Tel.: +81-46-291-3194, fax: +81-46-242-8490, E-mail address: sudo@ic.kanagawa-it.ac.jp Abstract This study experiments with the manufacturing efficiency by layout change of a factory b
Trang 1Procedia CIRP 7 ( 2013 ) 437 – 442
2212-8271 © 2013 The Authors Published by Elsevier B.V.
Selection and peer-review under responsibility of Professor Pedro Filipe do Carmo Cunha
doi: 10.1016/j.procir.2013.06.012
Forty Sixth CIRP Conference on Manufacturing Systems 2013 Agent based manufacturing simulation for efficient assembly operations
a Kanagawa Institute of Technology, 1030 Shimo-Ogino, Atsugi , Kanagawa, 243-0292 Japan
* Corresponding author Tel.: +81-46-291-3194, fax: +81-46-242-8490, E-mail address: sudo@ic.kanagawa-it.ac.jp
Abstract
This study experiments with the manufacturing efficiency by layout change of a factory by means of agent-based autonomous production scheduling, using the virtual factory on a multi-agent simulation system As infrastructure software for agent based simulation, the artisoc(c) is used In this virtual factory, three types of agents are equipped Users can alter a configuration such as input new jobs, adjusting a machine setting, etc, with monitoring conditions of agents As a result, by adjustment of the agent's behavior with shop floor detail, the assembly schedule becomes more effective The experiment is carried out to show that local negotiations contribute to global optimization when resources in the factory are effectively distributed and shared In this paper, the effectiveness of job-list cleanup method is shown In addition, the scheduling influence is simulated by the communication range of agents A part agent chooses a machine, by the length of a job list and the conveyance cost But the communication cost between agents increases with the size of the communication range From experimental results, when extending the communication range simply, the conclusion is reached that optimization did not necessarily result in progress
© 2013 The Authors Published by Elsevier B.V
Selection and/or peer-review under responsibility of Professor Pedro Filipe do Carmo Cunha
Keywords: Virtual factory; Dynamic scheduling; Multi agents; Concurrent engineering;
1 Introduction
Recently a manufacturing system is shifting into mass
production, a high-mix very low volume production and
flexible order-made production to respond to customer
needs With this conversion production planning
becomes more complicated, and researches on
production scheduling have taken a technological
turnaround
Because the solution space is too large, the
mathematical programming based on job-shop
scheduling is powerless to obtain the optimal solution
To build a practical production plan adopting suboptimal
solutions is essential, using a parallel processing and
problem reductions With such background, autonomous
manufacturing systems using multi-agents have been
proposed [1-2] In such autonomous manufacturing
system, a production plan is generated autonomously
and dynamically, using communication and negotiation
between agents that correspond to factory components
As a result, the system has flexibility and continuous activeness Even when an emergent trouble occurred, the necessity for a fresh start had been reduced Each agent's own action is determined by reference to simple rules and local negotiation, the assembly operation progresses autonomously [3-4] This agent based system adjusts the production schedule dynamically using only local negotiation when conditions have to be changed The most important feature of this architecture is, there is no manager that controls the factory as a representative Previously, authors proposed a method that is using agents for decentralized autonomous control This autonomous assembly type system consists of the following: assembly machine agents, product agents and parts agents [5-8] In addition, it has also discussed the possibility of implementation as a multi-agent system [9]
In this paper, the experimental results that the effectiveness of changing in behavior and parameters of the agent are shown, using the virtual factory built on a
© 2013 The Authors Published by Elsevier B.V.
Selection and peer-review under responsibility of Professor Pedro Filipe do Carmo Cunha
Trang 2multi-agent simulator artisoc(c) [10] The first
experiment is about the effectiveness of the job-list
cleanup method Next is the changing size of the
communication range of agents As a result, it was found
that both parameters had some influence on production
planning
2 Agent based autonomous assembly system
In the autonomous manufacturing system, a
production plan is generated autonomously and
dynamically, using communication and negotiation
between agents that correspond to factory components
2.1 Structure of the autonomous assembly system
The structure of a traditional manufacturing system is
a device oriented structure There are several attempts to
construct such kind of autonomous decentralized
manufacturing system One example is the holonic
manufacturing system [11-13] The elements of the
system act autonomously As a result, they organize the
system cooperatively Usually some manager
functionality is installed as an agent or blackboard If the
manufacturing system doesn’t need the manager
function that manages the entire system by controlling
each agent, it becomes a more flexible system This
means that the system should be constructed as an event
driven type system As a solution for the above
mentioned requirements, a configuration of a work-piece
agent and a machine tool agent for an autonomous
machining system has been introduced [4-5] In such
manufacturing systems, a manufacturing activity unit
such as a machine tool, assembly machine, robot, AGV
(Automatic Guided Vehicle), and manufacturing cell has
autonomous functionalities that are configured as agents
In these cases, the system structure was a device driven
system structure (Fig 1)
Moreover about the implementability of such a
software agent have been also discussed A part is just a
part, it is impossible to install an agent into parts To
install an agent’s capability into pallets (trays) which are
used in transporting parts is proposed [9] Parts are
transferred to an assembly machine by AGV or a kind of
conveyor belt In this case, the container is used in
general if packaging is considered This pallet is
reusable to install agents; it contributes to the realization
of the agent based autonomous assembly system
2.2 Characteristics of agent based system
The system consists of a distributed structure, it has
flexibility and is active continuously Even when an
emergent trouble occurs, the necessity for a fresh start
had been reduced Each agent's own action is determined
Shop Floor
New Product Order
Product’s Specification
Control Yard
Product Agents
Assembly Machine Agents
Work’s Specification Work’s Specification
Work’s Specification Work’s Specification
Work’s Specification
Work’s Specification
Assembly Request Assembly Request Assembly Request
Assembly Request Assembly Request
•Product model
•Production plan
•Arrow diagram
Shop Floor
New Product Order
Product’s Specification
Control Yard
Product Agents
Assembly Machine Agents
Work’s Specification Work’s Specification
Work’s Specification Work’s Specification
Work’s Specification
Work’s Specification
Assembly Request Assembly Request Assembly Request
Assembly Request Assembly Request
•Product model
•Production plan
•Arrow diagram
Fig 1 Outline of an agent based assembly system
by reference to simple rules and local negotiation Then, the assembly operation progresses autonomously Accordingly, transformation of scheduling results from agent's behavior and factory parameter had been further explored [14-16] In such an agent based system, since there are too many indefinite elements, in order to predict the behaviour of a factory line, the computer simulation is indispensable In recent years, concurrent engineering with a digital virtual factory has attracted attention These simulation systems are aimed at cost saving, shortening development time, improved quality etc This means that imperfections, troubles and problems can be found by using pre-manufacturing computer simulation
3 Agents in the autonomous assembly system
The established autonomous assembly system is structured of three kinds of agents [5-9] The required functional capabilities of each agent are as follows:
3.1 The product agent
The product agent generates the assembly work process from the product model Based on this work process, product agents put parts agents onto the shop floor After that, product agents watch delays of operations, and check a change of the deadline and quantity When there
is a need for a rescheduling plan, the product agent notifies parts agents of the related information If a new
Trang 3machine problem arises, the trouble information is sent
to parts agents, they will then re-select another assembly
machine If each parts agent does work greedily, it is not
an efficient assembly production line Each parts agent
must know its own priority for the assembly job The
priority is derived from an arrow-diagram obtained by
the assembly process model The product agent’s
capabilities are as follows:
Generation of the assembly process plan
Generation of the parts agent
Communicating ability with parts agents
Management of the arrow diagram using feedback of
the working results
3.2 The parts agent
Parts agents correspond to each assembly work They
have the assembly process model handed by the product
agent, and select the assembly machine based on the
estimated result The scheduling optimization process is
generated by negotiations between parts agents,
asynchronous distributed in real-time When a priority
change of a deadline is notified by the product agent, the
parts agent redoes the selection of the assembly machine
according to the contents Parts agents must have
abilities as follows:
Communication capability with other agents
Computation for the operations completion
Request the estimate to an assembly machine
Request of conveyance
3.3 The assembly machine agent
The assembly machine agent has its own machine
model that is described by specifications and capabilities
of the assembly machine The machine agent manages
its own operation schedule using this machine model
Moreover, the machine agent checks itself and notifies
the assembly machine’s condition to other agent
Machine agents must have abilities as follows:
The management function of a work list
Correspondence to work estimated request
(Calculation of the completion time of work)
The notice function of work schedule delay
Arrangements of attachment parts and pickup
4 Construction of the virtual factory
The above mentioned agent based manufacturing
system does not have advantages over other
manufacturing systems with respect to every factor
However, the proposed system has superior performance
under a particular set of conditions The virtual
production plant built on a computer is called a virtual
factory [17-18] It can be used for probing the problem
Parts Agent #1
Machine Agent a1 Parts
Agent #2 Parts Agent #3
Assembly Simulation System
Simulation Manager
Simulation Monitor
Machine Agent a2
Machine Agent b1
Virtual Factory
Machine Data
Production Plan Data
Parts Agent Generator
Shop Floor Configurator
Product Data
XML
XML
Operator
Parts Agent #1
Machine Agent a1 Parts
Agent #2 Parts Agent #3
Assembly Simulation System
Simulation Manager
Simulation Monitor
Machine Agent a2
Machine Agent b1
Virtual Factory
Machine Data
Production Plan Data
Parts Agent Generator
Shop Floor Configurator
Product Data
XML
XML
Operator
Fig 2 The structure of the assembly simulation system [14]
of the manufacturing line in operation, or attaining an increase in efficiency when newly designing a factory Moreover, it is possible to perform a simulation when parts of the plant stop working due to an accident or power failure
On the other hand, in the autonomous manufacturing system that is using a software agent, cooperation with computer systems is needed Moreover a lot of agents are autonomous-decentralized and actively act, thus, a prior simulation is indispensable As a consequence, development of the virtual factory as a verification system [14-16] has also been performed simultaneously with constructing an autonomous manufacturing system Fig 2 shows the structure of the assembly simulation system This simulation system is structured on the virtual assembly factory The operator can set up the initial shop floor configuration through the user interface The simulation manager generates parts agents from product models, and generates machine agents from machine models Product models and machine models are input as XML description files The simulation monitor shows progress status and condition of assembly processes from the product view and assembly machine view This assembly simulation system is prototyped
using the multi-agent simulator called artisoc(c) [10] as
a development environment Fig 3 shows simulation displays In the virtual assembly factory, each type of agent is implemented with the following key features are shown in Fig 3:
(A) Preview window: the agent's position can be dynamically checked by movement of icons
(B) Machine agent viewer: Supervise the situation of each assembly machine (job lists, developed power, mechanical condition, etc.)
(C) Work-advance graph: checking the completion rate of products
(D) The console window for debugging
Trang 4Fig 3 Prototyping of an assembly simulation system
(E) Control Panel (production request etc.)
(F) The work log of assembly machines (with a file
output function)
The input of a programming level is needed for
performing a detailed setup and control It is possible to
copy the position and conveyance course of an operating
machine with the layout of a real factory And the user is
able to change and adjust a parameter in real time and
visually In recent years, the design of a dynamic factory
or production control which used such a system is
attracting attention, it is called concurrent engineering
5 Improvement of productivity
In the agent based assembly system model presented,
after a part agent selects the assembly machine, it does
not negotiate for a change of the order to other parts
agents Here, it is excluded in case that product agent
announces a change in the time for delivery The
following method is newly proposed for improving
productivity
5.1 The job list clean-up method
A machine agent locks the order of the job List when
a real job is imminent Then the parts agent starts
preparation of assemblies, such as supply of
sub-assembled parts A machine agent transposes the
sequence of operations on a small scale only within the
case where the influence is sufficiently small at the time
Job List
Approach (Order Locked)
1.
2.
3.
4.
Assembly Machine 1 Checking work type2 Searching for a similar job
3 Notification of the delay
4 Interruption
5 Preparation of assembled parts 5.
Job List
Approach (Order Locked)
1.
2.
3.
4.
Assembly Machine 1 Checking work type2 Searching for a similar job
3 Notification of the delay
4 Interruption
5 Preparation of assembled parts 5.
Fig 4 The job list clean-up method
Shop Floor Human Worker
Screwing Machine Bonding Machine
Bonding
Screwing Machine
Shop Floor Human Worker
Screwing Machine Bonding Machine
Shop Floor Human Worker
Screwing Machine Bonding Machine
Bonding
Screwing Machine
Fig 5 The layout of assembly machines and human workers (1)
for delivery, just before approaching a limit In other words, a machine agent tries to reduce the time for initial set-up and tool change by continuously processing similar type jobs In this regard, a check is not carried out to the tail end of a list, it is limited to near the approach line (Fig 4)
5.2 Verification of the job-list cleanup method
The effectiveness of the job-list cleanup method is shown using a virtual factory demonstration Fig 5 shows the layout and number of assembly machines Table 1 shows parameters of manufactured products from the simulations Two types of products were assembled Straight type mobile phones which have two assembly sequences and the flip type mobile phones which have twelve sequences On the shop floor, there is one bonding machine and one screwing machine and 6 human workers Human workers can do both of the operations, but it requires time for changing the tools used
The bar graph in Fig 6 shows the relations between the number of steps to the completion of the work and
(B)
(A) (F)
(A)
(E) (D)
(B)
(A) (F)
(C)
(E) (D)
Trang 5Table 1 Data of assembled products used in simulations
Category of Products Straight type phone Flip-type phone
Assembly Sequences 1 bonding and
1 screwing
9 bonding and
3 screwing
Amount of Assembled Products [lot]
Amount of Assembled Products [lot]
Fig 6 The relations between amount of assembled products and
number of transpose incidences
the amount of assembled products The latter number is
proportional to the length of the waiting job-list for a
machine The line chart in Fig 6 shows the frequency of
transpose incidences The clean-up method does not run
when assembly machines (including human-workers)
have few jobs On the other hand, when the work list of
a machine agent becomes long it may be associated with
a high probability of an existence of the same kind of
work in the job-list, consequently the number of times of
calling the clean-up method increases As a result, the
length of time required to finish decreases up to 4%
compared to the case without the clean-up method This
means reduced time for the exchange of tools when
human-workers take up the next work This method is an
effective thing especially when employing a
general-purpose machine in which various types of assembly
processing could be utilized
6 Influence of changing communication range
Usually, the same configuration of machines is
applied to various products in small-lot production
Therefore the efficiency depends on placements of
resources A part agent chooses a machine, by the length
of a job list and the conveyance cost to get the parts
there But the communication cost between agents
increases with the size of the communication range (Fig
7) Therefore cost reduction will be also possible The
desired extent of the communication range can also be
tested
Shop Floor
Parts Agent Parts Agent
Smaller Scope
Parts Agent Parts Agent
Smaller Scope Larger Scope
Fig 7 The size of the agent’s communication range
Bonding Machine
Human Worker
Screwing Machine
Bonding Machine
Human Worker
Screwing Machine
Fig 8 The layout of assembly machines and human workers (2)
20 lots of Straight type phones and 30 lots of flip type phones are put onto the assembly line The parameters of productions are the same as in Table 1 Each of the assembly machines are laid out as indicated in fig 8 Simulation results are shown in Fig 9, where the viewing ranges of parts agents were set at 20%, 30%, 40%, 50%, 60%, 70% and 80% of the diagonal diameter
of the shop floor The vertical axis means the number of steps to the completion of the work, and total distance of parts agents moved When the communication range is narrow, the dispersion efficiency of work is bad because requests for work may concentrate on a nearby machine
On the other hand, if the communication range is extended, the length of movement tends to increase since
a vacant machine located at a further distance may be chosen With the factory composition of this experiment, while parts agents are acting in about 40% to 50% of the range, the operation step has been relatively small From these results, it is necessary to provide a suitable parameter according to the content to optimize the situation
Trang 6Steps Distance
Ratio of Agent’s Communication Ranges
to Floor Size [%]
Ratio of Agent’s Communication Ranges
to Floor Size [%]
Fig 9 The simulation results on changing agent’s viewing scopes
7 Conclusions
The first experimental result showed the effectiveness
of the job list clean-up method The next one showed the
relationship between agents’ viewing scopes and
conveyance costs From the second experimental result,
when simply extending the communication range, the
conclusion that optimization did not necessarily progress
was obtained In the case of such very large scale
manufacturing, the autonomous decentralized assembly
system may have advantages One item for future work
is designing an agent's algorithm according to various
purposes, such as not only shortening manufacturing
time but also reducing energy consumption
Acknowledgements
The authors are grateful to Dr Udo Graefe, retired
from the National Research Council of Canada for his
helpful assistance with the writing of this paper in
English
References
[1] Monostori, L., Váncza, J., Kumara, S., 2006, “Agent-based systems
for manufacturing”, CIRP Annals-Manufacturing Technology, vol
55, no 2, p 697
[2] Fujii, N., Kobayashi, M., Makita, T., Hatono, I and Ueda, K.,
2004,”Integration of Facility Planning and Layout Planning Using
Self-Organization in Semiconductor Manufacturing”, Proceedings
of the 37th CIRP-ISMS, p 175
[3] Matsuda, M., Ishikawa, Y and Utsumi, S., 2006,”Configuration of
Machine Tool Agents for Flexible Manufacturing”, Proceedings of
the 39th International Conference on Manufacturing Systems,
p 351
[4] Matsuda, M., and Sakao, N., 2008,”Configuration of An Autonomous Decentralized Digital Factory Using Product and Machine Agents”, Innovation in Manufacturing Networks, IFIP vol.266, p 215
[5] Sakao, N., Sudo, Y and Matsuda, M., 2008,”Product and Machine Agents for an Autonomous Assembly Production System”, Proceedings of Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems, p 1271
[6] Sakao, N., Matsuda M and Sudo, Y., 2009,”Assembly planning for
an autonomous decentralized manufacturing system led by a product part agent”, Proceedings of 42nd CIRP International Seminar on Manufacturing Systems (CD-ROM)
[7] Sudo, Y., Sakao, N and Matsuda, M., 2010,”An Agent Behavior Technique in an Autonomous Decentralized Manufacturing System”, Journal of Advanced Mechanical Design Systems and Manufacturing Vol 4, No 3, p 673
[8] Matsuda, M., Sakao, N., Sudo, Y and Kashiwase, K., 2010,”Flexible and Autonomous Production Planning Directed by Product Agents”, Proceedings of the 43rd CIRP International Conference on Manufacturing Systems, p 876
[9] Sudo, Y., Kashiwase, K and Matsuda, M., 2011, “The Implementability of Agent Based Autonomous Decentralized Assembly System”, Proceedings of International Symposium on Scheduling 2011, p 101
[10] Kozo Keikaku Engineering Inc., Artisoc User Manual English Edition, http://mas.kke.co.jp/modules/tinyd0/index.php?id=9 [11] Brussel, H V., Wyns, J., Valckenaers, P., Bongaerts, L and Peeters, P., 1998,”Reference architecture for holonic manufacturing systems: Prosa,” Computers in Industry, vol 37, no 1, p 255 [12] McFarlane, D.C and Bussman, S., 2000,”Developments in Holonic Production Planning and Control”, Production Planning and Control, vol 11, no 6, p 522
[13] Sugimura, N., Shrestha, R and Inoue, J., 2003, ”Integrated process planning and scheduling in holonic manufacturing systems -Optimization based on shop time and ma-chining cost”, Proceedings of the 2003 IEEE Interna-tional symposium on Assembly and task planning (ISATP2003), p 36
[14] Matsuda, M., Kashiwase, K and Sudo, Y., 2011, “Configuration Of
A Digital Factory For Autonomous Virtual Manufacturing”, Proceedings of 21st International Conference on Production Research, -Innovation in Product and Production (CD-ROM) [15] Matsuda, M., Kashiwase, K and Sudo, Y., 2012,”Agent oriented construction of a digital factory for validation of a production scenario”, Procedia CIRP (Special Issue on) 45th Conference on Manufacturing Systems, Elsevier, vol.3, p.115
[16] Sudo, Y., Kasiwase, K and Matsuda, M., 2012,”Verification of scheduling efficiency of an autonomous assembly system using the multi-agent manufacturing simulator”, Proceedings of the ASME
2012 International Symposium on Flexible Automation
[17] Bley, H., Franke, C., 2004, Integration of product design and assembly planning in the digital Factory, CIRP Annals - Manufacturing Technology, Vol 53, Issue 1, pp 25-30
[18] Butterfield, J., Crosby, S., Curran, R., Price, M., Armstrong, C G and Raghunathan, S., 2007,”Optimization of Aircraft Fuselage Assembly Process Using Digital Manufacturing”, Journal of Computing and Information Science in Engineering, Vol 7, No 3,
p 269