Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Influential factors in water planning for sustainable tourism destinations

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Sustainable Tourism on 28 Feb 2018, available online at: https://www.tandfonline.com/doi/full/10.1080/09669582.2018.1433183."Many destinations are implementing various water management alternatives to diminish the environmental impacts of tourism and increase sustainability. These efforts toward sustainability can be understood as part of corporate social responsibility (CSR) strategies implemented by tourism destinations. This paper is focused on the tourism destination of the Costa Brava (Catalonia, Spain) and proposes a method for selecting a list of influential factors in water management for sustainable tourism destinations by considering stakeholder preferences for technical, economic, social, political, and environmental factors. A new qualitative Delphi technique is used to identify a set of qualitative and quantitative factors by surveying eight stakeholders (six water management experts and two hotel managers). In addition, the study presents the weight for each of the influential factors that decision-makers – water planners, policy-makers, tourism destination managers and hotel managers – can use in assessing water management alternatives. Although research to date has addressed many aspects of responsible tourism, there is little literature on the importance of water management in responsible strategies for tourism destinations. This paper contributes to a more efficient selection of CSR strategies in tourism destinations by proposing a new methodology for identifying key factors for assessing sustainable solutions for water problems.Peer ReviewedPostprint (author's final draft

A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in cloud-based manufacturing is handling of datasets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such datasets. The framework proposed in this research uses a hybrid approach to deal with big dataset for smarter decisions. Furthermore, we compare the performance of radial basis function based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in cloud-based manufacturing, is to predict the effect of data errors on quality due to highly imbalance unstructured dataset. The proposed framework is an original contribution to the body of literature, where our proposed MapReduce framework has been used for fault detection by managing data imbalance problem appropriately and relating it to firm’s profit function. The experimental results are validated using a case study of steel plate manufacturing fault diagnosis, with crucial performance matrices such as accuracy, specificity and sensitivity. A comparative study shows that the methods used in the proposed framework outperform the traditional ones

Incomplete interval fuzzy preference relations and their applications

This paper investigates incomplete interval fuzzy preference relations. A characterization, which is proposed by Herrera-Viedma et al. (2004), of the additive consistency property of the fuzzy preference relations is extended to a more general case. This property is further generalized to interval fuzzy preference relations (IFPRs) based on additive transitivity. Subsequently, we examine how to characterize IFPR. Using these new characterizations, we propose a method to construct an additive consistent IFPR from a set of n − 1 preference data and an estimation algorithm for acceptable incomplete IFPRs with more known elements. Numerical examples are provided to illustrate the effectiveness and practicality of the solution process

Distance-based consensus models for fuzzy and multiplicative 3 preference relations

This paper proposes a distance-based consensus model for fuzzy preference relations where the weights of fuzzy preference relations are automatically determined. Two indices, an individual to group consensus index (ICI) and a group consensus index (GCI), are introduced. An iterative consensus reaching algorithm is presented and the process terminates until both the ICI and GCI are controlled within predefined thresholds. The model and algorithm are then extended to handle multiplicative preference relations. Finally, two examples are illustrated and comparative analyses demonstrate the effectiveness of the proposed methods

Cognition driven framework for improving collaborative working in construction projects: Negotiation perspective

Negotiation is the popular collaborative decision‐making behavior in inter‐organization systems, especially in the collaborative working in construction projects (CWCP). However, negotiation has long been recognized as a critical but time‐ and energy‐consuming process. The lack of an effective framework to improve the efficiency (performance) of negotiation is a major problem for those seeking to enhance the efficiency and effectiveness of collaborative working in construction projects. This paper aims to develop a cognitive mapping‐based application framework for improving collaborative working in construction project from negotiation perspective (CF‐CWCP). This framework includes two‐fold: (1) mapping negotiation process in construction projects using cognitive mapping technique; (2) developing CF‐CWCP by integrating intelligent agent and cognitive mapping techniques. This research will benefit the partners in construction projects to improve construction negotiation performance. A prototype of CF‐CWCP is developed. Santrauka Derybos yra populiarus bendradarbiavimu gristas tarimasis tarp organizaciniu sistemu priimti sprendi‐mus, ypač vykdant statybu projektus. Derybos jau seniai suvokiamos kaip vertingas, tačiau daug laiko ir energijos atimantis procesas. Veiksmingos sistemos, galinčios padeti pagerinti derybu efektyvuma, trūku‐mas yra viena iš pagrindiniu problemu siekiantiems padidinti bendradarbiavimo veiksminguma vykdant statybos projektus. Pagrindinis šio straipsnio tikslas ‐ išpletoti pažinimo kartografija paremtos sistemos, kuri pagerintuben‐dradarbiavima vykdant statybos projektus, taikyma atsižvelgiant i derybu perspektyvas. Šia sistema suda‐ro dvi dalys: 1) kartografinis derybu procesas vykdant statybos projektus, pagristas pažinimo kartografijos technologija; 2) pažinimo sistemos, gerinančios bendradarbiavima vykdant statybos projektus, pletojimas integruojant intelektinius agentus ir pažinimo kartografijos technologija. Šis tyrimas pades statybu projek‐tu dalyviams pagerinti derybu efektyvuma, be to, išpletotas pažinimo sistemos prototipas. First Published Online: 09 Jun 2011 Reikšminiai žodžiai: pažinimo kartografija, bendradarbiavimas, derybos, statybos projekta

Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors

This work was supported in part by the NSF of China under grants 71171160 and 71571124, in part by the SSEM Key Research Center at Sichuan Province under grant xq15b01, in part by the FEDER funds under grant TIN2013-40658-P, and in part by Andalusian Excellence Project under grant TIC-5991.The consensus reaching process (CRP) is a dynamic and iterative process for improving the consensus level among experts in group decision making. A large number of non-cooperative behaviors exist in the CRP. For example, some experts will express their opinions dishonestly or refuse to change their opinions to further their own interests. In this study, we propose a novel consensus framework for managing non-cooperative behaviors. In the proposed framework, a self-management mechanism to generate experts' weights dynamically is presented and then integrated into the CRP. This self-management mechanism is based on multi-attribute mutual evaluation matrices (MMEMs). During the CRP, the experts can provide and update their MMEMs regarding the experts' performances (e.g., professional skill, cooperation, and fairness), and the experts' weights are dynamically derived from the MMEMs. Detailed simulation experiments and comparison analysis are presented to justify the validity of the proposed consensus framework in managing the non-cooperative behaviors.National Natural Science Foundation of China 71171160 71571124SSEM Key Research Center at Sichuan Province xq15b01European Union (EU) TIN2013-40658-PAndalusian Excellence Project TIC-599