiece machining process is presented in this paper, and a practical approach for multiagent manufacturing system is considered, which can apply the functions regarding to deployment of dynamic scheduling and plug-and-play. A laboratory discrete manufacturing workshop system is used as a case study to prove the feasibility of this approach. In addition, a verification in industry is carried out, and the result proves the universality of this approach.

A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

Holonic Manufacturing System: the Holonic Revolution

This work shows how to develop a methodology to support and integrate the concepts and projects of the Holonic Manufacturing System (HMS) with the other areas of the organization for full organizational management success, being a new entrepreneurial management, with support of this new technology in the reduction of costs and increased value added. HMS is in the process of being developed in the so-called "Consortium of the Rich Countries for the 21st Century", which involves governments, companies and universities from the first world countries, developing technology and knowledge related to the Holonic Manufacturing System (HMS). This new concept, under development by the above consortium, will allow the countries that hold this advancement to overcome the challenges of the globalized market and gain even more international competitiveness.

사이버-물리 생산 시스템을 위한 혼용학습기반 예측적 공정계획 메커니즘

Cyber-Physical Production Systems (CPPS), which pursue the implementation of machine intelligence in manufacturing systems, receive much attention as an advanced technology in Smart Factories. CPPS significantly necessitates the selflearning capability because this capability enables manufacturing objects to foresee performance results during their process planning activities and thus to make data-driven autonomous and collaborative decisions. The present work designs and implements a self-learning factory mechanism, which performs predictive process planning for energy reduction in metal cutting industries based on a hybrid-learning approach. The hybrid-learning approach is designed to accommodate traditional machine-learning and transfer-learning, thereby providing the ability of predictive modeling in both data sufficient and insufficient environments. Those manufacturing objects are agentized under the paradigm of Holonic Manufacturing Systems to determine the best energy-efficient machine tool through their self-decisions and interactions without the intervention of humans’ decisions. For such purpose, this paper includes: the proposition of the hybrid-learning approach, the design of system architecture and operational procedure for the self-learning factory, and the implementation of a prototype system.

Evaluation of JADE multi-agent system and Erlang holonic control implementations for a manufacturing cell

ABSTRACTThis paper presents an evaluation of two holonic control implementations, using the Java Agent Development (JADE) framework multi-agent system and the Erlang programming language, respectively. Erlang exhibits several characteristics that make it suitable – and even potentially beneficial – for holonic control, while JADE multi-agent systems have become the status quo in holonic systems research. The evaluation is based on both quantitative and qualitative measures, which are specifically formulated to focus on the implementation of holonic control. Ultimately, the paper aims to aid the developers of holonic manufacturing systems in making informed decisions regarding the choice of implementation language. To this end, the authors reflect on the experience and insight gained from the evaluation experiment. The evaluation shows that both implementations are capable of satisfying the requirements and desired characteristics for holonic manufacturing systems. The authors find that Erlang is especially very well suited for implementing holonic control, offering several important benefits, and warrants further exploration and development.

On rescheduling in holonic manufacturing systems

This paper treats a topical issue for present manufacturing systems, which is the needed adaptability when manufacturing orders are received in an unpredictable way and/or a perturbation appears with respect to the used resources. To face such cases, a rescheduling mechanism is necessary and such a possibility is investigated for a holonic system that materializes its coordination through a combination between the Contract Net Protocol and Distributed Constraint Satisfaction Problem. The analysis is carried out for two of the most used distributed algorithms – Asynchronous Backtracking and Weak Commitment Search. Moreover, the improvement of searching through a learning process is also examined. The proposed method is investigated for a case study, by doing simulation experiments with the system colored Petri net model. The results are analyzed for cases when a new command received during execution is more or less important than the ongoing ones and for situations when a resource becomes unavailable.

Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation

Manufacturing industries have recently promoted smart manufacturing (SM) for achieving intelligence, connectedness, and responsiveness of manufacturing objects consisting of man, machine, and material. Traditional manufacturing platforms, which identify generic frameworks where common functionalities are shareable and diverse applications are workable, mainly focused on remote collaboration, distributed control, and data integration; however, they are limited to incorporating those characteristic achievements. The present work introduces an SM-toward manufacturing platform. The proposed platform incorporates the capabilities of (1) virtualization of manufacturing objects for their autonomy and cooperation, (2) processing of real and various manufacturing data for mediating physical and virtual objects, and (3) data-driven decision-making for predictive planning on those objects. For such capabilities, the proposed platform advances the framework of Holonic Manufacturing Systems with the use of agent technology. It integrates a distributed data warehouse to encompass data specification, storage, processing, and retrieval. It applies a data analytics approach to create empirical decision-making models based on real and historical data. Furthermore, it uses open and standardized data interfaces to embody interoperable data exchange across shop floors and manufacturing applications. We present the architecture and technical methods for implementing the proposed platform. We also present a prototype implementation to demonstrate the feasibility and effectiveness of the platform in energy-efficient machining.

Implementation of Eye-Tracking Technology in Holonic Manufacturing Systems

Abstract During the last decade, Eye-Tracking technology has been popular amongst practitioners and researchers from various disciplines. In spite of widespread applications of Eye-Tracking technology‘, considerably less attention has been paid to the potential of this technology in manufacturing system. An holonic manufacturing system is a manufacturing system with high agility, which is an important characteristic for future manufacturing systems. This paper provides a theoretical overview and guidance for the implementation of Eye-Tracking technology in the holonic manufacturing system. The description of the Eye-Tracking technology and a brief discussion of potential application and the limitations is provided. This study discusses the implementation of Eye-Tracking technology as an holon.

사이버-물리 제조시스템의 지능적 의사결정을 위한 데이터 및 모델 연계 홀로닉 구조 개발

In manufacturing industries, the intelligence, autonomy and interconnectivity of manufacturing objects, and the adaptiveness in dynamic changes have been recognized as desirable objectives; however, they were hard to implement. Recently, the convergence of the advances in information and communication technology is making it possible to implement those objectives, called Smart Manufacturing (SM). Of these SM technologies, Cyber-Physical Production Systems (CPPS) will be considerably one of the most-advanced technologies because CPPS envisions dynamic and autonomous capabilities through mirroring physical and their cyber manufacturing objects in real-time. These capabilities match with the aim of Holonic Manufacturing Systems (HMS) in terms of autonomy and cooperation, thereby making HMS re-highlighted as a control architecture of CPPS. This paper presents the development of a data and model-interconnected holonic control architecture that especially considers the synchronization with a big data infrastructure and a data analytics environment. The present work aims at providing : 1) a predictive process-planning mechanism with the connection of manufacturing data and decision-making models, and 2) a systems integration environment for opening the information about intelligent manufacturing objects. The goals of the present work are to increase intelligence and interconnection, which are the major performances pursued by CPPS.

The Holonic View of Organizations and Firms

In the world of organizations, firms, management and control, in general, a silent conceptual movement has been under way for less than 50 years now, beginning in 1967, when Arthur Koestler formally introduced the concepts of holon, holarchy (a hierarchical structure of holons) and holonic network (a reticular structure of holons). Thirty years later, Ken Wilber tried to generalize the holonic perspective. The new holonic perspective forces us to observe the surrounding universe (at a physical and biological level, and in the real or formal sense) by taking into account the whole/part (holarchies) and the before/after (holonic networks) relationships: any observable unit is at the same time a whole—composed of smaller parts—and part of a larger whole. In order to clarify the holonic vison and the ideas behind holons, holarchies and holonic networks, this study will examine some significant uses of these concepts and, in particular, the main sources of inspiration for those production systems referred to as the holonic manufacturing systems, comparing these to those defined as bionic and fractal manufacturing systems. Some forms of inter‐organizational networks, as well as holonic and virtual organizations, have also been considered. Copyright © 2017 John Wiley & Sons, Ltd.

Implementation of an Adapted Holonic Production Architecture

Recent developments in information and computation technologies open up possibilities for the practical implementation of flexible, self-controlling production systems. The decentralization of existing production systems and their control plays a decisive role in creating the demanded flexibility and achieving an overall self-controlled system. The basic concept of the decentralization of production systems was set by the paradigm of Holonic Manufacturing Systems (HMS). In a HMS an element (holon) of the production system works autonomously with its own schedule and properties. Just through the cooperation with other holons central tasks of the production system are determined and subsequently executed. In this paper the flexibility and self-control of the production system was applicated through the distribution of decision-making by adding supplementary information acquisition and processing tools to former executive units. The previously procedural connection of the machines was dissolved and replaced by a peer-to-peer communication protocol. Superordinate controlling units, mostly PLCs, were abolished and instead decentralized controlling agents were implemented. The automatic control of the entire system is reached just through the communication of all devices, a machine-based scheduling and additional monitoring agents. The underlying architecture is based on Holonic Manufacturing concepts including order agent, machine, resource, product, logistic and supply holons. In this paper the adapted architecture is presented and subsequently the practical implementation in a research laboratory is described.