Hybrid Knowledge-based System Research Articles

Real-time pipe system installation schedule generation and optimization using artificial intelligence and heuristic techniques

Infrastructure systems in the United States are aging and considerable investment is needed to renew and replace a significant proportion of the existing systems. Piping systems, which are used in many infrastructure systems such as the distribution networks for utilities – water, sewage, gas, oil, etc., are very important in this regard. Real time scheduling is an important and necessary task in the planning and execution of construction projects. This is of particular importance in the installation of pipe systems, for which it is time consuming to plan and coordinate between team members the detailed requirements and information for the generation of practical installation schedules. During the installation stage, there can be delays or interference that could lead to the failure of the initial schedule plan. Current approaches are time-consuming, not automated and do not provide real-time schedules. Thus, the process is still fragmented and essentially manual, with inefficient information flow. To effectively improve the installation schedule, current knowledge of the installation site situation is important, with this knowledge being used to generate realistic schedules. Artificial intelligence (AI) maximizes the value of data by learning from previous cases and facilitates decision-making by making the process smarter and automatic. This paper proposes a new AI framework with machine learning (ML) and heuristic optimization techniques for automating practical pipe system installation schedule generation and optimization. A BIM model is used as reference to provide pipe system component information. A hybrid knowledge-based system is developed to integrate data-driven knowledge base and site-driven knowledge base on pipe system installation. K-Nearest Neighbor (KNN) and Graph Neural Network (GNN) ML techniques are adapted to map extracted components with the installation activities and their requirements for installation based on knowledge obtained from industry experts and piping codes. In addition, a heuristic algorithm is adopted to optimize the installation schedule. Finally, an optimal installation schedule that minimizes overlapping activities, time and cost is suggested.

A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization

Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS.

A Hybrid Knowledge-Based and Collaborative Filtering Recommender System Model for Recommending Interventions to Improve Elderly Wellbeing

A Hybrid Knowledge-Based and Collaborative Filtering Recommender System Model for Recommending Interventions to Improve Elderly Wellbeing

The design of a knowledge-based system for quality management in healthcare: case study

The current healthcare systems have numerous gaps that need to be filled to reach the best practice. This paper presents a novel approach to design and validate a hybrid knowledge-based system (KBS) to evaluate QM of healthcare environment (QMHE) using a hybrid system that has not been used before. It will be combined with gauge absence perquisite (GAP) method to sustain a successful operation of the large number of key performance indicators (KPIs) that involved in QMHE and to detect the gap between each KPI and the anticipated point. Out of 354 KB rules answered, the system has categorised 225 as GPs and the remaining 128 as BPs. The 128 bad points are categorised into different problem categories (20 PC-1, 34 PC-2, 34 PC-3, 40 PC-4, and 0 PC-5) where they represent the actions that need to be enhanced to reach the desired level of quality management.

Knowledge-based lean six sigma system for enhancing quality management performance in healthcare environment

PurposeThis paper aims to present the development of a knowledge-based system (KBS) to support the implementation of Lean Six Sigma (L6s) principles applied to enhance quality management (QM) performance within a health-care environment.Design/methodology/approachThe process of KBS building has been started by acquiring knowledge from experts in field of L6σ and QM in health care. The acquired knowledge has been represented in a rule-based approach for capturing L6σ practices. These rules are produced in IF […].THEN way where IF is the premise and THEN is the action. The produced rules have been integrated with gauging absence pre-requisites (GAP) technique to facilitate benchmarking of best practice in a health-care environment. A comprehensive review of the structure of the system is given, detailing a typical output of the KBS.FindingsImplementation of L6s principles to enhance QM performance in a health-care environment requires a pre-assessment of the organisation’s competences. The KBS provides an enhanced strategic and operational decision-making hierarchy for achieving a performance benchmark.Research limitations/implicationsThe KBS needs validation in real health-care environment, which will be done in Oman’s hospitals.Practical implicationsThe paper is intended to benefit QM practitioners in the health-care sector during decision-making to achieve performance improvement against a best practice benchmark.Originality/valueThis research presents a novel application of a hybrid KBS with GAP methodology to support the implementation of L6s principles to enhance QM performance in a health-care environment.

Evidence-based personal applications of medical computing models in risk factors of cardiovascular disease for the middle-aged and elderly

Medical applications on cardiovascular disease (CVD) for hybrid computing models are an emerging research area. The CVD, including stroke, hypertension, and high cholesterol, is one of 10 leading causes of death in Taiwan in middle-aged and elderly; in particular, the CVD has become the top killer in advanced countries. Thus, this serious but interesting issue triggers the study to focus on patients of the CVD. The study explores variables, influencing cardiovascular functions for four risk factors of blood pressure, blood glucose, blood fat, and kidney diseases, in the middle-aged and elderly. By the data collection of regular physical examination system from a regional hospital, the original dataset contains 52 variables collected from October 2011 to February 2014. We model a hybrid knowledge-based classification system to organize expert experiences, integrated linear and nonlinear attribute selection methods, data discretization of smart expert method, rough set theory, the LEM2 algorithm, and rule-filtering technique to classify the CVD for the early warning purpose. After data cleaning, 20 attributes with 2027 records are remained. For effectively identifying the variables of CVD subjects, this study reclassifies the above four risk diseases into three classes: no disease, 1&2 diseases, and 3&4 diseases. To verify performance of the proposed procedure, we experience an empirical experiment to compare the full 20 used attributes, the used attributes of integrated linear and nonlinear attribute selections with rule-filtering technique, and various classifiers. Conclusively, the 13 used attributes obtained from optimal accuracy become the key determinants that affect the four risk factors of the CVD. The empirical results and findings benefit doctors’ and medical institutions’ early medical recommendations and treatments with the advantages of significantly reducing morbidity of CVD.

Assessing quality management system at a tertiary hospital in Oman using a hybrid knowledge-based system

The cost of medical care is snowballing at an alarming and unmaintainable rate universally. Consequently, the need for a trusted quality management (QM) system at healthcare organizations is a must...

Ontology-Based Web Service Architecture for Retail Supply Chain Management

Service-oriented computing (SOC) technologies provide numerous opportunities and value-added service capabilities that global retail business requires to remain competitive in the market. Initiative to semantic web service provision is playing a crucial role to realize the possibility of heterogenous information systems integration in supply chain. The ability to dynamically discover and invoke a web service is an important aspects of semantic web service-based architecture. An essential part of the service discovery process is the ontology-based semantic web service matchmaking algorithm. This paper presents the key features of an improved matchmaking algorithm to calculate the similarity between concepts on ontology for semantic web service. The matchmaking is taking place in the context of OWL-S (Ontology Web Language for Services) based retail sales management. The paper describes the Semantic Web Service Architecture-II (SWSA-II), which uses a hybrid knowledge-based system; and it consists of Structural Case-Based Reasoning (S-CBR), Rule-Based Reasoning (RBR), and an ontological concept similarity assessment algorithm. Finally, a business scenario is used to demonstrate the functionality of the algorithm.

A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining

In recent years, there has been significant growth in the use of online learning resources by learners. However, due to information overload, many learners are experiencing difficulties in retrieving useful and relevant learning resources that meet their learning needs. Although existing recommender systems have recorded significant success in e-commerce domain, they still experience drawbacks in making accurate recommendations of learning resources in e-learning domain due to differences in learner characteristics such as learning style, knowledge level as well as learners’ sequential learning patterns. Most of the existing recommendation techniques do not consider differences in learner characteristics. This problem can be alleviated through incorporation of additional information about the learner into the recommendation process. Furthermore, many recommendation techniques experience cold-start and rating sparsity problems. In this paper, we propose a hybrid knowledge-based recommender system based on ontology and sequential pattern mining (SPM) for recommendation of e-learning resources to learners. In the proposed recommendation approach, ontology is used to model and represent the domain knowledge about the learner and learning resources whereas SPM algorithm discovers the learners’ sequential learning patterns. Our approach involves four steps: (1) creating ontology to represent knowledge about the learner and learning resources, (2) computing ratings similarity based on ontology domain knowledge and making predictions for the target learner, (3) generation of top N learning items by the collaborative filtering recommendation engine, and (4) application of SPM algorithm to the top N learning items to generate the final recommendations for the target learner. A number of experiments were carried out to evaluate the proposed hybrid recommender system and results show improved performance. Furthermore, the proposed hybrid approach can alleviate both the cold-start and data sparsity problems by making use of ontological domain knowledge and learner’s sequential access pattern respectively before the initial data to work on is available in the recommender system.

A Semantic Web Service Architecture for Supply Chain Management

Abstract: In recent years, manufacturing supply chains are inclined to be worldwide business activity due to economic advantage of product development and service deployment in globalized world. This worldwide nature encompasses of complex business interactions and interchange of information between geographically distributed enterprises in different business sectors. Semantic web service can provide appropriate information exchange architectures for global supply chain business operations. This paper describes the main features of an ontology-based Semantic Web Service Architecture (SWSA), for which a prototype system was built for material procurement systems of a manufacturing supply chain. Description Logics (DL) are used to represent the terminological knowledge of SWSA in a structured way. The architecture uses a hybrid knowledge-based system which consists of Structured Case-Based Reasoning (S-CBR), Rule-Based Reasoning (RBR), and a service concept matching algorithm. SWSA includes: (1) a collection of web service descriptions in Ontology Web Language-Based Service (OWL-S), (2) service advertisement using business Complex Concept (CC), and (3) a concept similarity assessment algorithm. Finally, an example of this concept similarity assessment algorithm is presented to demonstrate its functionality.

Development of a hybrid expert system-multi criteria analysis on erosion and sediment control in construction industry

Construction activities combined with heavy rainfall can results in severe soil loss which eventually will be deposited into the adjacent water bodies via stormwater. The Best Management Practices (BMPs) adopted to minimise the erosion and sedimentation during construction activities are usually determined by standard guidelines and expert engineers. However, when the expertise and data are limited, knowledge-based systems have been proved as an effective alternative in making decision. A new algorithm of hybrid knowledge-based expert system and Multi-Criteria Analysis (MCA) was developed to minimise erosion and sedimentation due to stormwater in Malaysian construction sites. In this Eco-Friendly Erosion and Sediment Control (ECO-ESC) system, decision tables were developed based on the knowledge acquired from the domain experts specialised in erosion and sedimentation control and guidelines. The MCA was used to identify the best stormwater control measures based on the specific criteria and criterion’s weight. The ECO-ESC was validated in three stages: preliminary by the experts, field and statistical validations. Results of comparison have shown high correlation for recommended BMPs, sediment yield, and the water quality monitoring and proved that the ECO-ESC performs as good as the human expert in solving different problems related to the erosion and sediment control.

Hybrid Knowledge-Based System for Collaborative Green Automotive Manufacturing Management

The objective of this research paper is to demonstrate the application of hybrid knowledge-based system, gauging absences of pre-requisites (GAP), and analytic hierarchy process (AHP) approaches for selecting the improvement programs for Collaborative Green Manufacturing Management (CGMM) system. In this research, a generic knowledge-based system is developed to measure the level of CGMM adoption in automotive manufacturers compared to the ideal system. Using the GAP and AHP tools, the key green manufacturing improvement programs can be prioritized and demonstrated with an illustrative example.

Planning and design of a knowledge based system for green manufacturing management

This paper presents a conceptual design approach to the development of a hybrid Knowledge Based (KB) system for Green Manufacturing Management (GMM) at the planning and design stages. The research concentrates on the GMM by using a hybrid KB system, which is a blend of KB system and Gauging Absences of Pre-requisites (GAP). The hybrid KB/GAP system identifies all potentials elements of green manufacturing management issues throughout the development of this system. The KB system used in the planning and design stages analyses the gap between the existing and the benchmark organizations for an effective implementation through the GAP analysis technique. The proposed KBGMM model at the design stage explores two components, namely Competitive Priority and Lean Environment modules. Through the simulated results, the KBGMM System has identified, for each modules and sub-module, the problem categories in a prioritized manner. The System finalized all the Bad Points (BP) that need to be improved to achieve benchmark implementation of GMM at the design stage. The System provides valuable decision making information for the planning and design a GMM in term of business organization.

Welfare - Oriented Public House Distribution System by Knowware System

This article purposes a solution of constructing hybrid knowledge-based system using intelligent components in Knowware System (KWS), for the recommendation of an optimal house distribution plan to management based on manager’s feedback. The target system is customized and can be easily modified or reconstructed according to manager’s specific need when relevant policies and constraints are considered.

Hybrid knowledge-based expert system and multi-criteria analysis for minimizing erosion and sedimentation due to stormwater in Malaysian construction sites

Malaysia is located in a tropical zone wherein heavy rainfall occurs in all seasons of the year. Land clearing and earthwork activities combined with heavy rainfall and the widely varied topography can result in severe soil loss that shall eventually be deposited into the adjacent water bodies via stormwater. Recently knowledge-based systems have been used in many fields especially when human expertise and data are limited. In the current study, a hybrid knowledge-based expert system and multi-criteria analysis (MCA) were developed to minimize erosion and sedimentation due to stormwater in Malaysian construction sites. This system called the Multi-criteria Erosion and Sediment Control (MCESC) system. The role of MCA is to identify the best stormwater control measure based on specified criteria and the criterion's weight. Data from road construction project in Permas Jaya, Johor Bahru State, Malaysia has been utilized for testing the MCESC system's functionality in solving real erosion and sediment control problem. The MCESC system can be used by engineers, contractors, and decision makers to develop erosion and sediment control plan and environmental management plan for any construction site in Malaysia.

The development of a hybrid knowledge-based system for the design of a Low Volume Automotive Manufacturing (LVAM) system

A conceptual design approach is an important stage for the development of a hybrid Knowledge-Based System (KBS) for Low Volume Automotive Manufacturing (LVAM). The development of a hybrid KBS, which is a blend of KBS and Gauging Absences of Pre-requisites (GAP), is proposed for LVAM research. The hybrid KB/GAP system identifies all potential elements of LVAM issues throughout the development of this system. The KBS used in the system design stage of the LVAM system analyses the gap between the existing and the benchmark organisations for an effective implementation through the GAP analysis technique. The proposed KBLVAM model at the design stage explores three major components, namely LVAM car body parts manufacturing perspective, LVAM competitive priorities perspective and LVAM lean environment perspective. Initial results reveal that the KBLVAM system has identified, for each perspective modules and sub-modules, the Problem Categories (PC) in a prioritised manner.

Knowledge Based System Implementation for Lean Process in Low Volume Automotive Manufacturing (LVAM) with Reference to Process Manufacturing

Global manufacturing industry mostly depends on new product development and processes to become competitive. The product development process for automotive industry is normally complicated, lengthy, expensive, and risky. Hence, a study of lean manufacturing processes for low volume manufacturing in automotive industry is proposed to overcome this issue by eliminating all wastes in the lengthy process. This paper presents a conceptual design approach to the development of a hybrid Knowledge Based (KB) system for lean process in Low Volume Automotive Manufacturing (LVAM). The research concentrates on the low volume processes by using a hybrid KB system, which is a blend of KB system and Gauging Absences of Pre-requisites (GAP). The hybrid KB/GAP system identifies all potential waste elements of low volume process manufacturing. The KB system analyses the difference between the existing and the benchmark standards for lean process for an effective implementation through the GAP analysis technique. The proposed model explores three major lean process components, namely Employee Involvement, Waste Elimination, and Kaizen (continuous improvement). These three components provide valuable information in order for decision makers to design and implement an optimised low volume manufacturing process, but which can be applied in all process manufacturing, including chemical processing.

Framework for evaluation and selection of the software packages: A hybrid knowledge based system approach

Evaluation and selection of the software packages is complicated and time consuming decision making process. Selection of inappropriate software package can turn out to be costly and adversely affects business processes and functioning of the organization. In this paper we describe (i) generic methodology for software selection, (ii) software evaluation criteria, and (iii) hybrid knowledge based system (HKBS) approach to assist decision makers in evaluation and selection of the software packages. The proposed HKBS approach employs an integrated rule based and case based reasoning techniques. Rule based reasoning is used to capture user needs of the software package and formulate a problem case. Case based reasoning is used to retrieve and compare candidate software packages with the user needs of the package. This paper also evaluates and compares HKBS approach with the widely used existing software evaluation techniques such as analytic hierarchy process (AHP) and weighted scoring method (WSM).

Increasing the managerial capabilities in Indonesian garment manufacturing

The paper aims at achieving two objectives: identifying why it is important to increase managerial capabilities in financial monitoring-and-evaluating activities in Indonesian Garment Medium Scale Manufacturers (IGMS); determining how a hybrid Knowledge-based System (KBS) can help improve the first objective. Observations and in-depth interviews are presented on four IGMSs. The analyses of literature review indicate that the integration of various Artificial Intelligence methods is an adequate approach for complex problem solving. This leads to an idea to combine the earlier KBS work with the more recent work of Financial Statements Analysis (FSA) methods.

Knowledge-Based Collaborative Lean Manufacturing Management (KBCLMM) System

Knowledge-Based Collaborative Lean Manufacturing Management (KBCLMM) SystemThe objective of this research paper is to demonstrate the application of hybrid Knowledge-Based System, Gauging Absences of Pre-Requisites (GAP), and Analytic Hierarchy Process (AHP) approaches for selecting the improvement programs for Collaborative Lean Manufacturing Management (CLMM) System. In this research, a generic Knowledge-Based System is developed to measure the level of CLMM adoption in automotive manufacturers compared to the ideal system. Using the embedded GAP and AHP technique, the key lean manufacturing improvement programs can be prioritised by using both qualitative and quantitative criteria. The analysis covers the planning stage of the KBCLMM. The utilisation of the approach is demonstrated with an illustrative example.