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This paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.

For dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.

Uncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.

A new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.

This paper mainly focuses on how to induce all members to represent members' true preferences for supply and demand matching of E-commerce platform in order to generate stable matching schemes with more social welfare of Multi-agent Matching Platform (MMP) and individually stable advantages than traditional methods.

An MMP is designed. Meanwhile, a true preference inducing method, Lower-Bid Ranking (LBR), is proposed to reduce the number of false preferences, which is helpful to solve the problem that too much false preferences leads to low efficiency of platform operation and supply and demand matching. Then, a systematic model of LBR-based Stable Matching (SM-LBR) is proposed.

To obtain an ideal transaction partner, the adequate preference ordering and modifying according to market environment is needed for everyone, and the platform should give full play to the platforms' information advantages and process historical transaction and cooperation data. Meanwhile, the appropriate supply and demand matching is beneficial to improve the efficiency and quality of platform operation, and the design of matching guidance mechanism is essential.

The numerical experiments show that, the proposed model (SM-LBR) can induce members to represent the model's true preferences for stable matching and generate effective matchings with more social welfare of MMP and individually stable advantages than traditional methods, which may provide necessary method and model reference for the research of stable matching and E-commerce platform operation.

Digital disruption offers an innovative opportunity to address housing affordability issues through the use of market design theory and two-sided matching markets. The purpose of this paper is to scope a model for how “uberisation” can revolutionise the traditional apartment delivery model in Australia, leading to improved housing affordability.

This paper uses semi-structured interviews with operators of online real estate platforms and deliberative developers to examine how the principles of “uberisation”, that is two-sided matching markets, are driving innovation in the apartment supply process.

Findings confirm that real estate internet platforms and deliberative developers innovators are informed by the benefits of aggregating demand to reduce development risk, thus enabling apartments to be provided at a substantially lower price than by traditional methods.

The number of interviews is small reflecting the limited number of market actors currently engaged in the innovations investigated.

This research is innovative as it introduces theoretical understandings gained from market design theory and applies those concepts to disrupt the apartment development process.

We propose a methodology for estimating preference parameters in matching models. Our estimator applies to repeated observations of matchings among a fixed group of individuals. Our estimator is based on the stability conditions in matching models; we consider both transferable (TU) and nontransferable utility (NTU) models. In both cases, the stability conditions yield moment inequalities which can be taken to the data. The preference parameters are partially identified. We consider simple illustrative examples, and also an empirical application to aggregate marriage markets.

The peer-to-peer (P2P) accommodation-sharing market has developed rapidly on the strength of information technology in recent years. Matching providers and customers in an information technology (IT)-enabled platform is a key determinant of both parties' experiences and the healthy development of the platform. However, previous research has not sufficiently explained the mechanism of provider–customer matching in accommodation sharing, especially at the psychological level. Based on field cognitive style theory, this study examines how the match and mismatch affect customers' online and offline satisfaction and whether a significant difference exists between online and offline satisfaction under different matching patterns.

The authors test the proposed theoretical model using 122 provider–customer dyad data collected through a field study.

The results suggest that customers' online and offline satisfaction under match is significantly higher than that under mismatch. In addition, customers' online satisfaction is significantly higher than their offline satisfaction under mismatch, but there is no significant difference between the two under match. The perceived price fairness also plays a moderating role in the case of mismatch.

In summary, these findings provide a novel understanding about the matching patterns and their outcomes in the accommodation-sharing context and expand the contents and applications of field cognitive style theory and matching theory. This study will help these IT-enabled platforms to provide personalized matching services at the psychological level, thereby enhancing user experience and corporate competitiveness. 10; 10;

The purpose of this paper is to demonstrate a newly developed integrated multi‐criteria decision method (MCDM) with two‐sided preferences or selection scenario in build‐to‐order supply chains so as to explore the future applications like supplier parks.

The current state of the consolidation scenario of original equipment manufacturers (OEMs) and suppliers is presented and build‐to‐order imperativeness in the case of a supplier park is highlighted. These motivations guided the need for development of a new technique in two‐sided selection scenario. The MCDM technique, like analytical network process (ANP), is integrated with a mutual compatibility index. A hypothetical example is presented to demonstrate the proposed methodology.

The newly developed integrated methodology helps the decision maker to incorporate both sided preferences in the final supplier‐manufacturer selection.

This paper allows the reader to understand the build‐to‐order supply chain and supplier park situation. The emerging trends in the automobile sector in India are also highlighted. This new integrated technique certainly adds value to the decision maker in today's competitive world.

The 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.

The prospect theory and grey relational analysis are used to develop the proposed two-sided matching decision framework.

By 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.

The 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.

The proposed model can be applied to achieve a better matching between technological knowledge suppliers and users in a broker-assisted MFT.

A better matching between technological knowledge suppliers and users can enhance the success of open innovation, thereby contributing to the betterment of the society.

This 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.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.