Abstract
To improve the satisfaction of both service demanders (SDs) and service providers (SPs) in the matching of cloud manufacturing (CMfg) tasks and services, a two-sided stable matching model of CMfg tasks and service considering the nonlinear relationship between satisfaction and expectations is proposed. As the expectations of SDs and SPs are difficult to be quantified directly, an evaluation method based on interval-valued hesitant fuzzy linguistic sets (IVHFLSs) is first presented. Next, a nonlinear model of satisfaction and expectations is built to quantify the satisfaction, which achieves accurate quantification of satisfaction. Then, a two-sided stable matching model of CMfg tasks and service is built, which takes the satisfaction of SDs and SPs as the optimization goals and considers the individual rationalities and blocking pairs. Finally, an adaptive genetic algorithm (AGA) is designed to solve the proposed two-sided matching model. A practical application and comparison analysis is used to verify the effectiveness and superiority of the research.
Highlights
Cloud manufacturing (CMfg) is a new manufacturing mode that organizes the online service released by service providers (SPs) and allocates them to service demanders (SDs) on demand [1, 2]
Many studies concerning the matching of CMfg tasks and service have been performed. e research can be divided into two categories: single-sided matching and two-sided matching
SPs and SDs assessed each other based on linguistic information, and the assessment results were transformed into numerical values. en, the satisfaction was calculated by using the variable fuzzy recognition method
Summary
Cloud manufacturing (CMfg) is a new manufacturing mode that organizes the online service released by service providers (SPs) and allocates them to service demanders (SDs) on demand [1, 2]. Li et al built a two-sided matching model with hesitant fuzzy preference information for configuring CMfg tasks and service [15]. E complexity of satisfaction indexes and the limitation of human cognition, hesitation, and fuzziness always exist in the expression process of information Under such a situation, qualitative linguistic terms instead of precise quantitative numbers are more suitable for SDs and SPs to express information of satisfaction indexes [18]. (2) A two-sided stable matching model of CMfg tasks and service is put forward (as shown in Figure 1(b)), which directly takes the satisfaction of SDs and SPs as the optimization goals and considers the individual rationalities and blocking pairs, making the optimization of satisfaction more effective.
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