- Email: [email protected]
TOWARDS THE ASSESSMENT OF HOLONIC MANUFACTURING SYSTEMS
INCOM'2006: 12th IFAC/IFIP/IFORS/IEEE/IMS Symposium Information Control Problems in Manufacturing May 17-19 2006, Saint-Etienne, France
TOWARDS THE ASSESSMENT OF HOLONIC MANUFACTURING SYSTEMS Robert W. Brennan Department of Mechanical and Manufacturing Engineering University of Calgary, 2500 University Dr. N.W., Calgary, Canada, T2N 1N4 Email: [email protected]
Abstract: This paper reports on our preliminary work towards establishing target specifications that can be used to assess the performance of holonic manufacturing systems. This process first involves identifying the primary needs of holonic manufacturing systems then mapping these needs to appropriate metrics. Our work in this area has shown that there is general consensus about the primary needs of these systems, however further work is required to establish appropriate metrics. Copyright © 2002 IFAC Keywords: holonic systems, multi-agent systems, benchmarking.
1. INTRODUCTION
this needs to be demonstrated through appropriate benchmarking results.
The manufacturing domain has provided, and continues to provide, a wealth of practical problems for agent and holonic systems technologies. Although this area is traditionally known for its conservatism, it is not surprising that it is “one of the oldest and strongest areas of agent research” (Aparicio IV, 1999). For example, researchers have applied agent technology to manufacturing enterprise integration (Dabke, 1999), supply chain management (Shen et al., 1999), manufacturing planning, scheduling and control (Parunak, 1997; Maturana and Norrie, 1996), materials handling (Maturana and Norrie, 1996), rapid prototyping (Zubillaga-Elorza and Allen, 1999) and eventually holonic manufacturing systems (Bussmann, 1998; Van Brussel et al., 1998).
In order to properly benchmark the performance of holonic manufacturing systems relative to conventional manufacturing systems, it is important to first establish a clear set of target specifications that will be used as the basis of comparison. For example, a target specification may be that, for a given benchmark problem, throughput should exceed a certain level (based on the performance of a conventional system) or the system should be capable of responding to a change within a certain time interval. Clearly, these target specifications will need to based on a set of metrics. The question that arises at this point is how do we determine the appropriate set of metrics for holonic manufacturing systems? The typical approach that is used in engineering design is to first identify the needs or requirements of the system that is being developed, then map metrics to these needs. For example, McFarlane and Bussmann (2003) take a similar approach by mapping business trends to manufacturing system requirements, then mapping these requirements to the holonic control system properties.
Given this close fit between technology and problem domain as well as the amount of activity in this area, it is surprising however that the validation of agent and holonic systems is still in question. For example, McFarlane and Bussmann (2003) note that “… when fully developed, holonic manufacturing control has the potential to address many of the outstanding problems facing today’s industrial control systems”. Yet, before holonic systems can be widely adopted,
399
In this paper, we suggest that a similar approach can be used to facilitate the benchmarking of holonic manufacturing systems. More specifically, we propose following the conventional engineering design approach (Ulrich and Eppinger, 2004) of mapping requirements to metrics, then establishing target specifications based on these metrics. This approach is summarised in Figure 1.
• •
•
• Business Trends
Shortly after the HMS feasibility study, much of the fundamental work on the reference architecture for holonic manufacturing systems was conducted at the Katholieke Universiteit Leuven (Valckenaers et al., 1997; Van Brussel et al., 1998; Wyns, 1999). Not surprisingly, the basic requirements for holonic manufacturing systems remained relatively consistent with Christensen’s (1994) list. For example, in the area of “disturbance handling” Valckenaers et al. (1997) identified “autonomy” as the “ability to work under permanent perturbation”, “flexibility” was identified as the “ability to accept modifications in the production settings” (this was extended to include the need for “agility”, or the “capacity to use different alternatives”), and “robustness” was identified as the “capability to stay in a legal and stable state under perturbations”. “Cost” was also added to the list of needs as the cost of the initial development of the control system (Valckenaers et al., 1997) as well as the maintenance cost of the control system (Wyns, 1999).
Manufacturing System Requirements
Manufacturing System Metrics
Manufacturing System Target Specifications
Fig. 1. Establishing a set of target specifications for holonic systems (adapted from (McFarlane and Bussmann, 2003)) The paper begins with a summary of the basic requirements that have been identified for holonic manufacturing systems. Next we review the work on performance measures for these systems and related systems and attempt to map these metrics to the needs. We conclude with a summary of our views on the current state of work in this area and the requirements for future research.
In his thesis on the reference architecture for holonic manufacturing systems, Wyns (1999) identified three interrelated sets of requirements: “system requirements”, “system architecture requirements”, and “reference architecture requirements”. In this case, the reference architecture “serves as a template for developing system architectures for specific problems” while the system architecture is “used to implement the final control system” (Wyns, 1999). In this paper we focus on the “system requirements”, however these requirements can be extended to the reference architecture as Wyns (1999) suggests.
2. PRIMARY NEEDS OF NEXT GENERATION MANUFACTURING SYSTEMS Much of the work that has been carried out in recent years into the development of holonic manufacturing systems was motivated by a main critical factor with existing systems: their “fragility” (Valckenaers et al., 1997). To address this issue, researchers have identified various requirements or needs of the next generation of manufacturing systems. In this section, we summarise the primary needs that were identified in the early stages of holonic manufacturing systems research and then compare this with the more recent work in this area.
More than a decade has passed since the HMS feasibility study reported by Christensen (1994), however the basic requirements for the next generation manufacturing systems promised by HMS have not changed. For example, Marik et al. (2002) hit all of the primary needs identified by Christensen (1994): disturbance handling (i.e., “anticipate critical situations and prepare … for them”), human integration (i.e., “system must by unified”), availability (i.e., “high degree of reliability”), flexibility (i.e., “reconfiguration (selforganization)”), and robustness (i.e., “reactive and proactive features”). Similarly, McFarlane and Bussmann (2003) identify a set of “manufacturing system” and “control system” requirements in their paper that map closely to these primary needs. In some cases however, the focus of work in this area has become more specific. For example, Fletcher et
Christensen (1994) provides a good starting point for the basic requirements of holonic manufacturing systems. In this paper on the initial architecture and standard directions for holonic manufacturing systems, the primary needs are summarised in a list of “critical factors for system architecture”. These needs, which followed from the 1994 feasibility study on holonic manufacturing systems, are summarised as follows (Christensen, 1994): •
Human integration: Support better and more extensive use of human intelligence. Availability: Provide higher reliability and maintainability despite system size and complexity. Flexibility: Support continuously changing product designs, product mixes, and small lot sizes. Robustness: Maintain system operability in the face of large and small malfunctions.
Disturbance handling: Provide better and faster recognition of and response to machine malfunctions, rush orders, unpredictable process yields, human errors, etc.
400
al. (2004) focus more on the issue of the requirements at the device level and as a result, focus less on human integration issues.
(ii) (iii)
X X X X X
X X X X X
The three sets of criteria described above relate to the primary objectives that apply to scheduling problems (Vollmann et al., 1992). First, we are concerned with minimising the amount of time that jobs spend in the manufacturing system (criteria based on completion time and criteria based on inventory levels). Next, a typical goal is to reduce late job completion (criteria based on due dates). Finally, an important consideration for manufacturing systems, consisting of expensive equipment and personnel, is to fully utilise their limited capacity (criteria based on utilisation).
Fletcher et al. (2004)
Wyns (1999) X X X X X X
McFarlane & Bussmann (2003)
X X X X X
Marik et al. (2002)
X X X X X
Valckenaers et al. (1997)
System Requirement 1. Disturbance Handling 2. Human Integration 3. Availability 4. Flexibility 5. Robustness 6. Cost
Christensen (1994)
Paper
Figure 2 provides a summary of the primary needs identified in this section. The list of papers shown in this figure is not intended to be comprehensive, but rather representative of the work in this area since the HMS feasibility study. As can be seen in this figure, the primary needs of holonic manufacturing systems have remained consistent since the HMS feasibility study. The real challenge is mapping these needs/requirements to appropriate metrics, or as Chirn and McFarlane (2005) note, there is a “lack of appropriate performance criteria and quantitative measures to evaluate” these systems.
The Intelligent Manufacturing Systems Benchmarks and Performance Measures of Online Production Scheduling Systems (IMS, 2005) project has taken this basic approach to performance measurement (Terzi et al., 2004). For example, the Remote Factory project (Cavalieri et al., 2000; Cavalieri et al., 2002) uses an online emulation of the manufacturing plant as part of its Test Bench Assistant. In this project, two main criteria are used to compare different experimental scheduling algorithms: “quantitative measures based on different manufacturing performance indicators” and “qualitative measures based on more aggregated indicators (agility, robustness, etc.)” (Cavalieri et al., 1999). For example, Bandinelli (2005) maps performance measures to system requirements as follows:
X X X X X
Fig. 2. Holonic manufacturing systems primary needs 3. MEASURING THE PERFORMANCE OF HOLONIC MANUFACTURING SYSTEMS The holonic approach has been applied to various manufacturing planning and control problems (McFarlane and Bussmann, 2000). Unfortunately, in most cases the performance of the holonic system is either not reported or is not compared with the performance of a conventional system. One notable exception is the engine assembly plant case study by Bussmann and Sieverding (2001) where productivity is used to assess the robustness of a holonic manufacturing system. In this section, we summarise the work that has been done on identifying metrics that can be used to validate the performance of holonic manufacturing systems and attempt to map these metrics back to the requirements identified in the previous section.
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• • • •
Reliability: lateness, tardiness, earliness, % delayed orders, % defective work-pieces, number of machine disruptions, total machine repair time; Responsiveness: makespan, flow-time; Flexibility: setup time, queue time; Cost: value of cancelled orders; and Assets: WIP (work in progress), utilization.
It should be noted however, that Bandinelli’s (2005) requirements relate to the scheduling algorithm rather than the overall holonic system. For example, setup time and queue time may be appropriate measures for assessing the flexibility of a schedule; however, they may not be sufficient for assessing how well the system “supports continuously changing product designs, product mixes, and small lot sizes” (Christensen, 1994).
In the majority of cases where performance measures are identified in the literature, typical scheduling metrics are used. For example, these manufacturing system performance metrics tend to fall in the following categories (French, 1982): (i)
criteria based on due date performance (e.g., average lateness, average tardiness, proportion of tardy jobs), criteria based on inventory costs or resource utilisations (e.g., number of parts waiting, number of finished goods, number of parts in process, machine idle time).
criteria based on job completion times (e.g., average job flowtime),
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1. 2. 3. 4. 5. 6. 7.
6
6
System Productivity
5
Reconfigurabilty of software
4
Operational complexity of control software
Agent Specialization
3
Strategic complexity of control software
Agent Planning Horizon
message traffic
Maintenance Cost
Installation Cost
Cost of Cancelled Orders
Agent Decision-making Flexibility
1
scheduling criteria based on utilisation
scheduling criteria based on due dates
scheduling criteria based on inventory levels
scheduling criteria based on completion time
Metric System Requirement 1. Disturbance Handling 2. Human Integration 3. Availability 4. Flexibility 5. Robustness 6. Cost
1 1
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6 7
1
2
2
Bandinelli (2005) Wyns (1999) Brennan and Norrie (2003) Davis and Thompson (1993) Parunak (1987) Chirn and McFarlane (2005) Bussmann and Sieverding (2001)
Fig. 3. Needs-metrics matrix for holonic manufacturing systems The performance metrics that have been described so far relate to the efficient operation of the manufacturing system. These manufacturing system performance metrics are unquestionably important for holonic systems validation; however, as implied above, they may not be sufficient to demonstrate that all of the needs have been met. In order to evaluate the performance of holonic control systems relative to other, more conventional control approaches it is important to define performance measures that relate to the control architectures itself. These “operational performance metrics” (Chirn and McFarlane, 2005) or “control system metrics” (Brennan and Norrie, 2003) tend to be more nebulous, and as a result, less prevalent in the literature.
(Davis and Thompson, 1993; Villa, 1991): i.e., the full time span that an agent considers. This metric appears to map most closely “flexibility” since the long planning horizons will limit flexibility while short planning horizons (i.e., reactive behaviour) will support it. Parunak (1987) also proposed using agent specialisation to characterise the different capabilities of agents in a control architecture. Although this structural parameter could map to various requirements, it seems to fit best into “flexibility”: i.e., the more specialised agents available in the control system, the greater the range of behaviour. Somewhat related to agent specialisation, Brennan and Norrie (2003) also define a parameter that allows control architectures to be compared on the basis of how much decision-making flexibility each architecture offers that is based on the computer science theory of formal languages (Hopcroft and Ullman, 1979). The flexibility referred to here is related to the number of possible ways that agents in the control system can generate a solution to the control problem. In a very flexible system, a variety of agents will cooperate to generate a solution (e.g., complete a part process plan) that reflects the current state of the system. Decision-making flexibility is also necessary to handle disruptions such as machine failures and fluctuations in demand; this will require agents with the capability of altering their behaviour subject to these disruptions.
The control system metrics identified by Brennan and Norrie (2003) fall into two categories: those that can be evaluated experimentally, and those that describe the structure of the control architecture. The experimental metrics reported in (Brennan and Norrie, 2003) are primarily based on message traffic in the control architecture (coupling, listening coefficient) and how likely individual agents are to change during operation (volatility). All of these metrics relate to the reliability and maintainability of the control system as it grows (i.e., its “availability”). The second class of control system metrics are intended to help define each individual architecture with greater precision and to provide a basis for comparison between each of the approaches. It is through the analysis of these parameters that the inherent characteristics of various control approaches can be evaluated. One structural parameter that is commonly used in the literature is planning horizon
Chirn and McFarlane (2005) identify control system performance metrics that are related to the control system software. The “strategic complexity” of the control system software is a measure of the
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complexity of the Petri nets describing the overall control strategy. Similarly, the “operational complexity” of the control system software is defined as the weighted lines of code. Both of these measures appear to relate to the requirement for “availability”: i.e., the software complexity will factor in when considering how reliable and maintainable the control system is. In addition to these metrics, they also measure the “reconfigurability” (i.e., “flexibility”) of the control software through its “extension rate” and “reuse rate”.
can begin on identifying appropriate target specifications. This work should tie in nicely with the work that has been done by the IMS Benchmarks and Performance Measures of Online Production Scheduling Systems (IMS, 2005) group. In particular, the identification of target specifications will be closely linked to the identification of common benchmarks. REFERENCES Aparicio IV, M. (1999) Internet-scale network intelligence, IEEE Internet Computing, September/October, pp. 38-40. Bandinelli, R. (2005) Final report on the benchmarking service, Network of Excellence on Intelligent Manufacturing Systems Report, IST2001-65001. Brennan, R.W. and D.H. Norrie (2003) Metrics for evaluating distributed manufacturing control systems, Computers in Industry, 51(2), pp. 225235. Bussmann, S. (1998) An Agent-Oriented Architecture for Holonic Manufacturing Control, In: Proceedings of the First International Workshop on IMS, Lausanne, Switzerland. Bussmann, S. and J. Sieverding (2001) Holonic control of an engine assembly plant: an industrial evaluation, In: Proceedings of the IEEE Systems, Man, and Cybernetics Conference, pp. 169-174, Tucson, USA. Cavalieri, S., Bongaerts, L., Macchi, M., Taisch, M., and Wyns, J. (1999) A benchmark framework for manufacturing control. In: Proceedings of the 2nd International Workshop on Intelligent Manufacturing Systems (H. Van Brussel, P. Valekenaers (Eds.)), pp. 225–236, Katholieke Universiteit Leuven, Leuven, Belgium. Cavalieri, S., M. Garetti, M. Macchi, and M. Taisch (2000) An experimental benchmarking of two multi-agent architectures for production scheduling and control, Computers in Industry, 43(2), pp. 139-152. Cavalieri, S., M. Macchi, S. Terzi (2002) Benchmarking manufacturing control systems: development issues for the performance measurement system, Proceedings of the IFIP Performance Measurement Workshop, Hannover, Germany. Chirn, J. and D.C. McFarlane (2005) Evaluating holonic manufacturing systems: a case study, In: Proceedings of the 16th IFAC World Conference, Prague, Czech Republic. Christensen, J.H. (1994) Holonic manufacturing systems: initial architecture and standards directions, In: Proceedings of the First European Conference on Holonic Manufacturing Systems, Hannover, Germany. Dabke, P. (1999) Enterprise integration via CORBAbased information agents, IEEE Internet Computing, September/October, pp. 49-57. Davis, W. and S.D. Thompson (1993) Production planning and control hierarchy using a generic controller, IIE Transactions, 25, pp. 26-45.
Finally, we return to the engine assembly line case study of Bussmann and Sieverding (2001). As noted previously, they assess the robustness of their holonic system by computing its productivity over a long period of time. In this case, productivity is defined as the “maximal throughput of the system under disturbances divided by the maximal throughput of the system if no disturbances occur at all” (Bussmann and Sieverding, 2001). The metrics identified in this section are summarised in Figure 3. In this figure, we attempt to map the holonic manufacturing system needs identified previously to the metrics described here. It should be noted however that the mapping shown in this figure does not imply that each metric is sufficient to verify that each need is satisfied. For example, scheduling criteria that are based on completion times provide input on determining if a system is flexible, but other metrics also need to be used (e.g., reconfigurability of the software). 4. CONCLUSIONS Clearly, more work is needed in order to establish target specifications for holonic manufacturing systems. It appears that there is general consensus on the primary needs or requirements of holonic manufacturing systems, however more work is required on identifying appropriate metrics for these systems. As noted previously, in most cases the metrics identified in Figure 3 are not sufficient for determining if a given requirement is satisfied. In some cases, a combination of metrics may come close (e.g., a combination of the metrics associated with “flexibility”), however each requirement should be investigated more closely relative to its associated metrics before any conclusions can be drawn. As can be seen in Figure 3, there is also a lack of metrics associated with “human integration”. These metrics tend to be of a qualitative nature (e.g., user friendliness, etc.), and as such, are less obvious in the existing literature. It should be stressed that Figure 3 is merely a starting point for this work that can be used as a basis for discussion. In particular, further work is required to identify the most appropriate metrics associated with each of the system requirements. At that point, work
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Fletcher, M., D.C. McFarlane, J.P. Brusey, D. Jarvis, V. Marik, M. Pechoucek, and R.W. Brennan (2004) Towards a methodology for holonic manufacturing systems, In: Proceedings of the IMS International Forum on Global Challenges in Manufacturing, pp. 792-800, Cernobbio, Italy. French, S. (1982) Sequencing and Scheduling: An Introduction to the Mathematics of the Job Shop, Ellis Horwood. Hopcroft, J.E. and J.D. Ullman (1979) Introduction to Automata Theory, Languages, and Computation, Addison-Wesley. Intelligent Manufacturing Systems Benchmarks and Performance Measures of Online Production Scheduling Systems (2005) Web Site, http://www.ims-noe.org/about.asp. Marik, V., M. Fletcher, and M. Pechoucek (2002) Holons and agents: recent developments and mutual impacts, In: Multi-agent Systems and Applications II, Lecture Notes in Artificial Intelligence (V. Marik, O. Stepankova, H. Krautwurmova, M. Luck (Eds.)) pp. 233-267, Springer-Verlag, Berlin. Maturana, F. and D.H. Norrie (1996) Multi-agent mediator architecture for distributed manufacturing, Journal of Intelligent Manufacturing, 7, pp. 257-270. McFarlane, D.C., and Bussmann, S. (2000) Developments in holonic production planning and control, Production Planning & Control, 11(6), pp. 522–536. McFarlane, D.C. and S. Bussmann (2003) Holonic manufacturing control: rationales, developments and open issues, In: Advances in the Holonic Approach to Agent-based Manufacturing (S.M. Deen (Ed.)), pp. 303-326, Springer-Verlag. Parunak, H.V.D. (1997) Manufacturing experience with the contract net, In: Distributed Artificial Intelligence (M. N. Huhns, (Ed.)), pp. 285-310, Pitman. Shen, W., M. Ulieru, D.H. Norrie and R. Kremer (1999) Implementing the internet enabled supply chain through a collaborative agent system, In: Agents’99 Workshop on Agent Based Management of the Internet-Enabled SupplyChain, pp. 55-62, Seattle, USA. Terzi, S., S. Cavalieri, R. Bandinelli, and M. Tucci (2004) Experiences in distributed simulation for bechmarking production scheduling systems, Proceedings of the IMS International Forum, pp. 1017-1026, Cernobbio, Italy. Ulrich, K.T. and S.D. Eppinger (2004) Product Design and Development, Third Edition, McGraw Hill Irwin. Valckenaers, P., H. Van Brussel, L. Bongaerts, and J. Wyns (1997) Holonic manufacturing systems, Integrated Computer-Aided Engineering, 4, pp. 191-201. Van Brussel, H., J. Wyns, P. Valckenaers, L. Bongaerts, and P. Peeters (1998) Reference architecture for holonic manufacturing systems: PROSA, Computers in Industry, 37, pp. 255274.
Villa, A. (1991) Hybrid Control Systems in Manufacturing, Gordon and Breach. Vollmann, T.E., W.L. Berry, D.C. Whybark (1992) Manufacturing Planning and Control Systems, 3rd Edition, Irwin. Wyns, J. (1999) Reference architecture for holonic manufacturing systems, Ph.D. Thesis, Katholieke Universiteit Leuven. Zubillaga-Elorza, F. and C.R. Allen (1999) Virtual instrument toolkit: rapid prototyping on the web, IEEE Internet Computing, September/October, pp. 41-48
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TOWARDS THE ASSESSMENT OF HOLONIC MANUFACTURING SYSTEMS Robert W. Brennan Department of Mechanical and Manufacturing Engineering University of Calgary, 2500 University Dr. N.W., Calgary, Canada, T2N 1N4 Email: [email protected]
Abstract: This paper reports on our preliminary work towards establishing target specifications that can be used to assess the performance of holonic manufacturing systems. This process first involves identifying the primary needs of holonic manufacturing systems then mapping these needs to appropriate metrics. Our work in this area has shown that there is general consensus about the primary needs of these systems, however further work is required to establish appropriate metrics. Copyright © 2002 IFAC Keywords: holonic systems, multi-agent systems, benchmarking.
1. INTRODUCTION
this needs to be demonstrated through appropriate benchmarking results.
The manufacturing domain has provided, and continues to provide, a wealth of practical problems for agent and holonic systems technologies. Although this area is traditionally known for its conservatism, it is not surprising that it is “one of the oldest and strongest areas of agent research” (Aparicio IV, 1999). For example, researchers have applied agent technology to manufacturing enterprise integration (Dabke, 1999), supply chain management (Shen et al., 1999), manufacturing planning, scheduling and control (Parunak, 1997; Maturana and Norrie, 1996), materials handling (Maturana and Norrie, 1996), rapid prototyping (Zubillaga-Elorza and Allen, 1999) and eventually holonic manufacturing systems (Bussmann, 1998; Van Brussel et al., 1998).
In order to properly benchmark the performance of holonic manufacturing systems relative to conventional manufacturing systems, it is important to first establish a clear set of target specifications that will be used as the basis of comparison. For example, a target specification may be that, for a given benchmark problem, throughput should exceed a certain level (based on the performance of a conventional system) or the system should be capable of responding to a change within a certain time interval. Clearly, these target specifications will need to based on a set of metrics. The question that arises at this point is how do we determine the appropriate set of metrics for holonic manufacturing systems? The typical approach that is used in engineering design is to first identify the needs or requirements of the system that is being developed, then map metrics to these needs. For example, McFarlane and Bussmann (2003) take a similar approach by mapping business trends to manufacturing system requirements, then mapping these requirements to the holonic control system properties.
Given this close fit between technology and problem domain as well as the amount of activity in this area, it is surprising however that the validation of agent and holonic systems is still in question. For example, McFarlane and Bussmann (2003) note that “… when fully developed, holonic manufacturing control has the potential to address many of the outstanding problems facing today’s industrial control systems”. Yet, before holonic systems can be widely adopted,
399
In this paper, we suggest that a similar approach can be used to facilitate the benchmarking of holonic manufacturing systems. More specifically, we propose following the conventional engineering design approach (Ulrich and Eppinger, 2004) of mapping requirements to metrics, then establishing target specifications based on these metrics. This approach is summarised in Figure 1.
• •
•
• Business Trends
Shortly after the HMS feasibility study, much of the fundamental work on the reference architecture for holonic manufacturing systems was conducted at the Katholieke Universiteit Leuven (Valckenaers et al., 1997; Van Brussel et al., 1998; Wyns, 1999). Not surprisingly, the basic requirements for holonic manufacturing systems remained relatively consistent with Christensen’s (1994) list. For example, in the area of “disturbance handling” Valckenaers et al. (1997) identified “autonomy” as the “ability to work under permanent perturbation”, “flexibility” was identified as the “ability to accept modifications in the production settings” (this was extended to include the need for “agility”, or the “capacity to use different alternatives”), and “robustness” was identified as the “capability to stay in a legal and stable state under perturbations”. “Cost” was also added to the list of needs as the cost of the initial development of the control system (Valckenaers et al., 1997) as well as the maintenance cost of the control system (Wyns, 1999).
Manufacturing System Requirements
Manufacturing System Metrics
Manufacturing System Target Specifications
Fig. 1. Establishing a set of target specifications for holonic systems (adapted from (McFarlane and Bussmann, 2003)) The paper begins with a summary of the basic requirements that have been identified for holonic manufacturing systems. Next we review the work on performance measures for these systems and related systems and attempt to map these metrics to the needs. We conclude with a summary of our views on the current state of work in this area and the requirements for future research.
In his thesis on the reference architecture for holonic manufacturing systems, Wyns (1999) identified three interrelated sets of requirements: “system requirements”, “system architecture requirements”, and “reference architecture requirements”. In this case, the reference architecture “serves as a template for developing system architectures for specific problems” while the system architecture is “used to implement the final control system” (Wyns, 1999). In this paper we focus on the “system requirements”, however these requirements can be extended to the reference architecture as Wyns (1999) suggests.
2. PRIMARY NEEDS OF NEXT GENERATION MANUFACTURING SYSTEMS Much of the work that has been carried out in recent years into the development of holonic manufacturing systems was motivated by a main critical factor with existing systems: their “fragility” (Valckenaers et al., 1997). To address this issue, researchers have identified various requirements or needs of the next generation of manufacturing systems. In this section, we summarise the primary needs that were identified in the early stages of holonic manufacturing systems research and then compare this with the more recent work in this area.
More than a decade has passed since the HMS feasibility study reported by Christensen (1994), however the basic requirements for the next generation manufacturing systems promised by HMS have not changed. For example, Marik et al. (2002) hit all of the primary needs identified by Christensen (1994): disturbance handling (i.e., “anticipate critical situations and prepare … for them”), human integration (i.e., “system must by unified”), availability (i.e., “high degree of reliability”), flexibility (i.e., “reconfiguration (selforganization)”), and robustness (i.e., “reactive and proactive features”). Similarly, McFarlane and Bussmann (2003) identify a set of “manufacturing system” and “control system” requirements in their paper that map closely to these primary needs. In some cases however, the focus of work in this area has become more specific. For example, Fletcher et
Christensen (1994) provides a good starting point for the basic requirements of holonic manufacturing systems. In this paper on the initial architecture and standard directions for holonic manufacturing systems, the primary needs are summarised in a list of “critical factors for system architecture”. These needs, which followed from the 1994 feasibility study on holonic manufacturing systems, are summarised as follows (Christensen, 1994): •
Human integration: Support better and more extensive use of human intelligence. Availability: Provide higher reliability and maintainability despite system size and complexity. Flexibility: Support continuously changing product designs, product mixes, and small lot sizes. Robustness: Maintain system operability in the face of large and small malfunctions.
Disturbance handling: Provide better and faster recognition of and response to machine malfunctions, rush orders, unpredictable process yields, human errors, etc.
400
al. (2004) focus more on the issue of the requirements at the device level and as a result, focus less on human integration issues.
(ii) (iii)
X X X X X
X X X X X
The three sets of criteria described above relate to the primary objectives that apply to scheduling problems (Vollmann et al., 1992). First, we are concerned with minimising the amount of time that jobs spend in the manufacturing system (criteria based on completion time and criteria based on inventory levels). Next, a typical goal is to reduce late job completion (criteria based on due dates). Finally, an important consideration for manufacturing systems, consisting of expensive equipment and personnel, is to fully utilise their limited capacity (criteria based on utilisation).
Fletcher et al. (2004)
Wyns (1999) X X X X X X
McFarlane & Bussmann (2003)
X X X X X
Marik et al. (2002)
X X X X X
Valckenaers et al. (1997)
System Requirement 1. Disturbance Handling 2. Human Integration 3. Availability 4. Flexibility 5. Robustness 6. Cost
Christensen (1994)
Paper
Figure 2 provides a summary of the primary needs identified in this section. The list of papers shown in this figure is not intended to be comprehensive, but rather representative of the work in this area since the HMS feasibility study. As can be seen in this figure, the primary needs of holonic manufacturing systems have remained consistent since the HMS feasibility study. The real challenge is mapping these needs/requirements to appropriate metrics, or as Chirn and McFarlane (2005) note, there is a “lack of appropriate performance criteria and quantitative measures to evaluate” these systems.
The Intelligent Manufacturing Systems Benchmarks and Performance Measures of Online Production Scheduling Systems (IMS, 2005) project has taken this basic approach to performance measurement (Terzi et al., 2004). For example, the Remote Factory project (Cavalieri et al., 2000; Cavalieri et al., 2002) uses an online emulation of the manufacturing plant as part of its Test Bench Assistant. In this project, two main criteria are used to compare different experimental scheduling algorithms: “quantitative measures based on different manufacturing performance indicators” and “qualitative measures based on more aggregated indicators (agility, robustness, etc.)” (Cavalieri et al., 1999). For example, Bandinelli (2005) maps performance measures to system requirements as follows:
X X X X X
Fig. 2. Holonic manufacturing systems primary needs 3. MEASURING THE PERFORMANCE OF HOLONIC MANUFACTURING SYSTEMS The holonic approach has been applied to various manufacturing planning and control problems (McFarlane and Bussmann, 2000). Unfortunately, in most cases the performance of the holonic system is either not reported or is not compared with the performance of a conventional system. One notable exception is the engine assembly plant case study by Bussmann and Sieverding (2001) where productivity is used to assess the robustness of a holonic manufacturing system. In this section, we summarise the work that has been done on identifying metrics that can be used to validate the performance of holonic manufacturing systems and attempt to map these metrics back to the requirements identified in the previous section.
•
• • • •
Reliability: lateness, tardiness, earliness, % delayed orders, % defective work-pieces, number of machine disruptions, total machine repair time; Responsiveness: makespan, flow-time; Flexibility: setup time, queue time; Cost: value of cancelled orders; and Assets: WIP (work in progress), utilization.
It should be noted however, that Bandinelli’s (2005) requirements relate to the scheduling algorithm rather than the overall holonic system. For example, setup time and queue time may be appropriate measures for assessing the flexibility of a schedule; however, they may not be sufficient for assessing how well the system “supports continuously changing product designs, product mixes, and small lot sizes” (Christensen, 1994).
In the majority of cases where performance measures are identified in the literature, typical scheduling metrics are used. For example, these manufacturing system performance metrics tend to fall in the following categories (French, 1982): (i)
criteria based on due date performance (e.g., average lateness, average tardiness, proportion of tardy jobs), criteria based on inventory costs or resource utilisations (e.g., number of parts waiting, number of finished goods, number of parts in process, machine idle time).
criteria based on job completion times (e.g., average job flowtime),
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1. 2. 3. 4. 5. 6. 7.
6
6
System Productivity
5
Reconfigurabilty of software
4
Operational complexity of control software
Agent Specialization
3
Strategic complexity of control software
Agent Planning Horizon
message traffic
Maintenance Cost
Installation Cost
Cost of Cancelled Orders
Agent Decision-making Flexibility
1
scheduling criteria based on utilisation
scheduling criteria based on due dates
scheduling criteria based on inventory levels
scheduling criteria based on completion time
Metric System Requirement 1. Disturbance Handling 2. Human Integration 3. Availability 4. Flexibility 5. Robustness 6. Cost
1 1
1
3
1
6 7
1
2
2
Bandinelli (2005) Wyns (1999) Brennan and Norrie (2003) Davis and Thompson (1993) Parunak (1987) Chirn and McFarlane (2005) Bussmann and Sieverding (2001)
Fig. 3. Needs-metrics matrix for holonic manufacturing systems The performance metrics that have been described so far relate to the efficient operation of the manufacturing system. These manufacturing system performance metrics are unquestionably important for holonic systems validation; however, as implied above, they may not be sufficient to demonstrate that all of the needs have been met. In order to evaluate the performance of holonic control systems relative to other, more conventional control approaches it is important to define performance measures that relate to the control architectures itself. These “operational performance metrics” (Chirn and McFarlane, 2005) or “control system metrics” (Brennan and Norrie, 2003) tend to be more nebulous, and as a result, less prevalent in the literature.
(Davis and Thompson, 1993; Villa, 1991): i.e., the full time span that an agent considers. This metric appears to map most closely “flexibility” since the long planning horizons will limit flexibility while short planning horizons (i.e., reactive behaviour) will support it. Parunak (1987) also proposed using agent specialisation to characterise the different capabilities of agents in a control architecture. Although this structural parameter could map to various requirements, it seems to fit best into “flexibility”: i.e., the more specialised agents available in the control system, the greater the range of behaviour. Somewhat related to agent specialisation, Brennan and Norrie (2003) also define a parameter that allows control architectures to be compared on the basis of how much decision-making flexibility each architecture offers that is based on the computer science theory of formal languages (Hopcroft and Ullman, 1979). The flexibility referred to here is related to the number of possible ways that agents in the control system can generate a solution to the control problem. In a very flexible system, a variety of agents will cooperate to generate a solution (e.g., complete a part process plan) that reflects the current state of the system. Decision-making flexibility is also necessary to handle disruptions such as machine failures and fluctuations in demand; this will require agents with the capability of altering their behaviour subject to these disruptions.
The control system metrics identified by Brennan and Norrie (2003) fall into two categories: those that can be evaluated experimentally, and those that describe the structure of the control architecture. The experimental metrics reported in (Brennan and Norrie, 2003) are primarily based on message traffic in the control architecture (coupling, listening coefficient) and how likely individual agents are to change during operation (volatility). All of these metrics relate to the reliability and maintainability of the control system as it grows (i.e., its “availability”). The second class of control system metrics are intended to help define each individual architecture with greater precision and to provide a basis for comparison between each of the approaches. It is through the analysis of these parameters that the inherent characteristics of various control approaches can be evaluated. One structural parameter that is commonly used in the literature is planning horizon
Chirn and McFarlane (2005) identify control system performance metrics that are related to the control system software. The “strategic complexity” of the control system software is a measure of the
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complexity of the Petri nets describing the overall control strategy. Similarly, the “operational complexity” of the control system software is defined as the weighted lines of code. Both of these measures appear to relate to the requirement for “availability”: i.e., the software complexity will factor in when considering how reliable and maintainable the control system is. In addition to these metrics, they also measure the “reconfigurability” (i.e., “flexibility”) of the control software through its “extension rate” and “reuse rate”.
can begin on identifying appropriate target specifications. This work should tie in nicely with the work that has been done by the IMS Benchmarks and Performance Measures of Online Production Scheduling Systems (IMS, 2005) group. In particular, the identification of target specifications will be closely linked to the identification of common benchmarks. REFERENCES Aparicio IV, M. (1999) Internet-scale network intelligence, IEEE Internet Computing, September/October, pp. 38-40. Bandinelli, R. (2005) Final report on the benchmarking service, Network of Excellence on Intelligent Manufacturing Systems Report, IST2001-65001. Brennan, R.W. and D.H. Norrie (2003) Metrics for evaluating distributed manufacturing control systems, Computers in Industry, 51(2), pp. 225235. Bussmann, S. (1998) An Agent-Oriented Architecture for Holonic Manufacturing Control, In: Proceedings of the First International Workshop on IMS, Lausanne, Switzerland. Bussmann, S. and J. Sieverding (2001) Holonic control of an engine assembly plant: an industrial evaluation, In: Proceedings of the IEEE Systems, Man, and Cybernetics Conference, pp. 169-174, Tucson, USA. Cavalieri, S., Bongaerts, L., Macchi, M., Taisch, M., and Wyns, J. (1999) A benchmark framework for manufacturing control. In: Proceedings of the 2nd International Workshop on Intelligent Manufacturing Systems (H. Van Brussel, P. Valekenaers (Eds.)), pp. 225–236, Katholieke Universiteit Leuven, Leuven, Belgium. Cavalieri, S., M. Garetti, M. Macchi, and M. Taisch (2000) An experimental benchmarking of two multi-agent architectures for production scheduling and control, Computers in Industry, 43(2), pp. 139-152. Cavalieri, S., M. Macchi, S. Terzi (2002) Benchmarking manufacturing control systems: development issues for the performance measurement system, Proceedings of the IFIP Performance Measurement Workshop, Hannover, Germany. Chirn, J. and D.C. McFarlane (2005) Evaluating holonic manufacturing systems: a case study, In: Proceedings of the 16th IFAC World Conference, Prague, Czech Republic. Christensen, J.H. (1994) Holonic manufacturing systems: initial architecture and standards directions, In: Proceedings of the First European Conference on Holonic Manufacturing Systems, Hannover, Germany. Dabke, P. (1999) Enterprise integration via CORBAbased information agents, IEEE Internet Computing, September/October, pp. 49-57. Davis, W. and S.D. Thompson (1993) Production planning and control hierarchy using a generic controller, IIE Transactions, 25, pp. 26-45.
Finally, we return to the engine assembly line case study of Bussmann and Sieverding (2001). As noted previously, they assess the robustness of their holonic system by computing its productivity over a long period of time. In this case, productivity is defined as the “maximal throughput of the system under disturbances divided by the maximal throughput of the system if no disturbances occur at all” (Bussmann and Sieverding, 2001). The metrics identified in this section are summarised in Figure 3. In this figure, we attempt to map the holonic manufacturing system needs identified previously to the metrics described here. It should be noted however that the mapping shown in this figure does not imply that each metric is sufficient to verify that each need is satisfied. For example, scheduling criteria that are based on completion times provide input on determining if a system is flexible, but other metrics also need to be used (e.g., reconfigurability of the software). 4. CONCLUSIONS Clearly, more work is needed in order to establish target specifications for holonic manufacturing systems. It appears that there is general consensus on the primary needs or requirements of holonic manufacturing systems, however more work is required on identifying appropriate metrics for these systems. As noted previously, in most cases the metrics identified in Figure 3 are not sufficient for determining if a given requirement is satisfied. In some cases, a combination of metrics may come close (e.g., a combination of the metrics associated with “flexibility”), however each requirement should be investigated more closely relative to its associated metrics before any conclusions can be drawn. As can be seen in Figure 3, there is also a lack of metrics associated with “human integration”. These metrics tend to be of a qualitative nature (e.g., user friendliness, etc.), and as such, are less obvious in the existing literature. It should be stressed that Figure 3 is merely a starting point for this work that can be used as a basis for discussion. In particular, further work is required to identify the most appropriate metrics associated with each of the system requirements. At that point, work
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Villa, A. (1991) Hybrid Control Systems in Manufacturing, Gordon and Breach. Vollmann, T.E., W.L. Berry, D.C. Whybark (1992) Manufacturing Planning and Control Systems, 3rd Edition, Irwin. Wyns, J. (1999) Reference architecture for holonic manufacturing systems, Ph.D. Thesis, Katholieke Universiteit Leuven. Zubillaga-Elorza, F. and C.R. Allen (1999) Virtual instrument toolkit: rapid prototyping on the web, IEEE Internet Computing, September/October, pp. 41-48
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