Chun Che Huang

Modularity in design of products and systems

Modularity refers to the use of common units to create product variants. This paper aims at the development of models and solution approaches to the modularity problem for mechanical, electrical, and mixed process products (e.g., electromechanical products). To interpret various types of modularity, e.g., component-swapping, component-sharing and bus modularity, a matrix representation of the modularity problem is presented. The decomposition approach is used to determine modules for different products. The representation and solution approaches presented are illustrated with numerous examples. The paper presents a formal approach to modularity allowing for optimal forming of modules even in the situation of insufficient availability of information. The modules determined may be shared across different products.

Development of modular products

Modular products refer to products, assemblies, and components that fulfill various functions through the combination of distinct building blocks. As companies strive to rationalize engineering design, manufacturing, and support processes and to produce a large variety of products at lower costs, modularity is becoming a focus. However, modularity has been treated in the literature at an abstract level, and it has not been satisfactorily explored in industry. Furthermore, the modular technology in electronics such as multichip modules (MCMs) aims at decreasing the average spacing between integrated circuits rather than increasing the variety of product characteristics of other types of designs. This paper develops a methodology for determining modular products while considering cost and performance. To interpret various types of modularity such as component-swapping, component-sharing, and bus modularity, a graphical representation of the product modularity is presented, while the module components of a product set are determined by a heuristic approach. With the module components known, a rule-based fuzzy representation of the module development problem is presented, while the tradeoff between performance and cost of modules is analyzed by a fuzzy neural network approach. The approach is illustrated with the example of an MCM.

The agent-based negotiation process for B2C e-commerce

B2C e-commerce is becoming more widespread as more people come to recognize its convenience and its ability to rapidly respond to requests and as more products and services become available. However, many electronic marketplaces, especially in the business-to-consumer, are in essence some kind of search engine where buyers look for the best product in a database of products offered by sellers. Usually, such e-marketplaces do not use agent technology at all although agents could significantly improve the services provided both for the buyers and the sellers. Further, negotiation capabilities are essential for B2C e-commerce systems. In an automated negotiation, intelligent agents engage in broadly similar processes to achieve the same end. In more detail, the agents prepare bids for and evaluate offers on behalf of the parties they represent with the aim of obtaining the maximum benefit for their users. Nevertheless, in the current situation, price is the only criterion by which agents are created. This factor is easy to measure and automate. However, the criteria for advanced transactions need to be elaborated, for example, details of giveback and dividend. In this paper, we present a multiple-attributes negotiation model for B2C e-commerce, which deploys intelligent agents to facilitate autonomous and automatic on-line buying and selling by intelligent agents while quickly responding to consumers. These include a 4-phase model, information collection, search, negotiation, and evaluation. We also apply fuzzy theory and analytical hierarchy process to develop the system interface to facilitate the user inputs. Finally, an example of the notebook purchasing process is illustrated.

Rough set approach to case-based reasoning application

Abstract The case-based reasoning becomes a novel paradigm that solves a new problem by remembering a previous similar situation and by reusing information and knowledge of that situation. In general, the traditional representation of cases is too simple and is not well structured to support the decision-making in organization. Furthermore, the similarity testing of case-based reasoning is very time-consuming. Therefore, a novel approach to represent the knowledge of cases in an explicit manner and to search similar cases in an efficient way is desired. An Extensible Markup Language-based representation formulated with the Zachman framework is proposed in this paper. Through a rough set based approach, case-based reasoning becomes more efficient and complexity of computation of the similarity testing is significantly reduced.

Rule induction based on an incremental rough set

The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision table. In addition, these previous incremental approaches are not efficient in a large database. In this paper, an incremental rule-extraction algorithm based on the previous rule-extraction algorithm is proposed to resolve there aforementioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database.

Novel approach to multi-functional project team formation

Abstract A group of people from different functional departments or various areas of work responsibility, working together and exchanging information through networks, is called a “multi-functional team” in the e-world. Multi-functional teams are becoming crucial because organizations always require group cooperation across functional lines and the members may not be in the same location. The formation of multi-functional teams is becoming a key issue in project management. The literature does not provide analytical solutions for forming multi-functional teams under uncertain information environment. To deal with the underlying complexities of the multi-functional teams formation process, a methodology for the multi-functional teams formation is developed. The methodology is based on fuzzy sets theory and grey decision theory. Fuzzy sets theory is applied to deal with problems involving ambiguities, which are normally confronted in multi-functional teams formation practice and form groups, when there is no clear boundary for relationship between customers’ requirements and project characteristics. Grey decision theory is also used to select desired team members through abstractural information. Specifically, the team member is required to be competent in his/her work and also able to share other’s responsibility. In this paper, applying the fuzzy and grey approaches demonstrates its capability of forming a good multi-functional team and it is promising for dealing with insufficient information at the team forming stage.

Cost evaluation in design with form features

Reducing the cost of a product at the design stage is more effective than at the manufacturing stage. In this paper, an attempt is presented to quantify the manufacturing cost in feature-based design. Machining form features are classified as simple and complex. The machining cost of a part depends not only on the type of form features, but also on the relationship among the features. This cost is calculated for four different cases: traditional machining, simultaneous machining, simultaneous set-up and changeover, and multiple machine tools. A manufacturing process is represented with a digraph. The feature-based evaluation of manufacturing cost is formulated as the shortest path problem, and a mathematical model as well as an algorithm are presented to determine the minimum cost design alternative.

Sharing knowledge in a supply chain using the semantic web

Interoperability among multi-entities (companies) with heterogeneous knowledge sources becomes a research focus in the field of Supply chain management (SCM). Specifically, sharing knowledge among multiple entities in a supply chain is crucial. However, only a few studies have addressed the problem of interoperability and knowledge sharing in supply chains. Current technologies, such as EDI, RosettaNet or the current Web, are useful for sharing data/information, rather than knowledge. This paper proposes a solution for sharing knowledge with the semantic web. The solution involves (i) a semi-structured knowledge model to represent knowledge in not only an explicit and sharable, but also a meaningful format, (ii) an agent-based annotation process to resolve issues associated with the heterogeneity of knowledge documents, and (iii) an articulation mechanism to improve the effectiveness of interoperability between two heterogeneous ontologies. Based on the proposed solution, entities in a supply chain can represent, seek, and share knowledge effectively.

Manufacturing control with a push-pull approach

Kanban systems have been successfully used to reduce inventory and improve customer service in many industries. Their advantages and limitations are discussed in this paper. Although numerous models have been developed to overcome these limitations, the studies published did not consider some of the very essential characteristics of manufacturing systems and the value added to parts when the pull or push principle was applied. This paper presents a push-pull approach which pushes through certain manufacturing stages and pulls elsewhere based on the characteristics and value added at these stages. A generalized labelcorrecting algorithm to determine the desired stages of an IDEF3 model is developed and an industrial case study is presented. This methodology is suited for a repetitive manufacturing environment of a mixed type, i.e. job shop and flow shop, and leads to achieving the following objectives: (1) reduced in-process inventory; (2) shortened delivery lead times while maintaining or improving delive...

Alternative rule induction methods based on incremental object using rough set theory

The rough set (RS) theory can be seen as a new mathematical approach to vagueness and is capable of discovering important facts hidden in that data. However, traditional rough set approach ignores that the desired reducts are not necessarily unique since several reducts could include the same value of the strength index. In addition, the current RS algorithms have the ability to generate a set of classification rules efficiently, but they cannot generate rules incrementally when new objects are given. Numerous studies of incremental approaches are not capable to deal with the problems of large database. Therefore, an incremental rule-extraction algorithm is proposed to solve these issues in this study. Using this algorithm, when a new object is added up to an information system, it is unnecessary to re-compute rule sets from the very beginning, which can quickly generate the complete but not repetitive rules. In the case study, the results show that the incremental issues of new data add-in are resolved and a huge computation time is saved.