Jian-Bo Yang

The University of Manchester, Manchester, England, United Kingdom

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Publications (126)189.11 Total impact

  • Scientometrics 10/2015; DOI:10.1007/s11192-015-1770-8 · 2.18 Impact Factor
  • José M. Merigó · Jian-Bo Yang · Dong-Ling Xu ·

  • Guilan Kong · Dong-Ling Xu · Jian-Bo Yang · Xiemin Ma ·
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    ABSTRACT: Due to increasing demand for healthcare, medical quality has attracted significant attention in recent years. Most studies to date have tried to assess medical quality from objective quality indicators or subjective expert judgments or patient feedback perspective. In this study, the evidential reasoning approach is employed to combine objective quality indicators, subjective expert judgments and patient feedback in a multiple criteria framework to assess the quality of hospitals systematically and comprehensively. The evidential reasoning approach has the advantages of consistently handling both subjective evaluations and objective indicators under uncertainty within the same framework, and it can help to provide a robust alternative ranking. This study contributes to the literature with not only a novel medical quality assessment and aggregation framework, but also a pragmatic data transformation technique which can facilitate the combination of quantitative data and qualitative judgments using the evidential reasoning approach. A case study of three top-ranked teaching hospitals in Beijing is presented to demonstrate the framework and methodology proposed in this study.
    Expert Systems with Applications 08/2015; 42(13). DOI:10.1016/j.eswa.2015.03.009 · 2.24 Impact Factor
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    ABSTRACT: This paper aims to develop a data-driven approximate causal inference model using the newly-proposed evidential reasoning (ER) rule. The ER rule constitutes a generic conjunctive probabilistic reasoning process and generalises Dempster's rule and Bayesian inference. The belief rule based (BRB) methodology was developed to model complicated nonlinear causal relationships between antecedent attributes and consequents on the basis of the ER algorithm and traditional IF-THEN rule-based systems, and in essence it keeps methodological consistency with Bayesian Network (BN). In this paper, we firstly introduce the ER rule and then analyse its inference patterns with respect to the bounded sum of individual support and the orthogonal sum of collective support from multiple pieces of independent evidence. Furthermore, we propose an approximate causal inference model with the kernel mechanism of data-based approximate causal modelling and optimal learning. The exploratory approximate causal inference model inherits the main strengths of BN, BRB and relevant techniques, and can potentially extend the boundaries of applying approximate causal inference to complex decision and risk analysis, system identification, fault diagnosis, etc. A numerical study on the practical pipeline leak detection problem demonstrates the applicability and capability of the proposed data-driven approximate causal inference model.
  • Chao Fu · Jian-Bo Yang · Shan-Lin Yang ·
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    ABSTRACT: The reliability of an expert is an important concept in multiple attribute group decision analysis (MAGDA). However, reliability is rarely considered in MAGDA, or it may be simply assumed that all experts are fully reliable and thus their reliabilities do not need to be considered explicitly. In fact, any experts can only be bounded rational and their various degrees of reliabilities may significantly influence MAGDA results. In this paper, we propose a new method based on the evidential reasoning rule to explicitly measure the reliability of each expert in a group and use expert weights and reliabilities to combine expert assessments. Two sets of assessments, i.e., original assessments and updated assessments provided after group analysis and discussion are taken into account to measure expert reliabilities. When the assessments of some experts are incomplete while global ignorance is incurred, pairs of optimization problems are constructed to decide interval-valued expert reliabilities. The resulting expert reliabilities are applied to combine the expert assessments of alternatives on each attribute and then to generate the aggregated assessments of alternatives. An industry evaluation problem in Wuhu, a city in Anhui province of China is analyzed by using the proposed method as a real case study to demonstrate its detailed implementation process, validity, and applicability.
    European Journal of Operational Research 05/2015; 246(3). DOI:10.1016/j.ejor.2015.05.042 · 2.36 Impact Factor
  • Panitas Sureeyatanapas · Jian-Bo Yang · David Bamford ·
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    ABSTRACT: The assessment of corporate sustainability has become an increasingly important topic, both within academia and in industry. For manufacturing companies to conform to their commitments to sustainable development, a standard and reliable measurement framework is required. There is, however, a lack of sector-specific and empirical research in many areas, including the sugar industry. This paper presents an empirically developed framework for the assessment of corporate sustainability within the Thai sugar industry. Multiple case studies were conducted, and a survey using questionnaires was also employed to enhance the power of generalisation. The developed framework is an accurate and reliable measurement instrument of corporate sustainability, and guidelines to assess qualitative criteria are put forward. The proposed framework can be used for a company’s self-assessment and for guiding practitioners in performance improvement and policy decision-making.
    Production Planning and Control 04/2015; 26(13):1-17. DOI:10.1080/09537287.2015.1015470 · 1.47 Impact Factor
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    Panitas Sureeyatanapas · Jian-Bo Yang · David Bamford ·
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    ABSTRACT: As a consequence of an increasing demand in sustainable development for business organizations, the evaluation of corporate sustainability has become a topic intensively focused by academic researchers and business practitioners. Several techniques in the context of multiple criteria decision analysis (MCDA) have been suggested to facilitate the evaluation and the analysis of sustainability performance. However, due to the complexity of evaluation, such as a compilation of quantitative and qualitative measures, interrelationships among various sustainability criteria, the assessor's hesitation in scoring, or incomplete information, simple techniques may not be able to generate reliable results which can reflect the overall sustainability performance of a company. This paper proposes a series of mathematical formulations based upon the evidential reasoning (ER) approach which can be used to aggregate results from qualitative judgments with quantitative measurements under various types of complex and uncertain situations. The evaluation of corporate sustainability through the ER model is demonstrated using actual data generated from three sugar manufacturing companies in Thailand. The proposed model facilitates managers in analysing the performance and identifying improvement plans and goals. It also simplifies decision making related to sustainable development initiatives. The model can be generalized to a wider area of performance assessment, as well as to any cases of multiple criteria analysis.
    09/2014; 1(2):176-194. DOI:10.15302/J-FEM-2014025
  • Jian-Bo Yang · Dong-Ling Xu ·
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    ABSTRACT: Analysing performances for future improvement and resource planning is a key management function. Data Envelopment Analysis (DEA) provides an analytical mean for performance modelling without assuming parametric functions. Multiple Objective Optimisation (MOO) is well-suited for resource planning. This paper reports an investigation in exploring relationships between DEA and MOO models for equivalent efficiency analysis in a MOO process. It is shown that under certain conditions minimax reference point models are identical to input-oriented dual DEA models for performance assessment. The former can thus be used for Hybrid Efficiency and Trade-off Analyses (HETA). In this paper, these conditions are first established and the equivalent models are explored both analytically and graphically to better understand HETA. Further investigation in the equivalence models leads to the modification of efficiency measures and the development of a minimax reference point approach for supporting integrated performance analysis and resource planning, with the Decision Maker’s (DM) preferences taken into account in an interactive fashion. Both numerical and case studies are conducted to demonstrate the proposed approach and its potential applications.
    Computers & Operations Research 06/2014; 46:78–90. DOI:10.1016/j.cor.2014.01.002 · 1.86 Impact Factor
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    José M. Merigó · Montserrat Casanovas · Jian-Bo Yang ·
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    ABSTRACT: Expertons and uncertain aggregation operators are tools for dealing with imprecise information that can be assessed with interval numbers. This paper introduces the uncertain generalized probabilistic weighted averaging (UGPWA) operator. It is an aggregation operator that unifies the probability and the weighted average in the same formulation considering the degree of importance that each concept has in the aggregation. Moreover, it is able to assess uncertain environments that cannot be assessed with exact numbers but it is possible to use interval numbers. Thus, we can analyze imprecise information considering the minimum and the maximum result that may occur. Further extensions to this approach are presented including the quasi-arithmetic uncertain probabilistic weighted averaging operator and the uncertain generalized probabilistic weighted moving average. We analyze the applicability of this new approach in a group decision making problem by using the theory of expertons in strategic management.
    European Journal of Operational Research 05/2014; 235(1):215–224. DOI:10.1016/j.ejor.2013.10.011 · 2.36 Impact Factor
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    ABSTRACT: The objective of this paper is to construct reliable belief rule-based (BRB) models for the identification of uncertain nonlinear systems. The BRB methodology is developed from the evidential reasoning (ER) approach and traditional IF-THEN rule based system. It can be used to model complicated nonlinear causal relationships between antecedent attributes and consequents under different types of uncertainty. In a BRB model, various types of information and knowledge with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. In this paper, we first introduce the BRB methodology for modelling uncertain nonlinear systems. Then we present a comparative analysis of three BRB identification models through combining the BRB methodology with nonlinear optimisation techniques. The novel BRB identification models using l∞-norm and minimising mean uncertainties in belief rules (MUBR) show remarkable capabilities of capturing the lower and upper bounds of the interval outputs of uncertain nonlinear systems simultaneously. Trade-off analysis between identification accuracy and interval credibility are briefly discussed. Finally, a numerical study of a simplified car dynamics is conducted to demonstrate the capability and effectiveness of the BRB identification models for the modelling and identification of uncertain nonlinear systems.
    Knowledge-Based Systems 01/2014; 73. DOI:10.1016/j.knosys.2014.09.010 · 2.95 Impact Factor
  • Dong-ling Xu · Chris Foster · Ying Hu · Jian-bo Yang ·

    01/2014; 1(1):89. DOI:10.15302/J-FEM-2014015
  • Jian-Bo Yang · Dong-Ling Xu ·
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    ABSTRACT: This paper aims to establish a unique Evidential Reasoning (ER) rule to combine multiple pieces of independent evidence conjunctively with weights and reliabilities. The novel concept of Weighted Belief Distribution (WBD) is proposed and extended to WBD with Reliability (WBDR) to characterise evidence in complement of Belief Distribution (BD) introduced in Dempster–Shafer (D–S) theory of evidence. The implementation of the orthogonal sum operation on WBDs and WBDRs leads to the establishment of the new ER rule. The most important property of the new ER rule is that it constitutes a generic conjunctive probabilistic reasoning process, or a generalised Bayesian inference process. It is shown that the original ER algorithm is a special case of the ER rule when the reliability of evidence is equal to its weight and the weights of all pieces of evidence are normalised. It is proven that Dempsterʼs rule is also a special case of the ER rule when each piece of evidence is fully reliable. The ER rule completes and enhances Dempsterʼs rule by identifying how to combine pieces of fully reliable evidence that are highly or completely conflicting through a new reliability perturbation analysis. The main properties of the ER rule are explored to facilitate its applications. Several existing rules are discussed and compared with the ER rule. Numerical and simulation studies are conducted to show the features of the ER rule.
    Artificial Intelligence 12/2013; 205:1–29. DOI:10.1016/j.artint.2013.09.003 · 3.37 Impact Factor
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    ABSTRACT: Lymph Node Metastasis (LNM) in gastric cancer is an important prognostic factor regarding long-term survival. As it is difficult for doctors to combine multiple factors for a comprehensive analysis, Clinical Decision Support System (CDSS) is desired to help the analysis. In this paper, a novel Bi-level Belief Rule Based (BBRB) prototype CDSS is proposed. The CDSS consists of a two-layer Belief Rule Base (BRB) system. It can be used to handle uncertainty in both clinical data and specific domain knowledge. Initial BRBs are constructed by domain specific knowledge, which may not be accurate. Traditional methods for optimizing BRB are sensitive to initialization and are limited by their weak local searching abilities. In this paper, a new Clonal Selection Algorithm (CSA) is proposed to train a BRB system. Based on CSA, efficient global search can be achieved by reproducing individuals and selecting their improved maturated progenies after the affinity maturation process. The proposed prototype CDSS is validated using a set of real patient data and performs extremely well. In particular, BBRB is capable of providing more reliable and informative diagnosis than a single-layer BRB system in the case study. Compared with conventional optimization method, the new CSA could improve the diagnostic performance further by trying to avoid immature convergence to local optima.
    Knowledge-Based Systems 12/2013; 54:128–136. DOI:10.1016/j.knosys.2013.09.001 · 2.95 Impact Factor
  • José M. Merigé · Jian-Bo Yang · Dong-Ling Xu ·
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    ABSTRACT: The supply represents the available number of products of a specific variable. This paper presents a new approach for studying the supply through the use of aggregation systems. Its main advantage is that it permits to represent the information in a more complete way. Thus, it is possible to develop forecasting methods that consider optimistic and pessimistic scenarios and those that are more expected to occur. A wide range of aggregation operators are used giving special focus on the ordered weighted average (OWA). Several generalizations that use it with the weighted average and the probability are presented including the OWA weighted average (OWAWA) and the probabilistic OWAWA (POWAWA) operator. A numerical example is also presented.
    Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics; 10/2013
  • Emanuel-Emil Savan · Jian-Bo Yang · Dong-Ling Xu · Yu-Wang Chen ·
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    ABSTRACT: In this paper, a Genetic Algorithm (GA) search heuristic is proposed for validating the model-structure of Belief Rule-Based (BRB) methodologies. In order to ensure the balance between the model fit/ accuracy and the model complexity, the Akaike Information Criterion (AIC) is used in conjunction with the mentioned heuristic. The resulting framework is tested, using a model consisting of 3 inputs and one output, each of the 4 variables being allocated up to 5 referential values. The presented results illustrate the time-efficiency of the GA heuristic, as well as the penalty imposed by AIC on the number of parameters. The simplest model structure is indicated by AIC to be the optimal one. However, three additional model structures have been found to have AIC values which are moderately close to this optimum. An analysis of their coefficients of determination indicates a higher fit (than AIC optimum) on both testing sets and overall.
    Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics; 10/2013
  • Yu-Wang Chen · Jian-Bo Yang · Dong-Ling Xu ·
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    ABSTRACT: Belief rule-based (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information and knowledge with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. In this paper, we first review the scheme of belief rules for representing and inferring uncertain knowledge. Then we present two BRB system identification methods in which different training objectives are used. Finally, numerical studies are conducted to demonstrate the capability of BRB systems on uncertain nonlinear system modeling and identification.
    Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making; 07/2013
  • Yu-Wang Chen · Jian-Bo Yang · Dong-Ling Xu · Shan-Lin Yang ·
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    ABSTRACT: Belief rule based (BRB) system provides a generic inference framework for approximating complicated nonlinear causal relationships between antecedent inputs and output. It has been successfully applied to a wide range of areas, such as fault diagnosis, system identification and decision analysis. In this paper, we provide analytical and theoretical analyses on the inference and approximation properties of BRB systems. We first investigate the unified multi-model decomposition structure of BRB systems, under which the input space is partitioned into different local regions. Then we analyse the distributed approximation process of BRB systems. These analysis results unveil the underlying inference mechanisms that enable BRB systems to have superior approximation performances. Furthermore, by using the Stone–Weierstrass theorem, we constructively prove that BRB systems can approximate any continuous function on a compact set with arbitrary accuracy. This result provides a theoretical foundation for using and training BRB systems in practical applications. Finally, a numerical simulation study on the well-known benchmark nonlinear system identification problem of Box–Jenkins gas furnace is conducted to illustrate the validity of a BRB system and show its inference and approximation capability.
    Information Sciences 06/2013; 234:121–135. DOI:10.1016/j.ins.2013.01.022 · 4.04 Impact Factor
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    Mi Zhou · Xin-Bao Liu · Jian-Bo Yang · Chang Fang ·
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    ABSTRACT: Multiple attribute decision analysis (MADA) problems often include both qualitative and quantitative attributes which may be either precise or inaccurate. The evidential reasoning (ER) approach is one of reliable and rational methods for dealing with MADA problems and can generate aggregated assessments from a variety of attributes. In many real world decision situations, accurate assessments are difficult to provide such as in group decision situations. Extensive research in dealing with imprecise or uncertain belief structures has been conducted on the basis of the ER approach, such as interval belief degrees, interval weights and interval uncertainty. In this paper, the weights of attributes and utilities of evaluation grades are considered to be fuzzy numbers for the ER approach. Fuzzy analytic hierarchy process (FAHP) is used for generating triangular fuzzy weights for attributes from a triangular fuzzy judgment matrix provided by an expert. The weighted arithmetic mean method is proposed to aggregate the triangular fuzzy weights of attributes from a group of experts. α-cut is then used to transform the combined triangular fuzzy weights to interval weights for the purpose of dealing with the fuzzy type of weight and utility in a consistent way. Several pairs of group evidential reasoning based nonlinear programming models are then designed to calculate the global fuzzy belief degrees and the overall expected interval utilities of each alternative with interval weights and interval utilities as constraints. A case study is conducted to show the validity and effectiveness of the proposed approach and sensitivity analysis is also conducted on interval weights generated by different α-cuts.
    International Journal of Computational Intelligence Systems 03/2013; 6(3):423-441. DOI:10.1080/18756891.2013.780732 · 0.45 Impact Factor
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    ABSTRACT: A belief rule-based (BRB) system is a generic nonlinear modelling and inference scheme. It is based on the concept of belief structures and evidential reasoning (ER), and has been shown to be capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. The aim of this paper is to develop a BRB system that complements the RiskMetrics WealthBench system for portfolio optimisation with nonlinear cash-flows and constraints. Two optimisation methods are presented to locate efficient portfolios under different constraints specified by the investors. Numerical studies demonstrate the effectiveness and efficiency of the proposed methodology.
    European Journal of Operational Research 12/2012; 223(3):775–784. DOI:10.1016/j.ejor.2012.07.008 · 2.36 Impact Factor
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    Anil Kumar Maddulapalli · Jian-Bo Yang · Dong-Ling Xu ·
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    ABSTRACT: It is widely acknowledged that understanding and prioritizing the voice of customer is a critical step in new product development. In this work, we propose a novel approach to handle missing and incomplete data while combining information from different surveys for prioritizing customer voices. Our new approach comprises of the following stages: estimating and representing missing and incomplete data; estimating intervals for the criteria used in analyzing data; mapping data on criteria to a common scale; modeling interval data using interval belief structure; and aggregating evidence and ranking customer voices using the interval evidential reasoning algorithm. We demonstrate our approach using a case study from automotive domain with a given criteria hierarchy for analyzing data from three different surveys. We propose new optimization formulations for estimating intervals of the criteria used in our case study and logical yet pragmatic transformation functions for mapping criteria values to a common scale.
    European Journal of Operational Research 08/2012; 220(3):762–776. DOI:10.1016/j.ejor.2012.01.045 · 2.36 Impact Factor

Publication Stats

4k Citations
189.11 Total Impact Points


  • 1999-2015
    • The University of Manchester
      • Manchester Business School (MBS)
      Manchester, England, United Kingdom
  • 2006
    • Shanghai Jiao Tong University
      • Department of Automation
      Shanghai, Shanghai Shi, China
    • Nipissing University
      YYB, Ontario, Canada
  • 2005
    • Manchester University
      North Manchester, Indiana, United States