Stable two-sided satisfied matching for ridesharing system based on preference orders

Zhao, Rong; Jin, Maozhu; Ren, Peiyu; Zhang, Qian

doi:10.1007/s11227-020-03173-6

Cited by 15 publications

(14 citation statements)

References 27 publications

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“…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 ]. The difference of two numbers and the setting of two peers’ preferences weight cannot reflect the similarity and difference of peer’s subjective tendency.…”

Section: Related Workmentioning

confidence: 99%

“…Preferences information is the key basis for matching decisions and often used in the research and practice of matching decision-making, such as cross-border e-commerce [ 35 ], ridesharing system [ 37 ], cloud manufacturing [ 34 , 46 ], and smart intelligent technique transfer [ 47 ]. Integrating the extant studies on preferences and satisfaction in e-trading and two-sided matching [ 34 , 37 , 48 , 49 ], we advanced the definition of preferences of P2P platform peers as follows.…”

Section: Fair Matching and The Modelmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

Section: Fair Matching and The Modelmentioning

confidence: 99%

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

Wang

2020

Electron Commer Res

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“…Fundamentally, the disappointment theory suggests that when actual results are better than expected, policymakers will be ecstatic, when actual results are worse than expected, manufacturers will be disappointed, and that the greater the disparity between the outcome and the expectation, the greater the disappointment [21]. So far, there are three main contemporary disappointment models [25,26]. e first is the Bell-Loomes and Sugden disappointment model.…”

Section: Disappointmentmentioning

confidence: 99%

“…Zhao et al proposed a two-sided matching model to elaborate the preference order of SAD by considering the participants' psychological perception. Using the functions of disappointment and cheerfulness, this model effectively improves the carpool matching problem [26]. Another example is that by Fan et al, who proposed a two-way matching method that considers the psychological behavior of the agents of both parties and constructed a two-objective optimization model to obtain satisfactory matching results [29].…”

Section: Disappointmentmentioning

confidence: 99%

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Multiattribute Supply and Demand Matching Decision Model for Online-Listed Rental Housing: An Empirical Study Based on Shanghai

Jiang-ying

Yan

et al. 2020

Discrete Dynamics in Nature and Society

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The mismatch between the supply and demand of online-listed rental housing (ORH) is an important factor restricting the operational efficiency of online rental service platforms. However, extant literature pays little attention to this problem. This study proposes an ORH multiattribute supply and demand matching decision model based on the perceived utility of matching both sides of this market. The model considers the multiattribute information of ORH, such as area, transportation, rent, room, and interior decoration, and quantifies their perceived utility values based on the theory of disappointment. Thereafter, we construct the matching decision model and verify it for feasibility by applying it to Shanghai’s ORH supply and demand information—our empirical case. The results show that this method can be applied to online rental housing platforms and meet the supply and demand matching requirements to the greatest extent. The constructed model takes into account the perceptions of both supply and demand parties, may promote the effective matching of ORH supply and demand, and bears theoretical implications for the improvement of rental housing matching in ORH platforms.

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A matching method for second-hand goods exchange considering loss aversion of buyer and seller in e-brokerage

Fan

et al. 2021

Soft Comput

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Stable two-sided satisfied matching for ridesharing system based on preference orders

Cited by 15 publications

References 27 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

Multiattribute Supply and Demand Matching Decision Model for Online-Listed Rental Housing: An Empirical Study Based on Shanghai

A matching method for second-hand goods exchange considering loss aversion of buyer and seller in e-brokerage

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