Development of a product configuration system with an ontology-based approach

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<ul><li><p>uecProduct configurationOntologyOWLSemantic web</p><p>level, a configuration meta-model is defined. Based on this meta-model, domain-specific configurationknowledge can be derived by reusing or inheriting the classes or relations in the meta-model.Configuration models are formalized using OWL (Ontology Web Language), an ontology representationlanguage developed by W3C. As a result, configuration models have well-defined semantics due tothe logic semantics of OWL, making it possible to automatically detect inconsistencies of configurationknowledge bases. Furthermore, configuration constraints are represented in SWRL, a rule language basedon OWL. Finally, actual configuration processes are carried out using JESS, a rule engine for the Javaplatform, by mapping OWL-based configuration facts and SWRL-based configuration constraints intoJESS facts and JESS rules, respectively. The proposed methodology is illustrated with an example forconfiguring the ranger drilling machine.</p><p> 2008 Elsevier Ltd. All rights reserved.</p><p>1. Introduction</p><p>With the emerging paradigm of mass customization, productsare designed into customizable modules or parts to meet indi-vidual needs of customers [1,2]. With an increasing number ofmodules and parts in a customizable product, assembling thesemodules or parts into a legal constellation in a manual way be-comes impracticable [3]. To reduce lead-time and shorten productcycle, product configuration technologies are developed to auto-mate the processes of configuring a product [1,2]. A product con-figuration system is defined as one that is capable of automaticallyor interactively configuring a product to satisfy both customersneeds and technical constraints using product configuration tech-nologies. The application of product configuration systems facil-itates the sales-delivery process of products and avoids possibleerrors transferred between sale departments and engineering de-partments in manufacturing companies [4,46].</p><p>The study of effective product configuration technologies hasreceived much attention from the academic community and in-dustry over years [13]. Previous research effort focused mainlyon the actual configuration process for solving product config-uration problems, such as the rule-based approach [5] and the</p><p> Corresponding author. Tel.: +86 02154744138; fax: +86 02134206675.E-mail address: dongyang@sjtu.edu.cn (D. Yang).</p><p>CSP (Constraint Satisfaction Problem) approach [6,7]. Recently, at-tention has been directed towards the study of conceptual mod-eling of customizable products, namely, product configurationmodels [8,23,44]. This is due to well-defined conceptual mod-els being able to describe highly complex structures and con-straints of customizable products. As a result, product config-uration systems are able to deal with the problems of con-figuring complex products under mass customization. Further-more, the reusability of configuration models can effectivelyreduce the time of developing product configuration systems. On-tology, which is defined as the conceptualization of terms and re-lations in a domain, offers a means to structurally represent andreuse domain knowledge [9]. In this paper, we address the mod-eling of product configuration knowledge with an ontology-basedapproach in which structural knowledge is formalized in OWL(OntologyWeb Language) [10,11], an ontology representation lan-guage developed byWorldWideWeb Consortium (W3C), and con-straint knowledge in SWRL, (Semantic Web Rule Language) [12], arule language based on OWL. Through the transformation of con-figuration knowledge into JESS facts and JESS rules, actual config-uration processes are carried out with the support of JESS [13], arule engine for the Java platform.</p><p>The remainder of this paper is organized as follows. Thetechnical background is sketched in Section 2. Section 3 gives anoverview of related work. In Section 4, we present a four-layerComputer-Aided Desig</p><p>Contents lists availa</p><p>Computer-A</p><p>journal homepage: www</p><p>Development of a product configurationontology-based approachDong Yang , Ming Dong, Rui MiaoDepartment of Industrial Engineering, School of Mechanical Engineering, Shanghai Jiao To</p><p>a r t i c l e i n f o</p><p>Article history:Received 15 December 2007Accepted 20 May 2008</p><p>Keywords:</p><p>a b s t r a c t</p><p>Product configuration is a crset of customizable componof enabling efficient and effeknowledge, an ontology-basthis paper. The ontology-bas0010-4485/$ see front matter 2008 Elsevier Ltd. All rights reserved.doi:10.1016/j.cad.2008.05.004n 40 (2008) 863878</p><p>ble at ScienceDirect</p><p>ided Design</p><p>.elsevier.com/locate/cad</p><p>system with an</p><p>ng University, 800 Dong Chuan Road, Shanghai 200240, China</p><p>cial means to implement themass customization paradigm by assembling ants to satisfy both customers needs and technical constraints. With the aimtive development of product configuration systemsby reusing configurationed approach to modeling product configuration knowledge is presented ined product configuration models are hierarchically organized. At the lower</p></li><li><p>tecture of a product configuration system is shown in Fig. 1. Theproduct configuration problem can be formally described as fol-lows.</p><p>Definition 1. A configuration problem (CP) is formulated as:</p><p>CP := {C, P, Cr, R}where</p><p>C a set of components that may constitute a customizableproduct;</p><p>P a set of properties of components;Cr a set of constraints imposed on components due to</p><p>technical and economical factors.R a set of customer requirements, which are usually specified</p><p>in the forms of constraints.</p><p>Definition 2. A configuration Solution (CS) or a configuration isdefined as:</p><p>CS := {I, V , S}</p><p>O := {I, C, R, A}where</p><p>O an ontology for a domain of interest;I a set of individuals or objects in a domain;C a set of concepts in a domain;R a set of relations among concepts, relations and objects;A a set of axioms holding among concepts, relations and</p><p>objects.</p><p>2.3. OWL</p><p>To encode configuration knowledge in a formal manner, itis requisite to employ an expressive knowledge representationlanguage. OWL is a new ontology language for the SemanticWeb, developed by the World Wide Web Consortium (W3C)Web Ontology Working Group [10,33]. OWL is primarily devisedto represent knowledge, namely objects and how objects areinterrelated, to be shared and exchanged without dispute as to864 D. Yang et al. / Computer-Aide</p><p>Fig. 1. The architecture of pr</p><p>approach to modeling product configuration knowledge. Productconfiguration knowledge is then encoded using the ontologylanguage, namely OWL and the rule language, i.e. SWRL, whichis dealt with in Section 5. In Section 6, developing a productconfigurator based on the JESS rule engine is addressed. Finally,conclusions are drawn in Section 7.</p><p>2. Background</p><p>2.1. Product configuration background</p><p>Given a set of predefined components, the task of product con-figuration is to find a configuration solution satisfying individualneeds of customers without violating all constraints imposed oncomponents due to technical and economical factors [6]. Configu-ration models describing all legal combinations of components in-clude knowledge about the structure of products and knowledgeabout technical and economical constraints. Additionally, user re-quirements can be specified in the form of constraints, such as con-straints on properties of a component. Using a problem-solvingtechnology, configuration engines perform actual inference pro-cesses with both configuration models and user requirements asthe inputs and then generate a configuration as the output. A con-figuration (or configuration solution) consists of the componentindividuals, the assignment of values to properties of these indi-viduals and the connection relations among components such thatall constraints and customer requirements are satisfied. The archi-whered Design 40 (2008) 863878</p><p>oduct configuration systems.</p><p>I a set of individuals, which are instances of components.V a set of values, which are assigned to properties of</p><p>individuals.S a Boolean function, S : {Cr, R} {T , F}. The assignment</p><p>of I and V makes the expressions Cr and R true.</p><p>Definition 3. A configuration engine (Ce) is a function that maps aconfiguration problem CP to a set of configuration solutions CS:</p><p>Ce : {CP} {a finite set of CS}.</p><p>2.2. Ontology</p><p>An ontology is an explicit, formal specification of a sharedconceptualization of a domain of interest. The term is borrowedfrom philosophy, where an ontology is a systematic account ofexistence [9]. The ontology provides a shared understanding ofa domain of interest to support communication among humanbeings and applications. One main advantage of ontology is theability to support the sharing and reuse of formally representedknowledge by explicitly stating concepts, relations, and axioms ina domain.</p><p>Ontology has been widely applied in a variety of domains torepresent information or knowledge models owing to the fact thatits formal semantic can be unambiguously interpreted by humansand machines. Ontology can be formulated as below.</p><p>Definition 4. Ontology is defined as:precise meaning. Absorbing the advantages of various knowledgerepresentation languages, the development of OWLwas influenced</p></li><li><p>preferences on product attributes, which were measured usingutility function, and costs of a product. Different from the workin [14], a distinguishing characteristic in their research is thatconfiguration constraints, such as inclusive relations and exclusiverelations, are considered in the genetic algorithm for solvingand optimizing product configuration problems. Similar work onthe use of genetic algorithms for solving product configurationproblems was also reported by Li et al. [16] and Yeh et al. [17].Nevertheless, the GA-based approach for product configurationis only suitable for the configuration problems where only a fewconfiguration constraints exist in configuration models and thus</p><p>not be actively considered as a part of a final solution [7]. Althoughthe CSP-based approach and its variants have been widely appliedin solving product configuration problems, they cannot representcomplex structures of customized products, such as whole-part relations between components, abstract components, andadvanced constraints (such as resource constraints) because aproduct in the CSP-based approach is simply represented as thatconsisting of components and the connections between their ports.As a result, the CSP-based approach has difficulties in dealing withconfiguring highly complex and customizable products.</p><p>Tomodel customized and complex products, Felfernig et al. [23]D. Yang et al. / Computer-Aide</p><p>Table 1OWL constructs</p><p>OWL</p><p>rdfs:subClassOf (A, B)rdfs:subPropertyOf (A, B)P OWL: allValueFrom (C)P OWL: someValueFrom (C)P OWL: miniCardinality (n)P OWL: maxCardinality (n)P OWL: Cardinality (n)</p><p>by Description Logics [34], RDFS (Resource Description FrameworkSchema) [51] and Frame paradigms. The semantics of OWL [35] arebased on Description Logics whose advantage is that a knowledgebase can be automatically checked against subsumption of classes,inconsistency of concepts and entailment relationships. OWLalso offers surface syntax in both RDF (Resource DescriptionFramework) /XML and frame-like formats [35], making OWL easilyreadable and understandable even for non-experts. Regardinglanguage constructors and expressive power of languages, OWL is amajor extension over RDFS. Table 1 givesmain language constructsin OWL and their corresponding intuitive meaning.</p><p>3. Literature review</p><p>In this section, we mainly address related work in productconfiguration and ontology application in manufacturing. Duringrecent years, much research effort has been devoted to developingproduct configuration systems. Various techniques have beensuggested to solve product configuration problems, including theGA (Genetic algorithm)-based approach, case-based reasoning(CBR) method, rule-based approach, CSP-based technique, etc. Onthe other hand, ontology has been applied by many researchersto model engineering and design-related knowledge to facilitateknowledge reuse and information sharing among applications. Themain research work in both product configuration and ontologyapplication is summarized below.</p><p>3.1. Related work in product configuration</p><p>Genetic algorithms have been used in the literature to solve theproduct configuration problem [1417]. Hong et al. [14] addressedthe problem of optimal product configuration under the One-of-Kind production (OKP) paradigm. Variations for customizableproducts and parameters in the OKP product family were modeledwith ANDOR trees and parameters of nodes in this tree. Theyemployed the genetic algorithm as the solving mechanism toobtain optimal configuration, taking customer requirements ondifferent aspects such as performance and cost into consideration.Zhou et al. [15] adopted the ANDOR graph to represent theconfiguration spaces of a customized product. The optimization ofproduct configuration was done by means of a genetic algorithm.The objective function of optimization considered both customernumerous feasible solutions can be obtained. Obviously, it is not anideal candidate for configuration problems that are characterizedd Design 40 (2008) 863878 865</p><p>Intuition</p><p>Class A is a subclass of class B.Property A is a sub-property of property B.All values of property P belong to class C .Some value of property P belongs to class C .The number of values that property P can take must be greater than or equal to n.The number of values that property P can take must not exceed n.The number of values that property P takes is exactly n.</p><p>by complex structures and a number of strict constraints imposedon components.</p><p>A different viewpoint on product configuration is that theproduct configuration problem can be seen as one of case-basedreasoning (CBR) [18,19]. To configure a new customer order,similar previous cases are retrieved and the one with best degreeof similarity is recommended. For instance, Tseng et al. [18]adopted the CBR to perform actual product configuration, aimingto reuse previous successful reasoning case. Similar work thatused the CBR for product configuration was also reported byLee and Lee [19]. However, CBR-based method is only usefulwhen knowledge is incomplete. Therefore, the reuse of productstructure knowledge and constraints is not supported in the CBR-based product configuration. As a consequence, the CBR-basedconfiguration is limited to some special occasions.</p><p>The very earliest product configurator, namely R1 system (latercalled XCON), used a rule-based reasoning approach for productconfiguration [5]. However, the weakness of the rule-basedapproach lies in the problem with maintaining rule bases becauseboth configuration knowledge (including product structures andconstraints) and policy knowledge (namely that concerning howto solve configurations) are interweaved in rules [1].</p><p>Mittal and Frayman [6] viewed product configuration asthe CSP problem (Constraint Satisfaction Problem) where bothports...</p></li></ul>