Combination Of Case-based Reasoning Research Articles

Intelligent green retrofitting of existing buildings based on case-based reasoning and random forest

The decision-making on green retrofitting of existing buildings relies on both explicit and implicit knowledge, and its efficiency and reliability need improvement. Intelligent approaches that can sufficiently utilize the text information of existing projects are required to provide more suitable strategies for green retrofitting. This paper describes a decision-making approach combining Case-Based Reasoning (CBR) and Random Forest (RF), which can identify similar cases from the database containing 109 green retrofit projects and revise outdated measures. A practical project case study shows that the revised retrofit measures can reduce Energy Use Intensity (EUI) by 37%. The proposed approach optimizes and standardizes CBR processes and provides guidance for coping with semi-structured green retrofit decision-making problems, thereby promoting sustainable development and intelligent management in the construction field. The system prototype will be developed and promoted after the case database is expanded.

Risk Cost Measurement of Value for Money Evaluation Based on Case-Based Reasoning and Ontology: A Case Study of the Urban Rail Transit Public-Private Partnership Projects in China

Risk is demonstrated as one of the most crucial drivers of value for money (VFM) in public–private partnerships (PPP), but in previous studies, the risk cost estimation of the quantitative evaluation of VFM was still a dilemma that strongly depended on specialist discretion or had low methodological operability. This paper establishes a prediction model for estimating the risk cost in the phase of VFM evaluation through a combination of case-based reasoning (CBR) and ontology technology. PPP information ontology was established to provide the technical basis of knowledge representation for the CBR cycle. Then, according to whether the information data were quantitative or qualitative, similarity calculation methods were used for the retrieval of similar cases. The conceptual semantic similarity algorithm based on the ontology tree structure was well implemented to compare abstract information. After the most similar cases were extracted, a revision mechanism was followed when there were deviations in the similar cases. Finally, the risk costs of the target case were obtained by weighting the extracted similar cases based on the similarity. An empirical analysis was performed with 18 historical projects from the China Public–Private Partnerships Center. The results showed that the relative errors between the estimated and actual costs of total risk and retained risk were 11.05% and 2.41%, respectively. This indicates that the estimation model could achieve a better risk cost prediction with small errors, which validates the availability of the model. Based on the proposed model, this research establishes an extensible PPP information ontology model. It promotes the integration and interoperability of information knowledge in the PPP domain, which can be further expanded according to the requirements. Coherent accuracy is provided by the whole CBR-based measurement process, which has offered a systematic and objective method for the risk costs measurement of PPP projects.

Mitigating bullwhip effect in an agent-based supply chain through a fuzzy reverse ultimatum game negotiation module

Supply Chain Management is frequently regarded as a distributed system whose productivity is mainly influenced by healthy interaction and cooperation among members. Examples of such inefficiencies can be seen in “Demand amplification” referring to asymmetric increase of demands among supply echelons due to both operational and behavioral causes. This paper addresses defective loops of non-cooperation as well as absence of information to manage the dynamics of demand amplification in a four-echelon supply chain; a new agent-based structure is suggested to facilitate the cooperation and coordination among major components and provide a structured context for interactive information sharing. Adequate motivation to share the required information is derived from an automated negotiation between retailer and manufacturer echelons in a four-echelon serial supply model. In doing so, the retailer agent would be encouraged to share customer demand information using Token-Based ordering policy, through a Reverse Ultimatum Game negotiation module. A novel fuzzy approach is suggested in order to cope with ambiguities involved in this negotiation; Numerical experiments prove that the proposed fuzzy negotiation mechanism can warrant the agreement among negotiating parties in nearly half the number of RUG iterations with 30% agreement share. This success in bringing the negotiation parties to an agreement results in the bullwhip effect decreasing by 30% in a four-echelon agent-based supply system.The proposed agent-based supply model which is designed to facilitate this cooperative decision-making process includes some unique innovative features: the knowledge base of the proposed system is able to retrieve and reuse the previous negotiation outcomes, through a gradual learning module. A combination of Case-Based Reasoning and Rule-Based inference mechanisms are applied to facilitate this, so prior cases will be stored in a Frame-Based structure. Eventually, the integrity of the proposed agent-based system is examined through combining Top-Down and Subsets integration approaches and some numerical experiments are provided to confirm the efficiency of the proposed agent-based structure in bullwhip effect management.

Case-based reasoning adaptation based on fuzzy gravitational search algorithm for disaster emergency plan

Disasters can result in substantial destructive damages to the world. Emergency plan is vital to deal with these disasters. It is still difficult for the traditional CBR to generate emergency plans to meet requirements of rapid responses. An integrated system including Case-based reasoning (CBR) and gravitational search algorithm (GSA) is proposed to generate the disaster emergency plan. Fuzzy GSA (FGSA) is developed to enhance the convergence ability and accomplish the case adaptation in CBR. The proposed algorithm dynamically updates the main parameters of GSA by introducing a fuzzy system. The FGSA-CBR system is proposed, in which fitness function is defined based on the effectiveness of disaster emergency management. The comparison results have revealed that the proposed algorithm has good performances compared with the original GSA and other algorithms. A gas leakage accident is taken as an empirical study. The results have demonstrated that the FGSA-CBR has good performances when generating the disaster emergency plan. The combination of CBR and FGSA can realize the case adaptation, which provides a useful approach to the real applications.

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

In nowadays industry 4.0 and changeable manufacturing context, designers and manufacturing engineers struggle to determine appropriate quick, accurate (with flawless quality), and cost-effective processes to design highly customized products to meet customer requirements. To determine manufacturing processes, the matching between product features, material characteristics, and process capabilities needs to be optimized. Finding such an optimized matching is usually referred to as manufacturing process selection (MPS), which is not an easy task because of the infinite combinations of product features, numerous material characteristics, and various manufacturing processes. Although problems associated with MPS have received considerable attention, semantic web technologies are still underexplored and their potential is still uncovered. Almost no previous study has considered combining case-based reasoning (CBR) with ontologies, a famous and powerful semantic web enabler, to achieve MPS. In this study, we developed a decision support system (DSS) for MPS based on ontology-enabled CBR. By applying automatic reasoning and similarity retrieving on an industrial case study, we show that ontologies enable process selection by determining competitive matching between product features, material characteristics, and process capabilities and by endorsing effective case retrieval.

Final and dynamic mortality prediction from earthquakes in China

This paper aims at predicting the dynamic death tolls in earthquakes using a combination of case-based reasoning and a times series model from realistic perspective. The proposed methodology consists of three steps: 1) fuzzy clustering to classify Chinese earthquakes; 2) mortality rates calculating to predict the final death tolls; 3) time series modelling to predict dynamic number of deaths. The strength of this paper is that it provides scientific methods with overall forecast errors lower than 20%, and opens the door for conducting final and dynamic death forecasts with a qualitative and quantitative approach.

Final and dynamic mortality prediction from earthquakes in China

This paper aims at predicting the dynamic death tolls in earthquakes using a combination of case-based reasoning and a times series model from realistic perspective. The proposed methodology consists of three steps: 1) fuzzy clustering to classify Chinese earthquakes; 2) mortality rates calculating to predict the final death tolls; 3) time series modelling to predict dynamic number of deaths. The strength of this paper is that it provides scientific methods with overall forecast errors lower than 20%, and opens the door for conducting final and dynamic death forecasts with a qualitative and quantitative approach.

An integrated method for matching forest machinery and a weight-value adjustment

Proper matching of forestry machinery is important when raising mechanization levels for forestry production. In the matching process, forestry machinery needs not only expertise, but also improved methods for solving problems. I propose combination of case-based reasoning (CBR) and rule-based reasoning (RBR) by calculating the similarity of quantitative parameters of various forestry machines in an analytical and hierarchical process. I calculated the similarity of machinery used in forest industries to enable better selection and matching of equipment. I propose a weight-value adjusting method based on sums of squares of deviations in which the individual parameter weights were modified in the process of application. During the process of system design, I put forward a design method knowledge base and generated a dynamic web reasoning framework to integrate the processes of forest industry machinery selection and weight-value adjustment. This enables expansion of the scope of the complete system and enhancement of the reasoning efficiency. I demonstrate the validity and practicability of this method using a practical example.

Hybrid Intelligent Recommending System for Process Parameters in Differential Pressure Vacuum Casting

The determination of process parameters in Differential Pressure Vacuum Casting (DPVC) process depends on the technologist’s experience, and thus the optimized ones are usually determined through repeated molding trial and repairing process. However, this can result in problems of long production period and high cost. So, combining case based reasoning (CBR), neural network 、agent model and fuzzy inference, a hybrid intelligent model is proposed herein to solve this problem. First, the CBR strategy is adopted for setting the initial process parameters by simulating the technologist’s reaction where technologists determines the optimized parameters by referencing highly similar case from past experience. If the CBR fails, the neural network reasoning (NNR) strategy is applied to determine the initial process parameters by imitating technologist’s “experience reasoning” process; if no similar case exists, the agent model reasoning (AMR ) strategy is applied to determine the initial parameters. Finally, a fuzzy inference (FI) based on expert knowledge is developed for revising defects and optimizing process parameters during the molding trial process until the part quality can meet the requirements. Based on the intelligent model, the corresponding software system is developed, and the experiment results show that the system is effective and can be applied to practical production.

A case-based reasoning system for adapting selling

Quick and accurate adapting selling can boost business results. This study developed an adapting selling solution by combining case-based reasoning (CBR) and rough set theory for industrial proposal generation in business-to-business (B2B) context. In this regard, CBR was developed to find the right proposal by considering users’ needs. Rough set theory is then developed to find the proper attribute weights for CBR retrieval phase. Further, when cases have same problem features or they have same similarity values, it is not possible to select one case among the retrieved cases. For this purpose, this study presented a new method to discriminate equally retrieved cases. Sales data of a steel manufacturing company are used and inputted into the CBR system. Practical application of the proposed system illustrated efficacy of the procedures and algorithms.

Parameter Optimization in Cutting Process Database Based on Compounded Reasoning

Cutting process consists of many interaction parameters, the interrelationship of these parameters is developed, the cutting process database based on case and optimization is discussed. The cutting parameters are provided by means of the combination of case-based reasoning and optimization, the case database is established with experience, the cutting parameters are proposed, the optimization is applied when no information is in the case database, and the new data will be saved as a new case after it is verified by manufactured. The selection efficiency and precision of cutting parameters are improved by the means of case characteristics similarity calculation retrieval. The program is expansible in the way of open case storage.

Toward a user-oriented recommendation system for real estate websites

The Internet has become a significant transaction platform for the real estate industry. However, use of the Internet does not benefit homebuyers in terms of search time, flexibility, and intuitive results. While it does encourage buyers to search more intensively, and discover and visit more properties, it also wastes more time and energy. To improve the efficiency of real estate searches, we developed an online homebuyer’s search program, based on an investigation of search behaviors, and implement a user-oriented recommendation system for real estate websites via a combination of case-based reasoning and an ontological structure. An ontological structure is employed to improve information management efficiency while case-based reasoning improves recommendation accuracy. A user test demonstrates the effectiveness of the proposed system and validates the findings of this study. The limitations of the current study are also discussed for future research and applications.

Modeling Total Quality Management in Higher Education with Case-Based Reasoning

Total quality management (TQM) is a philosophy and a system for continuously improving the services and products offered to customers through ongoing refinements in response to continuous feedback. With regards to higher education, quality education needs to be emphasized in ensuring progressive intellectual capacity building. Both quality assurance and management are critical in ensuring that higher learning institutions provide better services to their primary customers. The continuous improvement and growth focus of TQM would offer more excitement and challenges to students and lecturers as compared to a “good enough” traditional learning environment. In this paper, elements of an education system that focus on total quality assurance and management are discussed. This paper emphasizes on the mastery learning approach, which is in line with TQM. The learning process contains the following steps: Plan, teach (Do), Check (formative evaluation), revised teaching (Act), and Test (summative evaluation). The two main objectives of using TQM in education, which are to improve learning and to improve cost effectiveness, are emphasized. The paper concludes with a TQM system model that applies genetic algorithm (GA) in case-based reasoning (CBR) that can be used as guidelines to total quality assurance and management in higher education. The combination of CBR and GA will produce a better TQM system model as compared to the one introduced earlier in a previous paper. It is believed that this newly proposed system model is useful in building intellectual capacity in higher education.

Case-based reasoning combined with statistics for diagnostics and prognosis

Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features.

A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center

Objective With the increased complexity and uncertainty in drug information, issuing medical prescriptions has become a vexing issue. As many as 240,000 medicines are available on the market, so this paper proposes a novel approach to the issuing of medical prescriptions. The proposed process will provide general practitioners (GPs) with medication advice and suggest a range of medicines for specific medical conditions by taking into consideration the collective pattern as well as the individual preferences of physicians’ prescription decisions. Methods and material A hybrid approach is described that uses a combination of case-based reasoning (CBR) and Bayesian reasoning. In the CBR process, all the previous knowledge retrieved via similarity measures is made available for the reference of physicians as to what medicines have been prescribed (to a particular patient) in the past. After obtaining the results from CBR, Bayesian reasoning is then applied to model the prescription experience of all physicians within the organization. By comparing the two sets of results, more refined recommendations on a range of medicines are suggested along with the ranking for each recommendation. Results To validate the proposed approach, a Hong Kong medical center was selected as a testing site. Through application of the hybrid approach in the medical center for a period of one month, the results demonstrated that the approach produced satisfactory performance in terms of user satisfaction, ease of use, flexibility and effectiveness. In addition, the proposed approach yields better results and a faster learning rate than when either CBR or Bayesian reasoning are applied alone. Conclusion Even with the help of a decision support system, the current approach to anticipating what drugs are to be prescribed is not flexible enough to cater for individual preferences of GPs, and provides little support for managing complex and dynamic changes in drug information. Therefore, with the increase in the amount of information about drugs, it is extremely difficult for physicians to write a good prescription. By integrating CBR and Bayesian reasoning, the general practitioners’ prescription practices can be retrieved and compared with the collective prescription experience as modeled by probabilistic reasoning. As a result, physicians can select the drugs which are supported by informed evidential decisions. That is, they can take into consideration the pattern of decisions made by other physicians in similar cases.

Hybrid neural network and case based reasoning system for Web user behavior clustering and classification

In this paper we present Casep2: a hybrid neuro-symbolic system combining case-based reasoning (CBR) and artificial neural networks that aims at clustering and classifying users' behavior in an e-commerce site. A user behavior is represented by a seq

Combination of case-based reasoning and analytical hierarchy process for providing intelligent decision support for product recycling strategies

Waste electric and electronic equipment (WEEE) may cause environmental problems during the waste management phase if it is not designed to be environment friendly. Many governments have made considerable efforts to frame policies to reduce the use of hazardous materials and improve a product’s recyclability. In this research, recyclability parameters are introduced to represent a product component’s recyclability and to aid a designer in evaluating the component’s recyclability. The recyclability index is defined as a measure of the ability of a material to regain its valued properties when subjected to a special recycling process. A method is proposed to determine the recyclability of a material during the design phase. This research method combines case-based reasoning (CBR) and the analytical hierarchy process to simplify the calculation of the recyclability index for a product. Designers can extract experiences from past cases to (1) calculate the recyclability rate of a designed product, (2) determine the possibility of recycling end-of-life (EOL) products, and (3) finalise a recycling plan for EOL products when including a new product in a recycling and treatment system.

A hybrid algorithm of improved case-based reasoning and multi-attribute decision making in fuzzy environment for investment loan evaluation

This paper presents a model to support the banking managerial decisions in the evaluation of investment plans, especially on rejecting inappropriate plans that can be done in short time (less than hour) and with minimal cost. Because there are some uncertainties in the evaluation process, our proposed model utilises fuzzy set theory to define the problem space in which an acceptance or rejection decision for a submitted investment plan is made. The model is based on lessons-learned concept and developed through the combination of case-based reasoning (CBR) and multiple attribute decision making in fuzzy environment. The model uses an enhanced version of CBR in which a novel concept as solution's truth value is implemented. A set of investment plans is evaluated to show the applicability and efficiency of the model. Different scenarios in terms of sensitivity analysis are also mentioned to capture managerial insights. Comparing the obtained results of the model with those of other algorithms shows its better proximity to human reasoning and decision making.

A New Path Estimation Strategy for Predicting Blind Persons′ Motion in Indoor Environments

Research on the characteristics of spatial cognition without vision is used to improve the design of indoor environments to be safer for blind and visually handicapped persons. A fuzzy cognitive map (FCM) decision mechanism is presented for modeling path planning strategies adopted by blind travelers including wall-following, and shortcutting through the environment. A statistical case based reasoning (CBR) strategy is introduced for anticipating the points of switch between the two mentioned behaviours along the path. The combination of CBR and FCM modules provided a robust model of decision making which can be used for predicting blind motions. In this research, 51 eye-masked subjects contributed for obtaining the path patterns and for validating the results obtained using the proposed path prediction approach.

Decision support for housing customization: A hybrid approach using case-based reasoning and genetic algorithm

Abstract Customization has been an important business strategy for market promotion. The customization of housing, however, requires intensive communication between the customer and the supplier, as well as complicated processes in design and construction phases. Non-professional customers' inability to analyze the cost and quality of customization may produce difficulty in decision making and discrepancies between expectation and result, which will eventually give rise to dissatisfaction with the service and the product. This paper presents a system to support decision making in housing customization using a hybrid approach combining case-based reasoning (CBR) and genetic algorithm (GA). With this system, a customer is able to participate in the design process by customizing the house according to one's preferred layout, finishing and budget without time-consuming communication with professional designers. The paper concludes that the method should make customization efficient and profitable when applied to pre-sale housing in Taiwan.