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An Emergent Synthetic Approach to Supply Network
An Emergent Synthetic Approach to Supply Network 1
K. Ueda' ( I ) , J. Vaario2, T. Takeshital, I. Hatono' Department of Mechanical Engineering, Kobe University, Kobe, Japan 2 Nokia Research Center, Tokyo, Japan Received on January 6, 1999
Abstract To deal with complex supply networks a new approach based on emergent synthesis is proposed. The approach is demonstrated and studied under simulation. The simulation model consists of customer, dealer, producer, supplier, and product elements. The emergence of products and supply networks by the customers' preference can be observed as a global behavior resulting from the local interactions between these elements. The possibility to experiment with various management strategies by interactive functions of the simulator is also discussed. The user can carry out several scenarios and obselve whether the product succeeds on the market and how the supply networks change. Keywords: Manufacturing, Synthesis, Interactive system simulation
INTRODUCTION The change from mass-production to masscustomization brings about a growing importance of production networks [I], in particular, it increases the complexity of supply networks. Current companies consist of complex structures of manufacturing entities and sales channels. Usually the network is not owned by a single company, but is more a virtual network of several companies each concentrating on their special competence areas. There are several factors that should be considered: the customer behavior, the technical innovations, the marketing and pricing strategies, etc. However, it is not easy to find a management strategy that could cover complex supply networks by applying the usual deterministic methods. It is almost impossible to build a mathematical model covering all possible influencing factors. Also, building heuristic rules to cover all possible cases results in a complex rule base that is difficult to use in sottware-supportedmanagement. In this paper a new approach based on emergent synthesis is first proposed. Emergence plays a key role in solving difficult problems arising in synthesis which is indispensable in almost all domains of artifactual activities, from the planning phase up to post sales. Then, this paper describes how computer simulation incorporating the idea of the emergent synthesis becomes able to deal with supply networks. The simulation demonstrates the evolution of products and the emergence of supply networks driven by the customers' preference. Emerging supply networks are observed as a global behavior arising from the local interactions between agents such as customer, dealer, producer and supplier. The possibility to test various management strategies by interactive functions of the simulator is also discussed. 1
2
purpose
..lf f
Purpose
envirorirnent
environment
Figure 1: The problem of synthesis. causality of existing natural systems in such a field as physics. On the other hand, synthesis is indispensable for creating novel artifacts that satisfy requirements given by humans [2]. The latter type of problem can be called inverse problem. Generally, the inverse problem can be defined to determine the system's structure in order to express its function under the constraints of a certain environment, as shown in Figure 1. The main concern here is when and whether completeness of the information could be achieved in the description of the environment and in the specification of the purpose of the system. As shown schematically in Figure 2, the problem becomes more difficult as the system complexity and informational uncertainty increase. Complexity of System
.. .
.. ..
gncertainty of Information
Incomplete specificationproblem
EMERGENT SYNTHESIS
2.1 Problem of Synthesis Analysis is an effective methodology to clarify the
Annals of the ClRP Vol. 48/1/1999
Consumption Figure 2: Problem classification.
377
Distribution of a toward customer
.
% ,‘
*’,
’
: Producer
$b
Figure 3: The concept of emergence. With respect to incompleteness of information of the environment andlor the specification, the difficulties in synthesis can be classified into three classes. Class I: Complete problem The infomation on environment and the specification are fully given, then the problem is completely described, however, it is often difficult to optimize. Class 11: Incomplete environment problem The information on the environment is incomplete, while the specification is complete, then the problem is not completely described, and therefore it is difficult to cope with the dynamic properties of the unknown environment. Class Ill: Incomplete specification problem Both the environment description and the specification are incomplete, so that the problem solving starts with an ambiguous purpose. Accordingly, human interaction becomes significant. 2.2 Emergent Approach It is not easy to solve the above-mentioned problems by traditional, deterministic approaches based on topdown type principles such as Operational Research, Knowledge-based Engineering, etc. [3, 41. However, emergent approaches with both bottom-up and top-down features, such as evolutionary computation, selforganization, behavior-based methods, reinforcement learning, multi-agents, etc. offer efficient, robust and adaptive solutions to ‘these problems. The concept of emergence varies in such fields as biology, physics, philosophy, etc. For example, according to Cariani [5], -‘the pragmatic relevance of emergence is intimately related to Descartes Dictum: how can a designer build a device which outperforms the designer’s specifications? .... Thus, the problem of emergence is the problem of specification vs. creativity, of closure and replicability vs. open-endless and surprise.”
(D
*-: Product component flow f
: Product flow
As shown in Figure 3, the term -‘emergence” is used here in the following sense: a global order of structure expressing new function is formed through bi-directional dynamic processes where local interactions between elements reveal a global behavior. and the global behavior results in new constraints to the behavior of the elements. This definition suggests that implicit global complexity emerges from explicit local simplicity. The definition is basically similar to that in Artificial Life and Complex Adaptive System studies [6]. However, the purpose and environment are defined more definitely in this study, where emergence in artifactual systems is investigated. The Biological Manufacturing System (BMS) framework as proposed by the author [7] has invoked the concept of emergence. By generalizing the framework of BMS, an interdisciplinary project ”Methodology of Emergent Synthesis” [8] has been recently started in the Research for the Future Program in Japan. Previous works by the authors have shown the effectiveness of the emergent approaches in solving manufacturing problems. For Class I problem, for instance, a new kind of Genetic Algorithms with neutral mutation has been developed to solve Job-Shop Scheduling Problem with high performance [Q]. For Class I I problem, a EMS model [lo] with the idea of self-organization has effectively solved the dynamic reconfiguration problem of a shop floor level system: the system has adapted itself both to changes in product demands and to malfunctioning of manufacturing cells. For Class 111. an attempt, Interactive Manufacturing, has been proposed [ I l l . where humans such as designers, manufacturers and customers, and artifacts interact with each others, so that performance of each participant can be improved in an interactive manner.
Stochastic
4
: Supplier : Dealer :Customer
Figure 4: Producer, supplier. dealer and customer agents.
Material flow
propagati~n of information
Stochastic oroDaaation
Monetarv flow
Figure 5: Material and monetary flows between the interacting agents.
378
Advertiiement of Products toward customers by stochastic propagation of information
The supply network problem belongs to Class 111. Preliminary results [12, 131 related to the supply networks are promising that the emergent behavior could be modeled.
3
SUPPLY NETWORK SIMULATION
3.1 Modeling The model under consideration consists of customer, dealer, producer, and supplier agents as shown in Figure 4. The key feature in the method is the autonomous and independent activity of each agent. The interactions between the agents happen through the products in the direction from producer to customer, and through monetary feedback in the direction from customer to producer as shown in Figure 5. Each agent has a set of parameters defining its local behavior. By changing the parameters, the observer can explore various emergent behavior. The emergence could be observed both from the engineering and from economic points of view. This is visualized in Figure 6. The product. model consists of design information modifiable by rules that are getting updated by a mechanism that is similar to genetic algorithms. The products are made of components supplied by other producers. The selection of suppliers is based on a negotiation mechanism (contract network) between the producer and alternative suppliers. The decisions on each contract between the agents are made by evaluating the reservation price of the products for the customers, the product price, the technical properties. the fixed cost, the transportation cost, the commercial information propagation effect and the fashion effect. The commercial effects, the fashion effects and the transportation effects depend on the distance between the agents.
For example, producers attach the price to the new product by using their own cost function:
j=l
where 4 is the price of the product i , xii are the properties of the product i , and n is the number of properties. The reservation prices of the products for the customers are calculated taking the weighted values for each technical property value into consideration. The fashion effect is considered by introducing two types of customers, conformist and snob, where the former is
Engineering point of view\._
Back
Interactionbetween customers (fwhion, word of mouse) Figure 6: The emergence from the product (engineering) and from the market (economic) point of view.
Figure 7:The evolution of an imaginary product. following to neighbor customers' selection and the latter is taking the opposite manner [14].
3.2 Result 1: Evolution of Product The first example illustrates how the program is capable of evolving products based on the customers' selection. The customers' selection is effected by the price (compared to the reservation price), the fashion parameters, and the technical preferences. The product design is conducted at every 500th execution step by replacing two worst selling products with two new products designed by varying two best selling products. The straightforward idea is to create products that are likely to be most attractive to customers. The simulation results - evolution of an imaginary product and its lifecycle - are shown in Figure 7. As can be seen, two distinct product families are evolved in time along quite different trajectories. The fashion effect is accounted for in the simulation. However, the analysis of the fashion effect is omitted here due to the space limitation. 3.3 Result 2: Emergence of Supply Networks Figure 8 demonstrates the emergence of the production network. The simulation starts with the conditions of 800 customers, 12 dealers, 12 suppliers, 7 producers and 7 kinds of products. The number of producer is changed from 7 to 8 at the 1500th step to investigate the effect of introducing a new producer (denoted by number 8). Figure 8 (a) and (b) show two snapshots of the networks that emerged at the 1500th and 3500th steps, respectively. For the sake of clarity, in Figure (a) and (b), we did not plot all the customers, and the four producers with smallest share at the 1500th step were omitted too. Figure 8 (c) shows the change of the market share of all the producers in the step range from 1000 to 3500. Due to the participation of the new producer, the structure of the networks changes remarkably: some new networks emerge and some others disappear. The amount of material flow at (b) also largely differs from (a). The new producer rapidly increases its market share and wins finally (Figure (c)). The winning producer is characterized by providing relatively low cost products with having
379
Steps (c) Market share of each producer Figure 8: Snapshots of the simulation. After introducing a new producer at step 1500, new supply networks emerge and the market shares are rapidly changing. averaged technical properties and appropriate distances from both the suppliers and dealers. 4
DISCUSSION
By using an interactive simulator like the one shown here, the various participants of the production and product development processes become able to test several scenarios and observe their perspectives of success in the competition with other agents. The simulator, for instance, gives a tool to producers to better understand the change of market shares. The simulation handles a variety of factors with a large number of parameters and it would be easy to fix some of the parameters in order to achieve an anticipated situation on the market. However, the intended use of such a simulation environment is an interactive educational tool, where two or more groups of humans simultaneously modify the parameters in order to achieve higher market share. Currently, our simulation faces the problem of how the complexity of the real world can be incorporated into the model that has been set in motion by the simulator. However, in some limited cases, the customers, competitors, and supplierldealer networks are known exactly enough, so that the simulation is able to provide merits for strategic planning, for managing supply networks or for supporting product design. 5
CONCLUSION
In this paper, the potential of the emergent synthetic approach to deal with a specific kind of complex systems, i.e., with supply networks has been described, and the emergent modeling method has been presented. Especially, the coupled phenomena of emergence of products and their underlying supply networks have been demonstrated. The advantage of the presented method is in its holistic approach to the design process. The design task should be considered as the final target of the company: to provide products with good selling qualities. 6
ACKNOWLEDGMENTS
This study has been conducted in “Methodology of Emergent Synthesis” Project (JSPS-RFTF96P00702) supported by the Japan Society for the Promotion of Science. The authors also acknowledge to Dr. A. Markus, Hungarian Academy of Science for his useful comments.
380
7
REFERENCE
[ l ] Westkiimper, E.. 1997, Manufacturing on Demand in Production Networks, Annals of the CIRP, 4611: 329334. [2] Yoshikawa, H., 1993, Techno-Globe, Kogyochosakai Pub. Co.. Tokyo. [3] Monostori, L.. Markus, A., Van Brussel, H., Westkamper, E., 1996, Machine Learning Approaches to Manufacturing, Annals of the CIRP, 4512: 675-71 2. [4] Wiendahl, H.P., Scholtissek. P.. 1994, Management and Control of Complexity in Manufacturing, Annals of the CIRP, 1312: 433-540. [5] Cariani, P., 1992, Emergence and Artificial Life, Artificial Life II, Addison-Wesley: 775-797. [6] Langton. C.G., 1989, Artificial Life, Addison-Wesley. [7‘J Ueda, K., Vaario, J., 1997, The Biological Manufacturing System: Adaptation to Growing Complexity and Dynamics in Manufacturing Environment, Proc. of the 29th CIRP International Seminar on Manufacturing Systems, Osaka: 39-44. [8] Peklenik, J., 1998, Complexity in Manufacturing Systems, Proc. of Workshop on the Methodology of Emergent Synthesis, Kobe: 5-12. [9] Gohtoh, T., Ohkura, K.. Ueda, K., 1996, An Application of Genetic Algorithm with Neutral Mutations to Job Shop Scheduling Problems, Advances in Production Management Systems, IFIP, Chapman B Hall: 563-568. [IOIUeda, K., Vaario, J., Ohkura. K., 1997, Modeling of Biological Manufacturing Systems for Dynamic Configuration, Annals of the CIRP, 4611 : 343-346. [11]Ueda. K., Vaario. J., Fujii, N., 1998, Interactive Manufacturing: Human Aspects for Biological Manufacturing Systems, Annals of the CIRP, 47/1: 389-392. [12]Markus, A., Vanua, J., 1998, Product Line Development with Customer Interaction, Annals of the CIRP, 47/1: 361-364. [13]Vaario, J., Takeshita, T., Ueda, K., 1998, Emergence of Products by Consumers’ Selection, Proc. of JapanUSA Sym. on Flexible Automation, Otsu: 131-137. [14]Oda, S., Miura, K., Ueda, K.. 1997, The Application of Cellular Automata to Network Externalities in Consumer’s Theory, Artificial Life V. MIT Press: 101-107.
K. Ueda' ( I ) , J. Vaario2, T. Takeshital, I. Hatono' Department of Mechanical Engineering, Kobe University, Kobe, Japan 2 Nokia Research Center, Tokyo, Japan Received on January 6, 1999
Abstract To deal with complex supply networks a new approach based on emergent synthesis is proposed. The approach is demonstrated and studied under simulation. The simulation model consists of customer, dealer, producer, supplier, and product elements. The emergence of products and supply networks by the customers' preference can be observed as a global behavior resulting from the local interactions between these elements. The possibility to experiment with various management strategies by interactive functions of the simulator is also discussed. The user can carry out several scenarios and obselve whether the product succeeds on the market and how the supply networks change. Keywords: Manufacturing, Synthesis, Interactive system simulation
INTRODUCTION The change from mass-production to masscustomization brings about a growing importance of production networks [I], in particular, it increases the complexity of supply networks. Current companies consist of complex structures of manufacturing entities and sales channels. Usually the network is not owned by a single company, but is more a virtual network of several companies each concentrating on their special competence areas. There are several factors that should be considered: the customer behavior, the technical innovations, the marketing and pricing strategies, etc. However, it is not easy to find a management strategy that could cover complex supply networks by applying the usual deterministic methods. It is almost impossible to build a mathematical model covering all possible influencing factors. Also, building heuristic rules to cover all possible cases results in a complex rule base that is difficult to use in sottware-supportedmanagement. In this paper a new approach based on emergent synthesis is first proposed. Emergence plays a key role in solving difficult problems arising in synthesis which is indispensable in almost all domains of artifactual activities, from the planning phase up to post sales. Then, this paper describes how computer simulation incorporating the idea of the emergent synthesis becomes able to deal with supply networks. The simulation demonstrates the evolution of products and the emergence of supply networks driven by the customers' preference. Emerging supply networks are observed as a global behavior arising from the local interactions between agents such as customer, dealer, producer and supplier. The possibility to test various management strategies by interactive functions of the simulator is also discussed. 1
2
purpose
..lf f
Purpose
envirorirnent
environment
Figure 1: The problem of synthesis. causality of existing natural systems in such a field as physics. On the other hand, synthesis is indispensable for creating novel artifacts that satisfy requirements given by humans [2]. The latter type of problem can be called inverse problem. Generally, the inverse problem can be defined to determine the system's structure in order to express its function under the constraints of a certain environment, as shown in Figure 1. The main concern here is when and whether completeness of the information could be achieved in the description of the environment and in the specification of the purpose of the system. As shown schematically in Figure 2, the problem becomes more difficult as the system complexity and informational uncertainty increase. Complexity of System
.. .
.. ..
gncertainty of Information
Incomplete specificationproblem
EMERGENT SYNTHESIS
2.1 Problem of Synthesis Analysis is an effective methodology to clarify the
Annals of the ClRP Vol. 48/1/1999
Consumption Figure 2: Problem classification.
377
Distribution of a toward customer
.
% ,‘
*’,
’
: Producer
$b
Figure 3: The concept of emergence. With respect to incompleteness of information of the environment andlor the specification, the difficulties in synthesis can be classified into three classes. Class I: Complete problem The infomation on environment and the specification are fully given, then the problem is completely described, however, it is often difficult to optimize. Class 11: Incomplete environment problem The information on the environment is incomplete, while the specification is complete, then the problem is not completely described, and therefore it is difficult to cope with the dynamic properties of the unknown environment. Class Ill: Incomplete specification problem Both the environment description and the specification are incomplete, so that the problem solving starts with an ambiguous purpose. Accordingly, human interaction becomes significant. 2.2 Emergent Approach It is not easy to solve the above-mentioned problems by traditional, deterministic approaches based on topdown type principles such as Operational Research, Knowledge-based Engineering, etc. [3, 41. However, emergent approaches with both bottom-up and top-down features, such as evolutionary computation, selforganization, behavior-based methods, reinforcement learning, multi-agents, etc. offer efficient, robust and adaptive solutions to ‘these problems. The concept of emergence varies in such fields as biology, physics, philosophy, etc. For example, according to Cariani [5], -‘the pragmatic relevance of emergence is intimately related to Descartes Dictum: how can a designer build a device which outperforms the designer’s specifications? .... Thus, the problem of emergence is the problem of specification vs. creativity, of closure and replicability vs. open-endless and surprise.”
(D
*-: Product component flow f
: Product flow
As shown in Figure 3, the term -‘emergence” is used here in the following sense: a global order of structure expressing new function is formed through bi-directional dynamic processes where local interactions between elements reveal a global behavior. and the global behavior results in new constraints to the behavior of the elements. This definition suggests that implicit global complexity emerges from explicit local simplicity. The definition is basically similar to that in Artificial Life and Complex Adaptive System studies [6]. However, the purpose and environment are defined more definitely in this study, where emergence in artifactual systems is investigated. The Biological Manufacturing System (BMS) framework as proposed by the author [7] has invoked the concept of emergence. By generalizing the framework of BMS, an interdisciplinary project ”Methodology of Emergent Synthesis” [8] has been recently started in the Research for the Future Program in Japan. Previous works by the authors have shown the effectiveness of the emergent approaches in solving manufacturing problems. For Class I problem, for instance, a new kind of Genetic Algorithms with neutral mutation has been developed to solve Job-Shop Scheduling Problem with high performance [Q]. For Class I I problem, a EMS model [lo] with the idea of self-organization has effectively solved the dynamic reconfiguration problem of a shop floor level system: the system has adapted itself both to changes in product demands and to malfunctioning of manufacturing cells. For Class 111. an attempt, Interactive Manufacturing, has been proposed [ I l l . where humans such as designers, manufacturers and customers, and artifacts interact with each others, so that performance of each participant can be improved in an interactive manner.
Stochastic
4
: Supplier : Dealer :Customer
Figure 4: Producer, supplier. dealer and customer agents.
Material flow
propagati~n of information
Stochastic oroDaaation
Monetarv flow
Figure 5: Material and monetary flows between the interacting agents.
378
Advertiiement of Products toward customers by stochastic propagation of information
The supply network problem belongs to Class 111. Preliminary results [12, 131 related to the supply networks are promising that the emergent behavior could be modeled.
3
SUPPLY NETWORK SIMULATION
3.1 Modeling The model under consideration consists of customer, dealer, producer, and supplier agents as shown in Figure 4. The key feature in the method is the autonomous and independent activity of each agent. The interactions between the agents happen through the products in the direction from producer to customer, and through monetary feedback in the direction from customer to producer as shown in Figure 5. Each agent has a set of parameters defining its local behavior. By changing the parameters, the observer can explore various emergent behavior. The emergence could be observed both from the engineering and from economic points of view. This is visualized in Figure 6. The product. model consists of design information modifiable by rules that are getting updated by a mechanism that is similar to genetic algorithms. The products are made of components supplied by other producers. The selection of suppliers is based on a negotiation mechanism (contract network) between the producer and alternative suppliers. The decisions on each contract between the agents are made by evaluating the reservation price of the products for the customers, the product price, the technical properties. the fixed cost, the transportation cost, the commercial information propagation effect and the fashion effect. The commercial effects, the fashion effects and the transportation effects depend on the distance between the agents.
For example, producers attach the price to the new product by using their own cost function:
j=l
where 4 is the price of the product i , xii are the properties of the product i , and n is the number of properties. The reservation prices of the products for the customers are calculated taking the weighted values for each technical property value into consideration. The fashion effect is considered by introducing two types of customers, conformist and snob, where the former is
Engineering point of view\._
Back
Interactionbetween customers (fwhion, word of mouse) Figure 6: The emergence from the product (engineering) and from the market (economic) point of view.
Figure 7:The evolution of an imaginary product. following to neighbor customers' selection and the latter is taking the opposite manner [14].
3.2 Result 1: Evolution of Product The first example illustrates how the program is capable of evolving products based on the customers' selection. The customers' selection is effected by the price (compared to the reservation price), the fashion parameters, and the technical preferences. The product design is conducted at every 500th execution step by replacing two worst selling products with two new products designed by varying two best selling products. The straightforward idea is to create products that are likely to be most attractive to customers. The simulation results - evolution of an imaginary product and its lifecycle - are shown in Figure 7. As can be seen, two distinct product families are evolved in time along quite different trajectories. The fashion effect is accounted for in the simulation. However, the analysis of the fashion effect is omitted here due to the space limitation. 3.3 Result 2: Emergence of Supply Networks Figure 8 demonstrates the emergence of the production network. The simulation starts with the conditions of 800 customers, 12 dealers, 12 suppliers, 7 producers and 7 kinds of products. The number of producer is changed from 7 to 8 at the 1500th step to investigate the effect of introducing a new producer (denoted by number 8). Figure 8 (a) and (b) show two snapshots of the networks that emerged at the 1500th and 3500th steps, respectively. For the sake of clarity, in Figure (a) and (b), we did not plot all the customers, and the four producers with smallest share at the 1500th step were omitted too. Figure 8 (c) shows the change of the market share of all the producers in the step range from 1000 to 3500. Due to the participation of the new producer, the structure of the networks changes remarkably: some new networks emerge and some others disappear. The amount of material flow at (b) also largely differs from (a). The new producer rapidly increases its market share and wins finally (Figure (c)). The winning producer is characterized by providing relatively low cost products with having
379
Steps (c) Market share of each producer Figure 8: Snapshots of the simulation. After introducing a new producer at step 1500, new supply networks emerge and the market shares are rapidly changing. averaged technical properties and appropriate distances from both the suppliers and dealers. 4
DISCUSSION
By using an interactive simulator like the one shown here, the various participants of the production and product development processes become able to test several scenarios and observe their perspectives of success in the competition with other agents. The simulator, for instance, gives a tool to producers to better understand the change of market shares. The simulation handles a variety of factors with a large number of parameters and it would be easy to fix some of the parameters in order to achieve an anticipated situation on the market. However, the intended use of such a simulation environment is an interactive educational tool, where two or more groups of humans simultaneously modify the parameters in order to achieve higher market share. Currently, our simulation faces the problem of how the complexity of the real world can be incorporated into the model that has been set in motion by the simulator. However, in some limited cases, the customers, competitors, and supplierldealer networks are known exactly enough, so that the simulation is able to provide merits for strategic planning, for managing supply networks or for supporting product design. 5
CONCLUSION
In this paper, the potential of the emergent synthetic approach to deal with a specific kind of complex systems, i.e., with supply networks has been described, and the emergent modeling method has been presented. Especially, the coupled phenomena of emergence of products and their underlying supply networks have been demonstrated. The advantage of the presented method is in its holistic approach to the design process. The design task should be considered as the final target of the company: to provide products with good selling qualities. 6
ACKNOWLEDGMENTS
This study has been conducted in “Methodology of Emergent Synthesis” Project (JSPS-RFTF96P00702) supported by the Japan Society for the Promotion of Science. The authors also acknowledge to Dr. A. Markus, Hungarian Academy of Science for his useful comments.
380
7
REFERENCE
[ l ] Westkiimper, E.. 1997, Manufacturing on Demand in Production Networks, Annals of the CIRP, 4611: 329334. [2] Yoshikawa, H., 1993, Techno-Globe, Kogyochosakai Pub. Co.. Tokyo. [3] Monostori, L.. Markus, A., Van Brussel, H., Westkamper, E., 1996, Machine Learning Approaches to Manufacturing, Annals of the CIRP, 4512: 675-71 2. [4] Wiendahl, H.P., Scholtissek. P.. 1994, Management and Control of Complexity in Manufacturing, Annals of the CIRP, 1312: 433-540. [5] Cariani, P., 1992, Emergence and Artificial Life, Artificial Life II, Addison-Wesley: 775-797. [6] Langton. C.G., 1989, Artificial Life, Addison-Wesley. [7‘J Ueda, K., Vaario, J., 1997, The Biological Manufacturing System: Adaptation to Growing Complexity and Dynamics in Manufacturing Environment, Proc. of the 29th CIRP International Seminar on Manufacturing Systems, Osaka: 39-44. [8] Peklenik, J., 1998, Complexity in Manufacturing Systems, Proc. of Workshop on the Methodology of Emergent Synthesis, Kobe: 5-12. [9] Gohtoh, T., Ohkura, K.. Ueda, K., 1996, An Application of Genetic Algorithm with Neutral Mutations to Job Shop Scheduling Problems, Advances in Production Management Systems, IFIP, Chapman B Hall: 563-568. [IOIUeda, K., Vaario, J., Ohkura. K., 1997, Modeling of Biological Manufacturing Systems for Dynamic Configuration, Annals of the CIRP, 4611 : 343-346. [11]Ueda. K., Vaario. J., Fujii, N., 1998, Interactive Manufacturing: Human Aspects for Biological Manufacturing Systems, Annals of the CIRP, 47/1: 389-392. [12]Markus, A., Vanua, J., 1998, Product Line Development with Customer Interaction, Annals of the CIRP, 47/1: 361-364. [13]Vaario, J., Takeshita, T., Ueda, K., 1998, Emergence of Products by Consumers’ Selection, Proc. of JapanUSA Sym. on Flexible Automation, Otsu: 131-137. [14]Oda, S., Miura, K., Ueda, K.. 1997, The Application of Cellular Automata to Network Externalities in Consumer’s Theory, Artificial Life V. MIT Press: 101-107.