Guangdong Tian

Fuzzy Petri nets for knowledge representation and reasoning

Fuzzy Petri nets (FPNs) are a potential modeling technique for knowledge representation and reasoning of rule-based expert systems. To date, many studies have focused on the improvement of FPNs and various new algorithms and models have been proposed in the literature to enhance the modeling power and applicability of FPNs. However, no systematic and comprehensive review has been provided for FPNs as knowledge representation formalisms. Giving this evolving research area, this work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms, knowledge representations and FPN models. In addition, we provide a survey of the applications of FPNs for solving practical problems in variety of fields. Finally, research trends in the current literature and potential directions for future investigations are pointed out, providing insights and robust roadmap for further studies in this field. We review the literature on FPNs published between 1988 and 2016.The reviewed papers are classified based on reasoning algorithms, knowledge representations and FPN models.A survey of the applications of FPNs for solving practical problems is provided.We offer directions for future research to improve the FPN performance.

AHP, Gray Correlation, and TOPSIS Combined Approach to Green Performance Evaluation of Design Alternatives

The green design of electromechanical products is a pivotal link to manufacturing industry. The question on how to design green products must be answered by excellent designers using both advanced design methods and effective assessment techniques of design alternatives. Making an objective and precise assessment of green designs is of increasing importance to ensure sustainable development. This work proposes a framework based on the combination of analytical hierarchy process (AHP), gray correlation (GC), and technique for order performance by similarity to ideal solution (TOPSIS) to evaluate the performance of design alternatives. AHP is used to determine the weights of performance indices and a nonlinear programming model with constraints is proposed to obtain the integrated closeness index based on the similarity closeness index from GC and distance closeness index from TOPSIS. A case study, i.e., three kinds of refrigerators, is illustrated to verify the proposed method. By comparing with existing methods, i.e., AHP-TOPSIS and AHP-GC, the effectiveness of the proposed method has been confirmed. In addition, sensitivity analysis is also provided in order to assess the robustness of the proposed method. Also, the following implication can be obtained from our results: 1) chloro–fluoro–carbons (mg/

A hybrid approach of rough set and case-based reasoning to remanufacturing process planning

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The construction and cost-benefit analysis of end-of-life vehicle disassembly plant: a typical case in China

) (C1), production noise (dB) (C6), and environmental cost (C16) have a large impact on refrigerator’s green design since these factors carry relatively larger weights. Results of the sensitivity analysis from different cases demonstrate that the best alternative may change when different weights are assigned to the evaluation criteria. This finding means the importance of establishing a qualified group of experts/designers in design evaluation and 2) the selection of design alternatives to produce a green product is critical to product development. The main contribution of this paper is the definition and development of an effective evaluation framework to guide managers to assess product design alternatives. The results show that it overcomes the one-sidedness of AHP-TOPSIS and AHP-GC, and makes the evaluation results more objective and realistic. It provides an accurate, effective, and systematic decision support tool for green performance evaluation of product design alternatives.

Multiobjective Optimization Models for Locating Vehicle Inspection Stations Subject to Stochastic Demand, Varying Velocity and Regional Constraints

Remanufacturing, a process returning used products to at least as good as new condition, is increasingly recognized as an important part of the circular economy. Since returned used components for remanufacturing have varying conditions and different defects, remanufacturing is very time-consuming and labor-intensive. There is an urgent need to reuse knowledge generated from existing parts remanufacturing to rapidly create sound process planning for the new arrival of used parts. A hybrid method combing rough set (RS) and cased-based reasoning (CBR) for remanufacturing process planning is presented in this paper. RS is employed for features reduction and rapid determination of features’ weights automatically, and CBR is utilized to calculate the similarity of process cases to identify the most suitable solution effectively from case database. The application of the methodology is demonstrated in an example of remanufacturing process for a saddle guide. The results indicated that the quality of remanufactured products has been improved significantly. The method has been implemented in a prototype system using Visual Studio 2010 and Microsoft SQL Server2008. The results suggested that the hybrid RS–CBR system is feasible and effective for the rapid generation of sound process planning for remanufacturing.

Environmentally friendly MCDM of reliability-based product optimisation combining DEMATEL-based ANP, interval uncertainty and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR)

With the rapid development of Chinese national economy, the number of car ownership in China increases sharply in last decades. More recently, as a consequence, a large number of end-of-life vehicles (ELVs) emerge. To meet the demands of environmental protection and cycling economy implementation, the development of ELV recycling industry is of primary importance and great urgency. In this context, we have performed the construction and investment analysis of ELV disassembly plant in China, on the basis of a typical case study. To match up the current ELV recycling industry in China, a new ELV disassembly process, with destructive disassembly and non-destructive one combined, was proposed and illustrated in detail. Building on the comprehensive subitems analysis, the investment and economic benefit were decomposed and calculated. The goal of this study is to develop a suitable ELV disassembly sequence for Chinese ELV disassembly industry and provide a beneficial reference for the investment of ELV recycling business. It represents a substantial innovation and will advance the development level of China’s ELV recycling industry.

Acquiring and Sharing Tacit Knowledge Based on Interval 2-Tuple Linguistic Assessments and Extended Fuzzy Petri Nets

Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.

Multiobjective Program and Hybrid Imperialist Competitive Algorithm for the Mixed-Model Two-Sided Assembly Lines Subject to Multiple Constraints

Environmentally friendly reliability-based product optimisation combining DANP, interval uncertainty and VIKOR is proposed.The proposed method conducts sufficient technical consideration of product environmental features.The organic combination of DANP with interval algorithms can handle the uncertainties and correlations among components.The organic integration of VIKOR can select the optimal reliability design scheme reasonably in the MCDM process.The superiority of the proposed method is verified by the case study of a waste tire shredder. With the far-reaching and overwhelming consequences resulting from energy crisis and carbon emissions, industrial products are required to be environmentally friendly, as well as of high quality and functionality. However, conventional reliability-based product optimisation methods cannot sufficiently ensure the environmental friendliness of modern industrial products. Firstly, the environmental features of a product are not considered. Secondly, the uncertainty of reliability-based product optimisation is not processed efficiently. Thirdly, no sufficient attention is being paid to capturing relationships among product components, despite such dependencies possibly exhibit a major impact on product functions. In order to address these issues, an environmentally friendly multi-criteria decision making (MCDM) model for reliability-based product optimisation is proposed by combining a decision-making trial and evaluation laboratory (DEMATEL)-based analytical network process (ANP) (DANP), interval uncertainty and the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The validity of this method is demonstrated by a numerical example.

A Cyber-Physical System for Product Conceptual Design Based on an Intelligent Psycho-Physiological Approach

In the highly competitive environment, capturing and disseminating of tacit knowledge are significant to an organization’s success with the development of knowledge-based systems. However, in pract...

Determining Truth Degrees of Input Places in Fuzzy Petri Nets

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature.