The evolution of the Internet of Things (IoT) over the past 20 years

Two-sided matching decision making (TSMDM) problems exist widely in human being’s daily life. For practical TSMDM problems, matching objects with different culture and knowledge backgrounds usually tend to provide linguistic assessments using different linguistic term sets (i.e., multi-granular linguistic information). Moreover, for TSMDM problems with high uncertainty, it is possible that matching objects may have some hesitancy and thus provide hesitant fuzzy linguistic term sets (HFLTSs). To model these situations, an approach to TSMDM with multi-granular HFLTSs is developed in the paper. In the proposed approach, some optimization models are first constructed to determine criteria weights for matching objects who do not provide clear criteria weight vectors. Afterwards, each matching object’s hesitant fuzzy linguistic decision matrix is aggregated to obtain his/her collective assessments over matching objects on the other side, which are denoted by multi-granular linguistic distribution assessments. These multi-granular linguistic distribution assessments are unified to obtain matching objects’ satisfaction degrees. Furthermore, an optimization model which aims to maximize the overall satisfaction degree of matching objects by considering the stable matching condition is then established and solved to determine the matching between matching objects. Eventually, an example for the matching of green building technology supply and demand is provided to demonstrate the characteristics of the proposed approach. Compared with previous studies, the proposed approach allows matching objects to provide linguistic assessments flexibly and can deal with the situations when incomplete criteria weight information is provided.

Two-sided matching decision making with multi-granular hesitant fuzzy linguistic term sets and incomplete criteria weight information

Owing to the complexity of socio-economic environments and the fuzziness of human cognition, information of cognitive preference provided by decision-making organizations composed of many experts is often hesitant and fuzzy. In consequence, for the sake of addressing the hesitance and fuzziness of preference information for the configuration of tasks and resources in cloud manufacturing, a decision-making model of a two-sided matching considering a bidirectional projection under preference information of hesitant fuzzy is put forward. Primarily, this paper describes the problem of two-sided matching and introduces the hesitant fuzzy set. Afterwards, according to preference information given by matching agents using the hesitant fuzzy element, the evaluation matrix is constructed. Meanwhile, the bidirectional projection technology and TOPSIS method are combined to calculate the closeness degree matrix. Further, by introducing the constraint of the stable matching, a decision-making model of a two-sided matching for maximizing the closeness degree of two-sided matching agents is constructed, and the optimal configuration results are obtained by the solving of the model. Subsequently, the illustrative case is provided to validate the rationality and effectiveness of the presented model in solving the configuration for cloud manufacturing tasks and resources. Also, the stability in the proposed configuration results is illustrated by making a sensitivity analysis. Further, the reliability in the given solving process is demonstrated through performing a comparative analysis, as well as the advantages in the proposed model is also discussed. It shows that this developed model can give stable configuration results and also provide a matching approach for different agents under an uncertain environment.

Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources

Two-sided matching and game on investing in carbon emission reduction technology under a cap-and-trade system

Matching-game approach for green technology investment strategies in a supply chain under environmental regulations

Due to the increasing complexity in socioeconomic environments and the fuzziness in human cognition, the cognitive information over alternatives provided by a decision organization consisting of several experts is usually uncertain and hesitant. Consequently, in order to solve the fuzziness and uncertainty of preference information in the matching process of complex product manufacturing tasks on the cloud manufacturing platform, a novel two-sided matching model based on dual hesitant fuzzy preference information is proposed. Firstly, the two-sided matching problem and the dual hesitant fuzzy set (DHFS) are described. Then, the dual hesitant fuzzy preference information evaluation matrix is constructed and normalized according to the preference information provided by agents on both sides. Sequentially, the dual hesitant fuzzy preference information evaluation matrix is transformed into the satisfaction degree matrix by the projection technology. Simultaneously, considering the stable matching constraint, a multi-objective two-sided matching optimization model which maximizes the satisfaction degree and minimizes the difference degree of two-sided agents is established. Further, the matching relative competition degree is used to convert the multi-objective optimization model into a single-objective optimization model, and the matching algorithm is designed for solving the model. Moreover, an illustrative example is employed to demonstrate the practicality and feasibility of the developed model. Subsequently, the sensitivity analysis is performed to validate the stability of the proposed matching results, and the comparative analysis is carried out to illustrate the reliability of the proposed matching algorithm and the merits of the developed model. It reveals that the proposed model can not only obtain stable matching results but also give more choices in matching methods.

Two-sided matching model for complex product manufacturing tasks based on dual hesitant fuzzy preference information

Changes in customer needs are unavoidable during the design process of complex mechanical products, and may bring severely negative impacts on product design, such as extra costs and delays. One of the effective ways to prevent and reduce these negative impacts is to evaluate and manage the core parts of the product. Therefore, in this paper, a modified Dempster-Shafer (D-S) evidential approach is proposed for identifying the core parts. Firstly, an undirected weighted network model is constructed to systematically describe the product structure. Secondly, a modified D-S evidential approach is proposed to systematically and scientifically evaluate the core parts, which takes into account the degree of the nodes, the weights of the nodes, the positions of the nodes, and the global information of the network. Finally, the evaluation of the core parts of a wind turbine is carried out to illustrate the effectiveness of the proposed method in the paper. The results show that the modified D-S evidential approach achieves better performance regarding the evaluation of core parts than the node degree centrality measure, node betweenness centrality measure, and node closeness centrality measure.

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Identifying Core Parts in Complex Mechanical Product for Change Management and Sustainable Design

For the multiple attribute decision-making problem, the decision-making approach which considers hesitant fuzzy decision information and unknown attribute weights is investigated. Primarily, the formed vectors of alternative, positive and negative ideal direction are defined. Subsequently, a bidirectional projection based on hesitant fuzzy information is established. Simultaneously, the improved closeness degree equation is proposed. Further, an attribute weight determination model which maximizes the closeness degree and entropy is constructed. In the last, an illustrative example is provided to demonstrate the validity and feasibility of the proposed approach.

A Decision Making Approach under Hesitant Fuzzy Information

The U-type assembly line is currently the focus research in production enterprises. However, the traditional assembly line balancing mostly optimizes the single objective ignoring the relationships between each other. Therefore, through the transformation of assembly sequence, a multiple objective mathematical model of the U-type assembly line is established based on the present analysis of enterprise assembly line process by maximizing the balance efficiency, optimizing the workstation load and increasing capacity. Then, the mathematical model is solved by lingo software to find the global optimal solution. In a practical application, we selected five different workstations in the Lingo software platform, calculated the loading effect of balance, and then compared U-type to straight assembly line to verify the feasibility and effectiveness of the proposed models and method in this research.

Baodong Li

Papers

Two-sided matching model for complex product manufacturing tasks based on dual hesitant fuzzy preference information

Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources

Identifying Core Parts in Complex Mechanical Product for Change Management and Sustainable Design

A Decision Making Approach under Hesitant Fuzzy Information

A method on multiple objective balancing of U-type assembly line