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Previous approaches to decision support for project planning using rule‐based expert systems techniques have failed to make an impact in practice. This is primarily because of the complexity and large‐scale nature of construction information, and problems with expert systems including: knowledge acquisition; rule‐based knowledge representation; information storage (or memory); learning; and robustness. Case‐based reasoning is one area of current research which may hold the key to overcoming a number of these problems. In the present paper, previous related case‐based reasoning work is examined. The key factors which influence the formulation of construction project plans are identified. This knowledge is used to develop a conceptual framework within which previous planning experiences can be captured and reused in new situations as a means of providing system decision support in construction planning and control. A prototype system, CBRidge, developed to test and demonstrate the concepts within the framework is presented. The results are very encouraging and provide a sound basis for the further development of case‐based reasoning for construction planning in practice.

Applied a hybrid approach using genetic algorithms (GAs) for a case-based retrieval process in order to increase the overall improved cost accuracy for a case-based library. The paper aims to discuss this issue.

A weight optimization approach using case-based reasoning (CBR) with proposed GAs for developing the CBR model. GAs are used to investigate optimized weight generation with an application to real project cases.

The proposed CBR model can reduce errors consistently, and be potentially useful in the early financial planning stage. The authors suggest the developed CBR model can provide decision-makers with accurate cost information for assessing and comparing multiple alternatives in order to obtain the optimal solution while controlling cost.

The system can operate with more accuracy or less cost, and CBR can be used to better understand the effects of factor interaction and variation during the developed system’s process.

Delivering a building project on time and under budget is still an increasingly complex and risky business. A host of new project delivery methods and management techniques have been promoted to help achieve this. Developing a project delivery strategy and contractual relationships is a particularly complex and large problem domain that requires diverse, highly sophisticated skills, expertise and knowledge. Expert knowledge and lessons learned in the construction phase of a building project hold a wealth of knowledge and expertise that can be often taken for granted. However, this body of knowledge is not systematically incorporated into the procurement phase of subsequent projects. Case‐based reasoning is a technology for problem solving based on recall and reuse of specific experiences; it offers techniques for acquiring, representing and managing the knowledge gained from previous experiences and augmenting a set of specific experiences with generalised knowledge. Outlines how current case‐based reasoning techniques support project delivery method decisions. Presents a case‐based framework for project delivery method selection. Finally, reports the findings of the first phase of the system development cycle.

The purpose of this paper is to discuss the case-based reasoning (CBR) approach to improve microcredit initiatives by means of providing a borrower risk rating system.

The CBR approach has been used to consider the Kiva microcredit system, which provides a characterization (rating) of the risk associated with the field partner supporting the loan, but not of the specific borrower which would benefit from it. The authors discuss how the combination of available historical data on loans and their outcomes (structured as a case base) and available knowledge on how to evaluate the risk associated with a loan request can be used to provide the end users with an indication of the risk rating associated with a loan request based on similar past situations.

The adopted approach is applied and evaluated employing a selection of cases from individual loans. From this perspective, the case base and the codified knowledge about how to evaluate risks associated with a loan represent two examples of knowledge IT artifacts.

The originality of the work lies in borrower risk rating in online indirect peer-to-peer microcredit lending platforms. The case base and the codified knowledge are the two contributions in knowledge IT artifacts.

This paper presents a hybrid artificial intelligence (AI) system capable of integrating techniques of case‐based reasoning, rule induction and expert system, using them for knowledge acquisition and problem solving of selecting appropriate retaining wall systems at the project planning stage. The proposed hybrid system can eliminate the bottleneck of knowledge acquisition in developing a knowledge‐based system and improve the solution quality of the AI‐based system. Test results indicate that solutions generated by the proposed hybrid system are better than those generated by using a single technique.

Many companies that were once centrally involved in the actual manufacture of products, and the delivery of their supporting services, now find themselves primarily engaged in integrating a number of other organizations, some of which they may own but many of which will be independent, each of which goes to make up a particular supply network. Consequently, continuously tracking performance of suppliers and an appropriate selection mechanism is one of the crucial activities in managing this supply network. This paper presents an intelligent generic supplier management tool (GSMT) using the case‐based reasoning (CBR) technique for outsourcing to suppliers and automating the decision‐making process when selecting them. The development of GSMT and how the CBR technique is applied is then given, followed by an application of GSMT in Honeywell Consumer Products (Hong Kong) Limited.

The rapid globalization in markets for manufactured goods and the growth of information technology has led to the demand for delivering superior value to international customers. Knowledge‐based systems, such as the case‐based reasoning (CBR) technique, have the potential to play a significant role in managing customer and supplier relationships under the dispersed network production systems model. The characteristic of the CBR technique in case retrieval, reuse, revision and retaining was used in solving a purchasing problem. However, there is a lack of literature in using a case‐based tool when comparing supplier bidding for the same order on a global basis. Proposes a case‐based supplier management tool (CBSMT) using the CBR technique in the areas of intelligent supplier selection and management that will enhance performance as compared to using the traditional approach. This CBSMT was first proposed as a task allocation tool, it was also used as a tool in a multi‐agent based virtual enterprise architecture. Describes an overview of the foundational issues related to CBR which provides some methodological approaches and exemplifies the current state through supplier management. Then defines a general supplier selection workflow, followed by the application of the case‐based technique to manage the problem of supplier selection. Finally, discusses an application case study of applying this supplier management tool to Honeywell Consumer Products (Hong Kong) Limited to help the selection of suppliers in their daily operations.

An intelligent supplier relationship management system (ISRMS) integrating a company’s customer relationship management (CRM) system, supplier rating system (SRS) and product coding system (PCS) by the case based reasoning (CBR) technique to select preferred suppliers during the new product development (NPD) process is discussed. By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, it is found that the outsource cycle time from the searching of potential suppliers to the allocation of order, as well as the delay in delivery of goods of suppliers after order allocation, are greatly reduced. In addition, performance of suppliers can be monitored effectively.

In increasingly competitive markets, customer satisfaction is a vital corporate objective. Key elements to increasing customer satisfaction include producing consistently high‐quality products and providing high‐quality customer service. Also, supplier relationship management (SRM) contributes to the supplier selection and increases the competitive advantage of manufacturers. SRM can enhance customer satisfaction and increase market share. Thus the development of a customer‐SRM system in the areas of outsourcing is essential for a company to remain competitive. Discusses an intelligent customer‐SRM system (ISRMS), using case‐based reasoning to help solve problems such as supplier selection and the help desk problem‐solving approach. By using ISRMS, companies can select the most suitable suppliers from the supplier list, as well as establishing a good customer‐supplier relationship between parties.

Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues.

Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem.

A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose.

The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.