CIRP Encyclopedia of Production Engineering

Living Edition
| Editors: The International Academy for Production Engineering, Sami Chatti, Tullio Tolio

Holonic Manufacturing Systems

  • Hendrik Van BrusselEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-35950-7_6556-4

Synonyms

Definition

A holonic manufacturing system (HMS) is a manufacturing system (MS) that is distributively controlled according to the holonic system paradigm. The MS components are modeled as autonomous, collaborative entities (agents), called holons. The production order holon calls for the required resources (resource holons) and production expertise (product holon) and schedules the consecutive production steps. Staff holons, e.g., schedulers, may be used to give centralized advice. Manufacturing execution is achieved by a distributed multi-agent system that coordinates and controls the interactions between the holons so as to combine the advantages of hierarchical and heterarchical control.

Theory and Application

History

The term “holon” was originally coined by Arthur Koestler (1967/1996) to explain the working of social organizations and living organisms. Koestler defined a holon as an autonomous, self-reliant unit able to function without instructions from higher authorities. Simultaneously, this holon would be subject to control from one or more of these higher authorities. Another observation by Koestler, also inspired by Herbert Simon in his parable of the two watchmakers (Simon 1968), was that complex systems will evolve from simple systems much more rapidly if there are stable intermediate forms than if there are not. Holons are such stable intermediate forms, able to cope with disturbances and providing functionality to a larger whole.

Following this definition, a holonic system or holarchy is defined as a set of holons that besides functioning as separate wholes and together as a greater whole also function in coordination with their local environment.

In the late 1980s, Koestler’s forgotten ideas were rediscovered by Japanese manufacturing scientists and adopted as the new paradigm for the factory of the twenty-first century. It became one of the key ideas underlying the ambitious international IMS (intelligent manufacturing system) research program introduced in 1990 by our CIRP colleague H. Yoshikawa. Part of this IMS program, the HMS (holonic manufacturing system) project was launched in 1994 and later continued in several EU framework programs and in national research programs of several countries. The HMS consortium translated the concepts that Koestler developed for social organizations into a set of appropriate concepts for manufacturing industries. The goal was to attain in manufacturing the benefits that holonic organization provides to living organisms and societies, i.e., stability in the face of disturbances, adaptability and flexibility in the face of change, and efficient use of available resources. The HMS concept combines the best features of hierarchical and heterarchical organization. The holarchy preserves the stability of a hierarchy while providing the dynamic flexibility of a heterarchy.

The HMS Reference Architecture (RA)

Important to note at the outset is that a holonic (manufacturing) system implies a structural decomposition of system components, in contrast to the commonly used functional decomposition, in which each component represents part of the system functionality. Thus, the holons described in this way reflect reality as single source of truth. As an example, for navigation purposes one can use an itinerary (function oriented) or a road map (structure oriented). An itinerary does not require map reading skills but is not robust against disturbances or changes in the world of interest. A map offers a much more robust solution. Provided the user has map reading skills, disturbances and changes are solved easily by adapting the currently chosen route by a new one. Also, changes in reality (e.g., a new roundabout) are easily taken into account by locally adapting the holon description (map).

An important consequence of the structural decomposition used in the HMS concept is that the system structure is decoupled from the system control algorithm. The RA only maps functionality onto a system decomposition and defines interactions between component types, without however imposing rules on the interaction dynamics. These latter are governed by an HMES (holonic manufacturing execution system), to be discussed later. The structural decomposition is described in a reference architecture (RA). Hereafter, the reference architecture PROSA (Van Brussel et al. 1998) will be described. It is nowadays accepted as a de facto standard in HMS reference architectures.

The three relatively independent manufacturing concerns that can be distinguished in any manufacturing setting, namely, resource aspects, product- and process-related technological aspects, and logistics concerns, have led to the definition of three types of basic holons, namely, resource holons, product holons, and order holons. An HMS consists of resource holons, product holons, and order holons, interacting with each other as shown in Fig. 1.
Fig. 1

Basic building blocks (holons) of an HMS and their interrelations

A resource holon contains a physical part, namely, a production resource of the manufacturing system, and an information processing part that controls the resource. It offers production capacity and functionality to the surrounding holons. It holds the methods to allocate the production resources and the knowledge and the procedures to organize, use, and control these production resources to drive production. A resource holon is an abstraction of the production means such as a factory, a machine shop, machines, furnaces, conveyors, pipelines, pallets, components, raw materials, tools, tool holders, material storage, personnel, energy, and floor space.

A product holon holds the process and product knowledge to assure the correct manufacture of the product. It contains consistent and up-to-date information on the product life cycle, user requirements, design, process plans, bills of material, quality assurance procedures, etc. The product holon comprises functionalities that are traditionally covered by product design, process planning, and quality assurance.

An order holon represents a task in the manufacturing system. It is responsible for performing the assigned work correctly and on time. It manages the physical product being produced, the product state model, and all logistic information processing related to the job. An order holon may represent customer orders, make-to-stock orders, prototype-making orders, maintenance and repair orders, etc. The order holon performs tasks traditionally assigned to a dispatcher, a progress monitor, and a short-term scheduler.

As shown in Fig. 1, these three types of holons exchange knowledge about the manufacturing system. Product holons and resource holons communicate process knowledge, product holons and order holons exchange production knowledge, and resource holons and order holons share process execution knowledge.

The reference architecture foresees in the possibility to provide staff holons to assist the basic holons in performing their work. For instance, in view of the complexity of resource allocation, a scheduler staff holon may assist by giving advice on the best schedule to follow, if that would be available. The concept of staff holons allows for the presence of centralized elements and functionality in the architecture, which is useful for problems that are hard to solve in a distributed manner. It allows a smooth migration from current hierarchical shop floor control systems to a holonic architecture. Since a staff holon only gives advice, it does not introduce a hierarchical rigidity into the system, since the final decision is still to be taken by the basic holon.

Holonic Manufacturing Execution Systems (HMES)

As mentioned above, an important advantage of the PROSA reference architecture is that the system structure is decoupled from the control algorithm. Therefore, in order to offer fully fledged manufacturing capability, PROSA has to be augmented with an appropriate coordination and control mechanism, to ensure that the process plans are properly executed. An added asset would be the capability to emergently forecast the workload of the manufacturing resources, as well as lead times and routings of the products.

In this section, a bioinspired, multi-agent coordination and control mechanism, inspired by stigmergy (the food foraging behavior in ant colonies), is proposed as a representative example of an HMES (Valckenaers and Van Brussel 2005). The PROSA holons create many lightweight agents (ants) to collect information on their behalf. Therefore, it is called delegate-multi-agent system (D-MAS). The resource and/or product holons use a feasibility D-MAS to place digital road signs showing technically correct routings to the order holons. The order holons make use of two D-MAS types: exploring ants and intention ants. The behaviors of these D-MASs are based on the food foraging mechanism of ants. In this context, ants travel through resources querying for available allocation slots.

Order agents create at a regular frequency ant agents that scout for possible solutions. An exploring ant generates one feasible solution while traveling virtually through the factory and making the resource agents virtually perform the required processing steps (Fig. 2). The ant is created at the location of its order agent. When the exploring ant arrives at a processing unit, it retrieves the processing capabilities from the resource agent and presents them to the product agent to discover which processing steps can be performed. The option selection mechanism of the ant, deciding which product routing will be explored, is a plug-in for the MES because it is system dependent.
Fig. 2

Exploring ant agents scout for solutions on behalf of their order agent

When an exploring ant finds a solution, it reports back to the order agent. This order agent evaluates the performance of this solution. At a given moment, the order agent selects the most attractive solution and starts to create intention ants at a regular frequency. Intention ants propagate the order holon’s intentions, informing resource holons about future allocations (Fig. 3). Their behavior is similar to that of exploring ants except that their task is to book the reservation on the resources rather than requesting a virtual execution. The intention selection mechanism of the order agent is also a plug-in for the MES because it is system dependent.
Fig. 3

Order agent has selected the second solution and its intention ant reserves time slots on resources on its behalf

This combination of exploring and intention ants provides both order agents and resource agents with short-term forecasts, enabling predictive heterarchical control. This technology is called the production radar of the HMES.

Salient Features of Holonic Manufacturing Systems

An HMS based on a PROSA reference architecture and an HMES based on D-MAS have a set of interesting features which make it stand out from traditional hierarchical (Senchi et al. 1994) and heterarchical (Duffie 1990) MSs:
  • Robustness against external disturbances, such as rush orders and machine breakdown

  • Smooth scalability thanks to the inherent decoupling of the MS structure from the control algorithm

  • Built-in short-time prediction capability of future MS behavior (production radar)

Application Areas

The described holonic framework is, by its superb generality, applicable to many other domains outside the manufacturing industry, in fact everywhere where a group or even a swarm of active entities of a system have to work together to achieve a global goal. Examples of past manufacturing and nonmanufacturing applications of the PROSA/HMES framework, where the holonic approach has shown its potential superiority, are:
  • Large car body paint shop (Peeters et al. 2001), FMS for weaving loom parts, multiplant heat treatment facility (Saint Germain et al. 2011), complex chain conveyor facility, cross-docking facility

  • Traffic and transportation control

  • Open-pit mining, road construction, harvesting (Valckenaers et al. 2011.

  • Smart grids

  • Multi-robot systems, e.g., a fleet of wheelchairs (Philips et al. 2011.

Cross-References

References

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Copyright information

© CIRP 2018

Authors and Affiliations

  1. 1.Mechanical EngineeringKU Leuven3001 Heverlee (Leuven)Belgium

Section editors and affiliations

  • Günther Schuh
    • 1
  1. 1.Forschungsinstitut für Rationalisierung (FIR) e. VRWTH AachenAachenGermany