Two-sided Matching Decision Research Articles

Two-sided matching decision method and application based on subject differences in interval intuitionistic fuzzy environment

In this paper, a two-sided matching decision model based on interval-valued intuitionistic fuzzy environment is proposed to maximize the demand of individual differences. Firstly, the attribute feature table and weight matrix of both agents are constructed, and a formula for calculating the comprehensive advantage in the interval-valued intuitionistic fuzzy environment is given, namely the interval-valued intuitionistic fuzzy comprehensive advantage aggregation operator (IIFCAAO); Secondly, the interval-valued intuitionistic fuzzy decision matrix of both agents is calculated by using the comprehensive advantage aggregation operator, and the interval-valued intuitionistic fuzzy decision matrix is transformed into a score function matrix by using a score function formula; Thirdly, the two-sided matching model is established to maximize the score; Finally, the scientificity and practicability of the model are verified by an example of college students changing their majors.

A two-sided matching decision method based on interval triangular fuzzy sets

The research work on two-sided matching decision problem considering interval triangular fuzzy information is very scarce, and it needs to be further studied. Based on this, this paper proposes a two-sided matching model based on interval triangular fuzzy sets, with the background of the two-sided matching problem in the interval triangular fuzzy set environment. Firstly, the theory of two-sided matching and interval triangular fuzzy sets is given; Secondly, the comprehensive mean value formula is defined, and the interval triangular fuzzy evaluation matrix is transformed into the comprehensive mean value matrix by using the comprehensive mean value formula; Thirdly, a two-sided matching model is built with the goal of maximizing the satisfaction of each subject; Finally, the feasibility and effectiveness of the proposed method are verified by examples of investment fund institutions and financing enterprises.

Two-sided matching decision making for probabilistic hesitant fuzzy numbers

Probabilistic hesitant fuzzy set (PHFS), as a complex ambiguous information representation tool, has been widely used in decision making problem, but is rarely applied in a two-sided matching (TSM). Therefore, it is important and necessary to investigate the TSM problem with PHFS. This paper proposes a decision method for TSM with probabilistic hesitant fuzzy Numbers (PHFNs) and applies it to the person-job fit problem. Firstly, a novel TSM decision model on the basis of PHFNs is constructed. In order to solve this model, the TSM model is transformed into a two-goal TSM model by using linear weighted method. And then, the two-goal TSM model with PHFN can be changed into a single-goal TSM model with scores through score function. The perfect alternative of TSM could be obtained through model solution. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method.

Two-Sided Matching Decision Making with Multi-Attribute Probabilistic Hesitant Fuzzy Sets

In previous research on two-sided matching (TSM) decision, agents’ preferences were often given in the form of exact values of ordinal numbers and linguistic phrase term sets. Nowdays, the matching agent cannot perform the exact evaluation in the TSM situations due to the great fuzziness of human thought and the complexity of reality. Probability hesitant fuzzy sets, however, have grown in popularity due to their advantages in communicating complex information. Therefore, this paper develops a TSM decision-making approach with multi-attribute probability hesitant fuzzy sets and unknown attribute weight information. The agent attribute weight vector should be obtained by using the maximum deviation method and Hamming distance. The probabilistic hesitancy fuzzy information matrix of each agent is then arranged to determine the comprehensive evaluation of two matching agent sets. The agent satisfaction degree is calculated using the technique for order preference by similarity to ideal solution (TOPSIS). Additionally, the multi-object programming technique is used to establish a TSM method with the objective of maximizing the agent satisfaction of two-sided agents, and the matching schemes are then established by solving the built model. The study concludes by providing a real-world supply-demand scenario to illustrate the effectiveness of the proposed method. The proposed method is more flexible than prior research since it expresses evaluation information using probability hesitating fuzzy sets and can be used in scenarios when attribute weight information is unclear.

Multi-attribute strict two-sided matching methods with interval-valued preference ordinal information

ABSTRACT In the study of two-sided matching decision problems, preference ordinal information is a key factor. However, in real life, it is often difficult to ascertain complete preference ordinal information, and in most cases we can only obtain an interval-valued preference ordinal information. In this paper, a strict two-sided matching based on multi-attribute interval-valued preference ordinal information is discussed. As a generalised decision model, the strict two-sided matching adequately considers the requirement of satisfaction degree of two-sided agents. Firstly, the ranking method of probability degree is introduced to deal with the information of various interval numbers. Then, in the case of multiple attributes, we propose two methods for strict two-sided matching problem. The one is to aggregate multi-attribute satisfaction degree and then construct the decision model. The another is to separately deal with the interval-valued preference ordinal information of each attribute and then design the corresponding model. Finally, in the context of Internet finance, we adopt an example of the venture capital two-sided matching problem to illustrate our proposed methods and confirm the effectiveness.

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

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.

A two-sided matching method considering the lowest value of acceptability with regret theory for probabilistic linguistic term sets

In order to solve matching problems for probabilistic linguistic information, a novel two-sided matching decision method for the probabilistic linguistic term sets (PLTSs) based on the regret theory considering the lowest value of acceptability is proposed. First, we propose a new utility function to transform the PLTSs to utility values, which can be conveniently applied to two-sided matching models. Then, to reflect the bounded rationality of expert and make the decision result close to real decision process, we put forward a novel regret-based model to obtain regret-rejoice by setting the lowest value of acceptability based on the utility function. Furthermore, we presented a new type of two-sided matching method considering constraint condition based on the lowest value of acceptability. Finally, we apply our method to a real case and make comparisons with two traditional two-sided methods.

Decision-Making Approach of Two-Sided Matching of Supply and Demand of Logistics Service Based on the Uncertain Preference Ordinal

Aiming at the problem of matching logistics service supply and demand, this paper proposes a two-sided matching decision model of logistics service supply and demand based on the uncertain preference ordinal. In this model, the uncertain preference ordinal information is first expressed by the interval numbers of the logistics service supply and demand, and it is converted into the satisfaction degree of supply and demand matching uncertainty expressed by the interval number. Then, a multiobjective optimal matching model is constructed based on the largest overall satisfaction of the logistics service supply and demand side and the smallest satisfaction variance of the supply- and demand-side individual, and the multiobjective solution algorithm is designed based on nondominated sorting genetic algorithm-III (NSGA-III). Interval numbers are used to sort the matching results to obtain the approximately optimal two-sided matching scheme. Finally, this paper verifies the correctness of the model and validity of the algorithm.

Consistency improvement for fuzzy preference relations with self-confidence: An application in two-sided matching decision making

The fuzzy preference relation with self-confidence (FPR-SC), whose elements are composed of the degree to which an alternative is preferred to another and the self-confidence level about the preference degree, is a useful tool for decision makers to express their preference information over alternatives. In this paper, an extended logarithmic least squares method (LLSM) is first proposed to derive a priority weight vector from an FPR-SC, based on which the multiplicative consistency of an FPR-SC is further defined and two algorithms are devised to improve the multiplicative consistency of an unacceptably consistent FPR-SC. Furthermore, we develop a novel approach to two-sided matching decision making with FPRs-SC based on the LLSM and the proposed consistency improving algorithms. Eventually, the feasibility and effectiveness of the two-sided matching decision making approach are demonstrated by an example for the matching of knowledge suppliers and knowledge demanders.

Stable two-sided matching decision making with incomplete fuzzy preference relations: A disappointment theory based approach

Practical two-sided matching decision making problems, such as marriage matching and person-job matching, are often characterized by a lack of knowledge and time constraints. Therefore, matching objects tend to provide comparative preferential information over other matching objects represented by incomplete fuzzy preference relations. In this paper, it is proposed a new approach to stable two-sided matching decision making with incomplete fuzzy preference relations based on disappointment theory. In the proposed approach, the subjective satisfaction degrees of each matching object on one side over matching objects on the other side are first calculated based on priority weight vectors derived from incomplete fuzzy preference relations. Based on disappointment theory, both the disappointment and elation degrees associated with each matching object over matching objects on the other side are calculated. This process is undertaken by considering the probability of each possible matching pair, which are further used to derive the adjusted satisfaction degrees of matching objects. Afterwards, a stable matching optimization model that aims to maximize the total adjusted satisfaction degrees of both sides is constructed by considering stable matching conditions under incomplete information. The optimal stable matching result can be further determined by solving the optimization model. Finally, a numerical example and some comparative studies are presented to demonstrate the characteristics, innovations and added value of the proposed approach.

A Two-Sided Matching Model for Task Distribution in Ridesharing: A Sustainable Operations Perspective

With regard to task distribution in a ridesharing company, both the suitability of the tasks assigned to the drivers and the acceptability of the riders receiving the service should be simultaneously considered to improve the sustainability regarding Hitch services. Firstly, the process of the bi-directional choice between the drivers and the riders is described as a one-to-one two-sided matching problem. Next, prospect theory is used to characterize the psychological perceived behavior of both sides towards the matching scheme under the multiple criteria. Thus, the suitability function concerning the drivers and the acceptability function regarding the riders are naturally constructed. Following this, a two-sided matching decision model with two objectives is proposed. Finally, numerical experiments are presented to verify the feasibility and effectiveness of the proposed model. Besides, managerial insights associated with how to set the optimization objectives under unbalanced supply-demand in ridesharing companies are given. Increasingly, this paper aims to not only validate the proposed methodology, but also to highlight the importance and urge of incorporating sustainability into the task distribution problem in ridesharing.

Two-Sided Matching for Triangular Intuitionistic Fuzzy Numbers in Smart Environmental Protection

Triangular intuitionistic fuzzy numbers (TIFNs) are effective and flexible to characterize the fuzziness and uncertainty in real-world problems. The theories of TIFNs have been used in multi-attribute decision making but are rarely applied in a two-sided matching decision. Therefore, it is important and necessary to investigate the two-sided matching problem with TIFNs. This paper develops a decision method for two-sided matching with triangular intuitionistic fuzzy numbers and applies it to smart environmental protection. First, a similarity measure between generalized triangular fuzzy numbers (TFNs) is presented. Then, a novel similarity measure between TIFNs is extended, where the maximum membership degrees and minimum non-membership degrees, areas, and perimeters are considered. With respect to the two-sided matching problem with TIFNs, the two-sided matching model with TIFNs is established. Using similarity measures between TIFNs, the similarity matrices of triangular intuitionistic fuzzy preference matrices are constructed by using the positive idea vectors. Then, the two-sided matching model with similarity measures is obtained. Using the arithmetic mean, normalization formulas and linear weighting, the two-sided matching model with similarity measures is transformed into a mono-objective model. The optimum matching scheme is obtained by solving the model. Thus, a similarity measure-based two-sided matching decision method for TIFNs is proposed. Finally, a matching example in smart environmental protection is provided to illustrate the advantages of the proposed method.

Multi-Criterion Two-Sided Matching of Public–Private Partnership Infrastructure Projects: Criteria and Methods

Two kinds of evaluative criteria are associated with Public–Private Partnership (PPP) infrastructure projects, i.e., private evaluative criteria and public evaluative criteria. These evaluative criteria are inversely related, that is, the higher the public benefits; the lower the private surplus. To balance evaluative criteria in the Two-Sided Matching (TSM) decision, this paper develops a quantitative matching decision model to select an optimal matching scheme for PPP infrastructure projects based on the Hesitant Fuzzy Set (HFS) under unknown evaluative criterion weights. In the model, HFS is introduced to describe values of the evaluative criteria and multi-criterion information is fully considered given by groups. The optimal model is built and solved by maximizing the whole deviation of each criterion so that the evaluative criterion weights are determined objectively. Then, the match-degree of the two sides is calculated and a multi-objective optimization model is introduced to select an optimal matching scheme via a min-max approach. The results provide new insights and implications of the influence on evaluative criteria in the TSM decision.

A novel two-sided matching decision method for technological knowledge supplier and demander considering the network collaboration effect

To obtain competitive advantage, the key target for the two-sided matching between technological knowledge supplier and demander is maximizing the individual exchange satisfaction. The structure of the supplier and demander relationship networks means that the two-sided matching approach not only gives point-to-point matching but also gives network-to-network matching. In this paper, supply and demand network characteristics are embedded into a two-phase decision analysis method. First, to select the matching pairs, a matching satisfaction matrix is constructed based on the supply and demand network characteristics, after which a multi-objective optimal model is built to determine the best optimization matching results and the overall improvements illustrated through comparison. Finally, a numerical example is given to show the practicality and validity of the proposed approach.

Hesitant fuzzy linguistic two-sided matching decision making

This paper combines the theory of hesitant fuzzy linguistic term sets (HFLTSs) with two-sided matching decision making (TSMDM). The related definitions of HFLTSs and two-sided matchings (TSMs) are introduced. Then, the problem of TSMDM with HFLTSs is presented. For solving this problem, a model of TSMDM with HFLTSs is developed. The AHP method is used to determine the important degrees of agents of each side. On this base, the model of TSMDM can be changed into a double-goal model with HFLTSs. Then, the double-goal model with HFLTSs is changed into the double-goal model with scores through using the proposed score function. Furthermore, the double-goal model can be changed into a single-goal model by using the linear weighting technique once again. The scheme of TSM can be obtained through solving the single-goal model. At last, an example with sensitive analysis is provided for the illustration of the presented approach of TSM.

Extend TOPSIS-Based Two-Sided Matching Decision in Incomplete Indifferent Order Relations Setting Considering Matching Aspirations

This paper develops a method for two-sided matching decision in the environment of incomplete indifferent order relations. The two-sided matching decision problem with incomplete indifferent order relations and matching aspirations is firstly described. In order to solve this problem, the incomplete indifferent order relations are converted into the generalized Borda number matrices. The matching aspiration matrix can be determined based on the model calculation on the reciprocal differences of generalized Borda numbers. On this basis, the weighted satisfaction degree matrices are set up. The extended relative closeness matrices are determined by using an extended TOPSIS technique. Moreover, a two-sided matching model is developed. The two-sided matching alternative can be obtained by solving the model. For the purpose of illustration, an example including sensitivity analysis is presented.

A two-sided matching decision method for supply and demand of technological knowledge

PurposeThe purpose of this paper is to propose a novel prospect-based two-sided matching decision model for matching supply and demand of technological knowledge assisted by a broker. This model enables the analyst to account for the stakeholders’ psychological behaviours and their impact on the matching decision in an open innovation setting.Design/methodology/approachThe prospect theory and grey relational analysis are used to develop the proposed two-sided matching decision framework.FindingsBy properly calibrating model parameters, the case study demonstrates that the proposed approach can be applied to real-world technological knowledge trading in a market for technology (MFT) and yields matching results that are more consistent with the reality.Research limitations/implicationsThe proposed model does not differentiate the types of knowledge exchanged (established vs novel, tacit vs codified, general vs specialized) (Ardito et al., 2016, Nielsen and Nielsen, 2009). Moreover, the model focuses on incorporating psychological behaviour of the MFT participants and does not consider their other characteristics.Practical implicationsThe proposed model can be applied to achieve a better matching between technological knowledge suppliers and users in a broker-assisted MFT.Social implicationsA better matching between technological knowledge suppliers and users can enhance the success of open innovation, thereby contributing to the betterment of the society.Originality/valueThis paper furnishes a novel theoretical model for matching supply and demand in a broker-assisted MFT. Methodologically, the proposed model can effectively capture market participants’ psychological considerations.

A new method for two-sided matching decision making of PPP projects based on intuitionistic fuzzy choquet integral

A new method for two-sided matching decision making of PPP projects based on intuitionistic fuzzy choquet integral