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

Li, Baodong; Yang, Yu; Su, Jiafu; Zhang, Na; Wang, Sheng

doi:10.1016/j.knosys.2019.104989

Cited by 41 publications

(39 citation statements)

References 47 publications

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“…We designed a calculation method for the proportion of similar matching pairs (similarity rate) between two different data sets and the average similarity rate of matching results between these data sets, and expressed with Eqs. (34) and (35). In the equation, NS ij and NT i represent the number of similar matching pairs between dataseti and datasetj, and the number of matching pairs of each data set, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Based on Eqs. (34) and (35), we design an algorithm to calculate the similarity rate of matching results (Algorithm 1). (34)…”

Section: Discussionmentioning

confidence: 99%

“…Fairness of matching refers to the preferences of both sides are treated fairly in the matching process, and the matching goal is the minimization of differences between these preferences [ 30 , 31 ], to ensure that the preferences of two sides are as close as possible and reduce differences and jealousy, which is always used as a support for the matching stability. For better matching results, the matching optimization strategies with fairness increase gradually, such as issue of matching on uncertain preference sequence [ 32 ], supply meet demand of technological knowledge [ 33 ], task match resource in cloud manufacturing [ 34 ], selection of foreign customers in B2B export cross-border e-commerce [ 35 ]. They achieve the fairness of matching mainly by setting weight coefficients for the preferences of both parties, such as (0.5, 0.5) [ 36 , 37 ], or the minimization of the absolute value of preference difference [ 32 , 34 ].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Supply and demand matching model of P2P sharing accommodation platforms considering fairness

Wang

2020

Electron Commer Res

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Due to the continuous expansion of sharing economy, the diversification of users and the heterogeneity of resources and needs on P2P platform are speeding up, which makes it difficult to match the supply and demand of P2P platform effectively. Therefore, how to achieve effective matching between service providers and customers in an increasingly complex market is a question worthy of study. In order to achieve more effective matching of heterogeneous resources and requirements, this paper focuses on P2P sharing accommodation platform, advances a theoretical framework of fair matching and builds a matching model which considering fairness. First, we analyze the transaction mode of P2P sharing accommodation platform, proposed the framework of fair matching based on preferences consistency and fairness of matching. Second, we build a matching model based on fair matching, to maximize the consistency of preference and minimize the difference between supply and demand, the fair matching framework deals with heterogeneous resources and needs by matching diversified preferences. Finally, the effectiveness and feasibility of the strategy are verified by example and sensitivity analysis. This strategy provides optimization ideas for the matching issue of P2P sharing accommodation.

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Section: Discussionmentioning

confidence: 99%

“…Based on Eqs. (34) and (35), we design an algorithm to calculate the similarity rate of matching results (Algorithm 1). (34)…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Supply and demand matching model of P2P sharing accommodation platforms considering fairness

Wang

2020

Electron Commer Res

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“…With regard to service matching, it aims to find suitable services from the discovered candidate service with the same functions by comparing their non-functional attributes, such as product quality and delivery time. Li et al (2019) developed a two-side matching model for non-function attributes of services such as product quality and supply capacity using dual hesitant fuzzy sets [8]. Tao et al (2009) presented a framework for integrating service search and service matching by considering similarity degrees between services and subtasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Yang

Qi-dong

et al. 2020

Sustainability

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Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.

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“…Two-sided matching (TsM) is a well-known research direction of decision-making. It had been extensively employed in numerous fields, such as mechanical systems [1], complex product manufacturing tasks [2], green building technologies [3], content sharing in internet of vehicles [4], matching with the stars [5], stable job matching [6], marriage problems [7], and loan market [8]. As early as 1962, Gale and Shapley [9] have investigated two classical TsM model using preferences of ordinal numbers, i.e., stable marriage and college admissions.…”

Section: Introductionmentioning

confidence: 99%

TOPSIS Based Two-Sided Matching Under Interval-Valued Intuitionistic Fuzzy Environment in Virtual Reality Technology Transfer

Yue

Zhang

2020

IEEE Access

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Bodies' behaviors remain important to the decision-making process of two-sided matching. The two-sided matching problem under interval-valued intuitionistic fuzzy environment is investigated from the perspective of bodies' behavior, i.e., the behavior of matching willingness. For solving the problem, a normalized interval-valued score function is firstly presented. Through using this function, interval-valued intuitionistic fuzzy number (IvIFN) preference matrices are converted to normalized interval-valued score matrices. The normalized interval-valued score matrices are then converted to score matrices. Based on score matrices, the matching willingness can be obtained by solving an optimal model. Based on score matrices and matching willingness matrix, the weighting score matrices are set up. Aiming at weighting score matrices, we use the TOPSIS method to calculate the closeness degrees. Based on closeness degree matrices and matching matrix, a two-sided matching (TsM) model is developed. Considering the same or different statuses of bodies, the TsM model is transformed to the one-goal TsM model. Through model solution, the optimal scheme of TsM can be obtained. Ultimately, the availability of TOPSIS based TsM method is illustrated by using a matching example of virtual reality technology. INDEX TERMS Two-sided matching, interval-valued intuitionistic fuzzy number, bodies' behavior, model, virtual reality technology.

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

Cited by 41 publications

References 47 publications

Supply and demand matching model of P2P sharing accommodation platforms considering fairness

Supply and demand matching model of P2P sharing accommodation platforms considering fairness

Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

TOPSIS Based Two-Sided Matching Under Interval-Valued Intuitionistic Fuzzy Environment in Virtual Reality Technology Transfer

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