manufacturing enterprise collaboration based on a goal-oriented fuzzy trust evaluation model in a...

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Computers & Industrial Engineering 56 (2009) 888–901

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Manufacturing enterprise collaboration based on agoal-oriented fuzzy trust evaluation model in a virtual enterprise

Jungtae Mun a, Moonsoo Shin a, Kyunghuy Lee b, Mooyoung Jung c,*

a Department of Industrial and Management Engineering, POSTECH, Pohang 790-784, Republic of Koreab Department of IT Business Management Engineering, Daejeon University, Daejeon 300-716, Republic of Korea

c School of Technology Management, UNIST, BanYeon-Ri 194, Ulsan 689-805, Republic of Korea

Available online 20 September 2008

Abstract

To cope with the rapidly changing manufacturing environment, enterprise collaboration is getting increasingly moreattention than ever before. The virtual enterprise (VE) is a concept that supports temporary alliances of manufacturingenterprises that have various collaboration models, such as extended enterprise, networked enterprise, concurrent enter-prise, etc. Selection of trustworthy partners and trust building are important in virtual domains because they have largelybeen affecting the success of a VE. However, because of its complexity of trust, trust models in the literature are limited intheir ability to cope with dynamic and virtual environment. In this paper, we propose a trust evaluation method of sup-porting enterprise collaboration and maximizing the satisfaction of cooperation. In this context, trust means the goalachievement probability. Trust value of an enterprise can be obtained by a fuzzy inference system whose rule-base is basedon the top-level goal of a VE. According to the selector’s preference, various rules can be applied to trust evaluation. Forfurther study, the planning and scheduling problems should be considered along with the trust-based partner selection forcollaboration among manufacturing enterprises.� 2008 Elsevier Ltd. All rights reserved.

Keywords: Breeding environment; Fractal enterprise; Virtual enterprise; Trust evaluation; Fuzzy inference system

1. Introduction

Manufacturing enterprises need to adapt to the rapidly changing environment that reflects the customers’demands, unpredicted situations, incessant evolution of software and hardware, and advances in infrastruc-tures. To cope with the turbulent and unpredictable environment, many flexible organizational models andenterprise network models have been proposed. The new models reflect the current business environment inwhich competitiveness is no longer between enterprises, but between enterprise networks (e.g., supply chains,innovation clusters) because individual enterprises often do not have all the necessary skills and competencies

0360-8352/$ - see front matter � 2008 Elsevier Ltd. All rights reserved.doi:10.1016/j.cie.2008.09.022

* Corresponding author. Tel.: +82 52 708 7012; fax: +82 52 708 7010.E-mail addresses: [email protected] (J. Mun), [email protected] (M. Shin), [email protected] (K. Lee), [email protected]

(M. Jung).

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J. Mun et al. / Computers & Industrial Engineering 56 (2009) 888–901 889

to satisfy the new market requirements (Park & Favrel, 1999; Putnik, Cunha, Sousa, & Avila, 2005; Schultze& Orlikowski, 2001).

By adopting the idea of highly flexible organizations and by reconfiguring themselves to cope with the needsand opportunities of the business environment, enterprises have been able to obtain a number of benefits suchas agility, complementary roles, operational dimensions, competitiveness, resource optimization, and innova-tion (Camarinha-Matos & Afsarmanesh, 2003). Although the conceptual advantages of virtual enterprise arewell known, it has not been so easy to find practical applications of virtual enterprise in the real field, exceptfor more stable, long-term networks. In the literature, many researchers have pointed out that there are somebarriers, as well as challenges such as the lack of common reference models (Putnik et al., 2005), the lack oftrust and competitive nature (Smyth, 2003), and the difficulty of preparing the necessary conditions for start-ing a VE or a VO creation (Camarinha-Matos & Afsarmanesh, 2007). Among these barriers, partner selectionand the related trust issues such as trust evaluation, mutual trust, and trust building have largely been affectingthe success of a VE (Lavra�c et al., 2007; Norman et al., 2004; Schmidt, Steele, Dillon, & Chang, 2007; Sreenath& Singh, 2004). A wide of variety of literature now exists on trust, ranging from specific applications to generalmodels. However, because of the complexity and ambiguity of trust, trust models have focused on qualitativemeasurement.

In this paper, a fuzzy trust evaluation method useful in selecting good partners is proposed with exemplaryscenarios. The remainder of this paper is organized as follows. In Section 2, we briefly introduce the concept oftrust, fractal organization, a fractal-based virtual enterprise model. In Section 3, a trust model is proposedwith the goal model, the trust evaluation of a single enterprise, and the evaluation procedure. A simple exam-ple is presented in Section 4 which shows how to apply the proposed trust model to partner selection. Finally,Section 5 concludes the paper and presents further research plans.

2. Related works

2.1. Trust evaluation model

Trust is an abstract concept in many kinds of interactions, allowing people or organizations to act underuncertainty with the risk of negative consequences. Because of a wide variety of definitions of trust, we offerthe following definition of trust to give the reader a reference point (Olmedilla, Rana, Matthews, & Nejdl,2005):

‘‘Trust of a party A to a party B for a service X is the measurable belief of A in that B behaves depend-ably for a specified period within a specified context (in relation to service X).”

In a virtual environment, trust is an important factor which enables enterprise collaboration. Recentresearch has shown that mutual trust can contribute to knowledge sharing, resource sharing, and taking jointrisks (Lavra�c et al., 2007). Therefore, many research efforts have focused on how to evaluate the trust value ofan enterprise when it is considered as the potential partner of a virtual enterprise. In the multi-agent system,trust models can be classified into three categories (Sarvapali, Ramchurn, & Jennings, 2004): (1) learning-based models using game theory, (2) reputation-based models using past collaboration performance, (3) socio-cognitive-based models assessing the outcome of interactions between agents. Also, agent-based trust modelshave been widely researched in the web-service area. Trust value is used when an agent selects a service becausethe agent has neither enough information about the service provider nor collaboration experiences. Socialinformation filtering (the most well-known is collaborative filtering) (Sarwar, Karypis, Konstan, & Riedl,2000), a reputation system (Sabater & Sierra, 2002; Zacharia & Maes, 2000; Zarcharia, Moukas, & Maes,1999), and a referral system (Singh, Yu, & Venkatraman, 2001) have been proposed as trust models usingagents in the web-service area. Those approaches are useful in evaluating an agent or a small company; how-ever, in large enterprises or VEs, in which complex organizations are interconnected, it is not easy to apply thetrust models in the multi-agent systems to virtual enterprise.

Trust models in VE inherit the concept of trust models and extend the models to fit in the enterprise level.There are mainly two inputs when evaluating the trust value of an enterprise (Lavra�c et al., 2007; Schmidt etal., 2007; Sreenath & Singh, 2004). They are (1) enterprise information including enterprise capability as well

890 J. Mun et al. / Computers & Industrial Engineering 56 (2009) 888–901

as its collaboration results and (2) enterprise reputation including peer evaluation and the propagated trustand credibility of an enterprise from the network. In this paper, we use these two inputs in evaluating the trustvalue of an enterprise, which are included in the top-level goals of a VO/VE. The difference between the trustmodel proposed by us and other models is that in our model, we consider the goal of a newly created VO/VEand use the goal in evaluating the trust of other enterprises.

2.2. Fractal organization

Since manufacturing systems have been fixed in on a centralized or hierarchical control model, the existingmanufacturing systems are experiencing difficulties in meeting the needs to adapt to the dynamically changingenvironment because of their static control structure. Thus, a distributed manufacturing paradigm that decen-tralizes authority and responsibility for system control has been considered an indispensable choice of thefuture manufacturing system. Many researchers have proposed new manufacturing philosophies based on dis-tributed manufacturing paradigms such as the bionic manufacturing system (Okino, 1993; Ueda, 1992) andthe holonic manufacturing system (Brussel, Wyns, Valckenaers, Bongaerts, & Peeters, 1998). As a principalnext-generation manufacturing system along with the HMS and the BMS (Tharumarajah, Wells, & Nemes,1996), the fractal manufacturing system (FrMS, Ryu & Jung, 2003) is also based on the distributed paradigm.

Fractal is represented as a recursive pattern, which has the characteristics of self-similarity with fractionaldimension. It has been widely applied to describe and model various spatial phenomena, urban morphologies,transportation networks, and complex structures. The fractal concept is embodied in a fractal factory, whosecharacteristics are self-similarity, self-organization, and goal-orientation (Warnecke, 1993). FrMS is a novelorganizational paradigm for distributed manufacturing systems, evolving out of the principles of fractalfactory and the BFU model (Tirpak, Daniel, LaLonde, & Davis, 1992). FrMS is ‘a flexible, fault-tolerant,and self-reconfigurable manufacturing system developed and operated under the fractal architecture’ (Ryu& Jung, 2003). We offer the following definitions in order to give the reader a reference point (Shin & Jung,2007).

� Fractal is a corporate entity embodied in a self-similar pattern.

� Fractal organization is an organization based on fractal architecture, in which a self-similar pattern is recur-

sively defined. That is, it is a system of fractals.

2.2.1. Self-similarity

Self-similarity is the basic feature of a fractal where the nested parts of a system are shaped into the samepattern as the whole. A complex organization can be generated easily based on the nature of fractal structures,which can change directions quickly. In a business in which self-similarity of values and beliefs has emerged atany level or any geographic area, effective organizations can be assembled very quickly to take advantage ofsudden opportunities or handle unexpected disturbances (Kelly & Allison, 1999). Self-similarity refers not onlyto the structural characteristics of organizational design, but circumscribes the manner of performing a job, aswell as the formulation and pursuit of goals (Warnecke, 1993).

2.2.2. Self-organization

The term self-organization is typically used to denote the feature of a system determining its characteristicsby itself, which is achieved by cooperation of autonomous entities. In the context of fractal-based systems,self-organization is realized by self-reconfiguration in the life-cycle of a fractal entity. Self-reconfiguration isa more specific concept, which is used to configure or reconfigure the structure of a system as well as the struc-ture of an enterprise network. The constituents of a fractal organization try to achieve their assigned goals andthe system level goals simultaneously through cooperation. To achieve their goals, they construct an effectiveand efficient control structure, referred to as fractal structure, and then distribute tasks. To optimize the struc-ture and respond quickly to dynamic changes, the constituents change their structure through the dynamicorganizing process.


2.2.3. Goal-orientation

A fractal organization is operated under goal-oriented working mechanisms. All constituents have individ-ual goals, and they try to achieve their goals in their life-cycle. The constituents generate their own goals andmodify the goals in order to harmonize their collaborative networks by resolving the conflicts between entitiesand integrating them.

2.3. Fractal-based virtual enterprise model

Fractal-based virtual enterprise model (FrVE) is a framework for virtual enterprises and for the fractal-based breeding environment that is operated under the fractal-based network architecture. In FrVE, enter-prises can collaborate through a fractal architecture, referred to as fractal organization. FrVE consists of frac-tal enterprises, a fractal proxy server, and their working mechanisms. Fractal enterprises are basic units ofFrVE, which are virtualized models of real enterprises. Fractal enterprises are categorized into three typesfrom the viewpoint of their collaborative network architecture: a single enterprise, a virtual organization,and a virtual enterprise. A fractal proxy server is a kind of external entities that are enabling or supportingthe virtual enterprise (VE) integration as well as the reconfiguration dynamics. It supports the certificationprocess of a fractal enterprise, the registration process, the partner selection process, and the trust evaluationprocess.

2.3.1. Fractal enterprise

Traditional enterprises can join the fractal-based breeding environment by implementing a virtual fractalunit (VFU) or modifying the VFU template. A VFU model is a virtual interface which receives various kindsof orders, referred to as business opportunities, from the fractal proxy server and communicates with otherenterprises as well. In this paper, it is assumed that the enterprises in the fractal-based breeding environmenthave implemented VFUs. According to their collaborative network architecture, enterprises are classified intothree types as follows.

� Single enterprise: basic unit of FrVE. When a traditional enterprise joins the breeding environment, theenterprise is designated as a single enterprise. As shown in Fig. 1, an enterprise can join the breeding envi-ronment through the registration procedure with some portions of the enterprise resources. A single enter-prise has its own capabilities in the breeding environment. A capability is the ability to mobilize a numberof resources in order to respond to the need of a task. A capability of a single enterprise Ent1 can be for-mally defined as (1)

CEnt1¼ h½REnt1

�; ½CpEnt1�i ð1Þ

where ½REnt1� ¼ RhT i jR1i typevdots..

.

RhT k jRji type

" #; RhT k jRji indicates the identification of the resource Rj required by the

task Tk, type e {classification descriptions such as machining, assembly, coating, material handling, etc.},CpEnt1

is competency of the enterprise.

VFU

Enterprise A

Single Enterprise A

Virtual domainTraditional

Core Resource

Fig. 1. Virtualization of an enterprise.


If necessary, a single enterprise can join a virtual organization or a virtual enterprise to maximize profit orfollow its risk diversification strategy.

� Virtual organization: virtual organization (VO) is a temporary alliance of single enterprises, virtual orga-nizations, and virtual enterprises, which is similar to a consortium or a cluster. In a VO, all enterprises sharethe authorities and responsibilities of the VO. There is no dominant actor in a VO, therefore, all partici-pants in a virtual organization make decisions through the negotiation process. In the view point ofICT, there is a representative VFU which receive orders from the environment, and the VFUs of all par-ticipants act as its sub-enterprises as shown in Fig. 2. The representative VFU acts as a community portal ofthe VO.� Virtual enterprise: virtual enterprise (VE) is similar to a VO. However; in a VE, there is a leading enterprise.

The leading enterprise acts as an employer, and it has the authority and responsibility to operate the virtualenterprise. When catching a business opportunity, the leading enterprise analyzes the business opportunityand searches its potential partners from the breeding environment or open universe. With a certain kind ofpartner selection process, the leading enterprise selects its partners and contracts with them about the termof the contract, profit sharing, ICT platforms, ontology, etc. Though their structures of a VO and a VE aresimilar, the overall processes such as the decision making process and coordination process are quite dif-ferent. The structure of a virtual organization is close to the distributed architecture, whereas that of a vir-tual enterprise is similar to the hierarchical architecture.

Three types of enterprises, a single enterprise, a VO, and a VE, are treated as a fractal enterprise as shownin Fig. 3a. A fractal enterprise can join or create a VO/VE to catch a business opportunity. Fig. 3b showsexamples of network topology of fractal enterprises.

Each enterprise is guaranteed to have authority by a VFU, whether its system is automatic or interactiveone. As shown in Fig. 4, a VFU is in charge of information exchanger. When an enterprise joins a value net-work, the enterprise creates a VFU based on the services that the enterprise wants to serve or use. There aresome constraints in implementing a VFU, which are necessary to cooperate with other enterprises and trans-late information.

2.3.2. Fractal proxy server

A fractal proxy server consists of a value-network server and a business innovation server. The value-net-work server manages (1) the descriptions of fractal enterprises such as enterprise type, scale, location, andVFU IP, and (2) enterprise collaboration results such as project results, peer evaluation results, etc. Thevalue-network server supports the partner search process of a fractal enterprise and enables the fractal enter-prise to build its enterprise networks. The business innovation server manages the information of businessopportunities and collaboration opportunities which include new business/collaboration opportunities, on-going business/collaboration opportunities, and completed ones. Through the business innovation server,each fractal enterprise realizes its enterprise goals and maximizes its profits.

Enterprise A Enterprise B Enterprise C

VFU VFU VFU

VFU

Virtual Organization

Fig. 2. Virtual organization concept.

Fractal architecture

: VFU of real enterprise : VFU of virtual organization

0..*

1..*

0..*

1..*

0..*0..*

0..*

0..*

0..*

0..*

1 1

Fractal Enterprise

Virtual Organization Virtual Enterprise

Single EnterpriseTraditional Enterprise

(b) Example of fractal enterprises(a) Basic units of fractal enterprises

Virtual Organization Virtual Enterprise

Single Enterprise

Fig. 3. Basic units of fractal enterprise.

Enterprise A

Enterprise B

Value Network

VFU A VFU BService

Abstraction

Enterprise C

Enterprise D Enterprise E

Automatic system Interactive System

Information exchange

physicalexchange

Fig. 4. Virtual fractal units.


3. Trust model

In our model, the trust values of enterprise A to enterprise B depend on the goal of collaboration. VE andVO creations are essentially a multi-criteria decision making problem, including soft factors such as culture,preferences, and learning ability (Camarinha-Matos & Afsarmanesh, 2007). Therefore, the optimization in VOand VE creation is not an important issue in practice. To integrate the overall distributed enterprises and opti-mize the enterprise network, FrVE provides a reference goal model and a goal propagation mechanism. Forexample, when a leading enterprise tries to find its partners, the predefined goals of VE are used to evaluatetrust values of alternatives, i.e., goal achievement probability.

3.1. Goal model

In FrVE, the goals of fractal enterprises are defined as the status of enterprises, which the enterprises aresupposed to satisfy or achieve over a certain period of time. The goal of a VE is normally represented in lin-guistic form and is difficult to quantify. In FrVE, the goal of a VE is represented in linguistic values which arecategorized into (1) maximization, (2) minimization, (3) meet, and (4) optimization (Ryu & Jung, 2004). Forexample, ‘‘minimization of due date violation” could be a goal of a VE. However, in the real world, it isimpossible to fulfill all goals. Therefore, each goal is fuzzified to allow prioritizing according to the importanceand urgency within a reasonable range.

In this paper, we assume that a leading enterprise represents its goal in terms of triangular fuzzy member-ship functions. Then a goal can be represented as Eq. (2), where i is the enterprise ID and j is the goal indi-cator. If required, other possible shapes such as trapezoid, Gaussian, sigmoid, and exponential functions couldbe used to refine the model. For more details of the goal model and the goal-formation process, refer to Ryu &Jung, 2004; Shin, Cha, Ryu, & Jung, 2006.

G ¼ f~gði;jÞ; ~gði;jÞ ¼ ðgði;jÞ; gði;jÞ; gði;jÞÞ; gði;jÞ P 0; gði;jÞ P 0g ð2Þ


3.2. Trust evaluation with a fuzzy goal

In this paper, the concept of trust is interpreted as the goal achievement probability of an enterprise when itcompletes the given task. When a goal and the interested facts are represented as a crisp value, we can use asimple evaluation equation to determine the trust value of an enterprise as Eq. (3), where xi is a fact and gi isthe goal of the fact.

F trustðxiÞ ¼1

ejxi�gijð3Þ

However, our input and output values are fuzzy sets, therefore, we need to modify Eq. (3) to determine thetrust value of an enterprise based on fuzzy sets. Fuzzy reasoning is used to determine trust value of an enter-prise. Fuzzy reasoning is an inference procedure that derives conclusions from a set of fuzzy if-then rules andknown facts. For example, in this context, a fuzzy if-then rule with two antecedents is usually written as ‘‘if x isA and y is B, then trust value is C.” The corresponding problem for fuzzy reasoning, lC0 (trust_value), is ex-pressed as

premise 1 (fact): x is A0 and y is B0;premise 2 (rule1): if x is A1 and y is B1 then trust value is C1;premise 3 (rule2): if x is A2 and y is B2 then trust value is C2;consequence (conclusion): trust value is C0.

Fig. 5 shows the operation of fuzzy reasoning graphically for multiple rules with multiple antecedents.

3.3. Trust of a single enterprise

The trust value of an enterprise evaluated by the goal achievement probability cannot be measured as crispvalues since it always involves ambiguity and subjectivity. Nevertheless, its evaluation is highly desirable in thevirtual environment to find the right and trustworthy partners. To cope with this problem, a fuzzy model isused to evaluate trust values of enterprises. In FrVE, the intangible factors are measured with a fuzzy trustevaluation model as well as the tangible factors such as cost, time, and quality. The trust of a single enterprisewould be different according to the goal of a VO or a VE as shown in Fig. 6. It is assumed that the goal of the

Fig. 5. Fuzzy reasoning for multiple rules and multiple antecedents.

Fig. 6. Trust evaluation with enterprise information.


newly generated VO or VE is (1) to produce a product with the budget of 96, (2) to deliver the final product toa warehouse until 110 unit time, and (3) to satisfy the quality level of the product 0.002 as shown in Fig. 7. Inthis case, if an enterprise who can complete the given sub-task with high punctuality and high quality, the trustvalue of an enterprise would be high. The goal of the newly generated VO/VE can be interpreted as follows.

The trust of a single enterprise Ei is obtained by Eq. (4), where N is the number of sub-goals, lRjðIEiÞ is

defuzzified trust value of Ei according to sub-goal j, IEi is information of Ei, and dj is a weight of factor j.

T SðEiÞ ¼XN

j¼1

djlRjðIEiÞ ð4Þ

3.4. Evaluation procedure

In the fractal enterprise, an enterprise uses trust value when it has alternatives in selecting its partners. Trustvalue represents the probability that the target enterprise will satisfy the requirements or constraints. Forexample, if an enterprise wants to find a partner who produces high quality products, the enterprise appliestrust value of quality when it searches its potential partner from the breeding environment, and selects theone who has the best trust value. The procedure of evaluating trust value of an enterprise follows (1) goal gen-eration, (2) factor selection, (3) fuzzy inference system design, and (4) trust evaluation.

3.4.1. Goal generation

The decision-maker of a leading enterprise first generates the goal of the planned VO or VE in linguisticform. In this paper, to minimize the problem size, it is assumed that the leading enterprise wants to createa VE and its goal is ‘‘produce product A with low lateness, high quality, and normal cost.” Their reasonableranges are illustrated in Fig. 7.

3.4.2. Factor selection

For each goal, the decision-maker determines and selects the factors which are related to the goal andstored in the fractal proxy server. For example, for the goal of the time as mentioned above, the availablerange is from 108 to 115 with low lateness. It means that the time required to complete everything fromthe first material preparation to the final assembly or manufacturing should not exceed the limit, 115. Further-more, the process should satisfy the constraint that maintains low lateness. In this case, the decision-maker canselect the factors such as processing time, average due date violation, variance of due date violation, the ratio

Cost94 96 98 108 110 115 0.001 0.002 0.007Time Quality

Fig. 7. Generated fuzzy goal.


of due date violation, delivery time, etc. Collaboration results and enterprise information are stored in thefractal proxy server for this purpose.

3.4.3. Fuzzy inference system design

After the factor selection is completed, the decision-maker designs a fuzzy inference system with the help ofanalysis. Fuzzy logic represents a promising concept to incorporate uncertainty and close the gap betweenhuman reasoning and computational logic. It is also easy to apply in the real world with simple steps.

3.4.3.1. Fuzzy sets & membership functions. The selected factors are the inputs of the fuzzy inference system. Inthis step, appropriated fuzzy set should be defined. For example, if the factor ‘‘average due date violation” isselected, then fuzzy sets ‘‘low”, ‘‘medium”, and ‘‘high” can be defined, which are characterized by membershipfunctions llow, lmedium, and lhigh. In the same manner, the fuzzy sets ‘‘very poor”, ‘‘poor”, ‘‘normal”, ‘‘good”,and ‘‘very good” can be defined for the factor ‘‘number of defects” which are related to quality. Fig. 8 repre-sents the fuzzy set for the factor ‘‘average due data violation” and ‘‘number of defects.”

3.4.3.2. Fuzzy rule design. Fuzzy rule design describes the fuzzy rules expressing the relationship between theinput variables and the trust value. Each rule is defined by a fuzzy if-then rule. According to the number ofinput variables and their fuzzy sets, partitioning of the input spaces should be performed to minimize the num-ber of rules usually referred to as the curse of dimensionality. An example for a rule would be:

IF ‘‘avg. due date violation” is low;AND ‘‘avg. number of defects” is normal;THEN ‘‘trust” is high.

3.4.4. Trust evaluation

The evaluated trust values of enterprises are used as one of the partner selection criteria. In FrVE, aleading enterprise can find its partners either by passive partner selection or by active partner selection. Inpassive partner selection, a leading enterprise registers its business opportunity (BO) in the fractal proxyserver. The fractal proxy server verifies the BO and announces it to other fractal enterprises. The enter-prises who are interested in the BO start a negotiation process with the leading enterprise. The leadingenterprise receives information of candidates who call for bids on the BO and selects proper enterprisesafter enterprise evaluation. On the other hand, in the active partner selection, the leading enterprise ana-lyzes the BO and requests for required resource capacity in the fractal proxy server. The server reports thelist of enterprises and then the leading enterprise starts a negotiation process with the enterprises in thelist. Whatever the selection process is, the decision-maker considers the trust values of candidates beforethe partner selection.

Through the partner selection of a VO or VE creation, many enterprises would be selected to take part insome portions of the projects. Therefore, it is important to select good combinations of enterprises. Becausethis problem is beyond the scope of this paper, we use the following simple rules.

0

1.0

T1 T2 T3

Low Medium High

(a) Avg. due date violation (day)

0C1

Very poorPoor

(b) Avg. number of defects (defects/10000)

C2 C3 C4 C5

1.0Normal

GoodVery good

Fig. 8. Example of the fuzzy sets for time and quality.


� Processes are serial: in this case, the average of the trust values with their relative importance represents thefinal trust value of the alternative.� Processes are parallel: in this case, the lowest one of the trust values is the final trust value of the alternative.

In this paper, we use Xfuzzy 3.0 (IMSE-CNM., 2003) to model the fuzzy trust evaluation module and gen-erate C++ code. Xfuzzy 3.0 is a development environment for the fuzzy inference system which is composedof several tools that cover different stages of the design process, from initial description to the final implemen-tation. Implemented fuzzy trust evaluation is illustrated in Fig. 9.

3.5. Model comparison

In developing a trust evaluation model which is suitable for VE environment, minimization of cost and timeas well as minimization of human experts’ effort are important criteria in developing the model. Table 1 sum-marizes trust evaluation models with their inputs and evaluation methods in the literature. Sreenath & Sing’smodel (2004) uses two inputs, subjective evaluation and reputation obtained from other agents, and calculatetrust value through the weighed sum of the two inputs. Schmidt et al.’s model (2007) also uses subjective eval-uation, collaboration estimates, and credibility information of a target agent. They use a fuzzy inference sys-tem to calculate trust value because the inputs of their model are fuzzy sets. However, in the real field, toobtain reputation information of other enterprises is not easy. Lavra�c et al. (2007) have proposed a trustmodel based on multi-criteria decision analysis. They use reputation and collaboration estimates as inputs.Msanjila and Afsarmanesh (2008) have proposed a trust model based on causal analysis. They try to developa quantitative evaluation model, however, causal effects and relations between trust elements are not presentedin their model. These models require human experts’ effort to evaluate the trust value of an enterprise. Also,the purpose of finding partners (the objective of VO/VE) does not affect the trust values of enterprises. If thenumber of enterprises to be evaluated increases, the required time and cost would increase geometrically.Goal-oriented trust model uses two inputs which are goals and factors of concern. The model proposed by

Fig. 9. A fuzzy trust evaluation model.

Table 1Comparison with other trust models

Model Input Evaluation tool/methods

Target

Sreenath and Singh (2004) Subjectiveevaluation

Sum of weightedreputation

An agent

Reputation

Schmidt et al. (2007) Subjectiveevaluation

Fuzzy inferencesystem

An agent

CollaborationestimatesCredibility

Lavra�c et al. (2007) Reputation Multi-criteriadecision analysis

SingleenterpriseCollaboration

estimates

Msanjila and Afsarmanesh (2008) Trust elements(known factors)

Causal analysis Singleenterprise

Goal-oriented trust model Goal of a VO orVE

Fuzzy inferencesystem

Singleenterprise, VO,VEFactors of concern


us is more practical than other models in VE environment. The targets of trust evaluation are single enterprise,VOs, and VEs.

4. Exemplary scenario

It is assumed that a leading enterprise wants to produce products with a process plan (simply AND-ORgraph form) as shown in Fig. 10. It searches the breeding environment for its potential partners where variousindexes (quantity, cost, quality, etc.,) can be applied to find appropriate and available alternatives. To simplifythe problem size, Operation01 (OP01) and Operation02 (OP02) are considered in this example. After analyz-ing the business opportunity, the leading enterprise determines the process plan in the Fig. 10. The fuzzy goalsfor the operation set of {OP01, OP02} are as shown in Fig. 6.

The enterprise searches its partners who will be in charge of OP01, OP02, and the distribution betweenthem. In the case of our problem, there are 4 manufacturing enterprises (Ent A, Ent B, Ent C, and Ent D),and 2 distribution enterprises (Ent K, Ent M) except the leading enterprise. Table 2 shows 8 alternatives,which are the feasible results of partner search. The enterprise information set is defined as {cost, (start time,

Fig. 10. Process plan of a leading enterprise.

Table 2Results of partner search

Alt. OP01 OP01 ? OP02 OP02 Cost Time

1 EntA {48,(0,50)} EntK{2,(50,60)} EntB{45,(60,110)} 95 1102 EntA {48,(0,50)} EntK{3,(55,65)} EntD{46,(65,115)} 97 1153 EntA {48,(0,50)} EntM{3,(50,60)} EntB{45,(60,110)} 96 1104 EntA {48,(0,50)} EntM{4,(55,65)} EntD{46,(65,115)} 98 1155 EntC {49,(0,48)} EntK{1,(48,58)} EntB{45,(58,108)} 95 1086 EntC {49,(0,48)} EntK{2,(48,65)} EntD{46,(65,115)} 97 1157 EntC {49,(0,48)} EntM{2,(48,58)} EntB{45,(58,108)} 96 1088 EntC {49,(0,48)} EntM{3,(48,65)} EntD{46,(65,115)} 98 115


finish time)}. In the conventional researches, the alternatives that minimize cost, minimize expected time or themixed one will be selected according to the enterprise strategy. Optimization-based methods of selecting part-ners have been widely researched. However, it has been recognized that VE and VO creations are essentially amulti-criteria decision making problem with soft factors. To develop the goal model and fuzzy inference sys-tem is the out of the scope of the paper, therefore, we assume that trust values of enterprises are obtain fromthe data listed in Table 3.

Trust score of each alternative is calculated as follows.In our example, OP01 and OP02 are serial; therefore, the average of the trust values is calculated. The rel-

ative importance is simply calculated with the time portion. Alternative 1 consists of EntA, EntB, and EntK.The final trust score of alternative 1 is

TableTrust v

Enterp

EntAEntBEntCEntDEntMEntK

TableOveral

Altern

12345678

Trustalt1 ¼ 0:49 � ð50=110Þ þ 0:80 � ð10=110Þ þ 0:72 � ð50=110Þ ¼ 0:623:

The overall results, calculated by using the equation above, are listed in Table 4. Although alternative 7 isnot the minimum cost, it has the highest trust value compared to other alternatives, and therefore, our modelrecommends this alternative. Our results show that alternative 5 has the second highest trust value with thelowest cost. The decision-maker can select this alternative when cost is the key success factor.

3alues of enterprises

rise Avg. due date violation Avg. # of defects Evaluated trust value

5.5 58 0.492.3 37 0.722.1 19 0.784.4 35 0.570.1 – 0.830.3 – 0.80

4l results of alternative evaluation

ative Cost Time Trust

95 110 0.62397 115 0.56596 110 0.62598 115 0.56995 108 0.73396 115 0.69297 108 0.75798 115 0.687


5. Conclusion

In the virtual enterprise domain, the enterprises work together in the value network, referred to as a breed-ing environment. There are many advantages when enterprises join the breeding environment. However, somebarriers to collaboration prevent the enterprises from working together. Trust is one of the imminent problemsin the virtual environment. To cope with this problem, we proposed a goal-oriented fuzzy trust evaluationmodel for a VO/VE creation in the fractal-based virtual enterprise model (FrVE). FrVE is a fractal-basedVE model that consists of fractal enterprises, a fractal proxy server, and their working mechanisms. The frac-tal enterprises are basic units of FrVE, which are virtualized models of real enterprises. A fractal proxy serveris a kind of external entities as the environment for enabling or supporting the virtual enterprise (VE) integra-tion as well as a reconfiguration dynamics.

In this paper, we also proposed a trust evaluation method of supporting enterprise collaboration and max-imizing the satisfaction of cooperation. Trust value of an enterprise can be obtained by fuzzy inference systemwhose rule-base is based on the top-level goal of a VE. According to the selector’s preference, various rules canbe applied to trust evaluation. In our example, the result of partner selection is completely different when trustvalue is added to the criteria of alternative selection.

For further study, the planning and scheduling problems should be considered with the trust-based partnerselection for collaboration among manufacturing enterprises. Maximizing the enterprise satisfaction of collab-oration and designing the elaborate trust evaluation methods would be investigated.

Acknowledgments

This work was supported by the Korea Research Foundation Grant funded by the Korean Government(MOEHRD) (KRF-2006-311-D0095). The authors would like to express their gratitude for the support.

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