Base type selection of product service system based on convolutional neural network

Zhou, Yue-Ning; Liu, Wei; Chai, Nana; Fan, Beibei; Zhang, Zaifang

doi:10.1016/j.procir.2019.03.115

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…To understand customer needs by using quantitative approaches, Yu et al [34] proposed a knowledge-based artificial neural network (ANN) combined with a decision tree to substantiate customer needs to product specification. Zhou et al [14] trained the CNN to obtain the complex nonlinear mapping relationship between the customer's demand attributes and the basic types of product-service systems, to determine the PSS configuration. Shen et al [35] proposed a method that combines cluster neural network and rule algorithm to extract configuration rules between service parameters and functional requirements, customer features, or product features to gain higher effectiveness of configuration solution.…”

Section: Pss Configurationmentioning

confidence: 99%

“…The key in configuration is to combine product modules and service modules to satisfy the customer demands. The existing PSS configuration approaches include genetic algorithm (GA) [10,11], ontology modeling [12,13], convolutional neural network (CNN) [14], multi-objective programming [5,15], and so on. These approaches solve the PSS configuration problem as an optimization model with an objective or a fitness function.…”

Section: Introductionmentioning

confidence: 99%

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Product Service System Configuration Based on a PCA-QPSO-SVM Model

Cui

Geng

2021

Sustainability

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To achieve sustainable development and improve market competitiveness, many manufacturers are transforming from traditional product manufacturing to service manufacturing. In this trend, the product service system (PSS) has become the mainstream of supply to satisfy customers with individualized products and service combinations. The diversified customer requirements can be realized by the PSS configuration based on modular design. PSS configuration can be deemed as a multi-classification problem. Customer requirements are input, and specific PSS is output. This paper proposes an improved support vector machine (SVM) model optimized by principal component analysis (PCA) and the quantum particle swarm optimization (QPSO) algorithm, which is defined as a PCA-QPSO-SVM model. The model is used to solve the PSS configuration problem. The PCA method is used to reduce the dimension of the customer requirements, and the QPSO is used to optimize the internal parameters of the SVM to improve the prediction accuracy of the SVM classifier. In the case study, a dataset for central air conditioning PSS configuration is used to construct and test the PCA-QPSO-SVM model, and the optimal PSS configuration can be predicted well for specific customer requirements.

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Section: Pss Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Product Service System Configuration Based on a PCA-QPSO-SVM Model

Cui

Geng

2021

Sustainability

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Design and Development of Sustainable Product Service Systems Based on Design-Centric Complexity

et al. 2021

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Product-service system (PSS) complexity is rapidly increasing in order to meet complex user requirements. Increased complexity leads to PSSs failing to meet sustainability requirements in their initial design. To enhance PSS sustainability and support social benefits, this paper proposes a sustainable PSS development framework based on design-centric complexity (DCC) theory. In the early design stage of a PSS, DCC theory is used to analyze the complexity of the system. Determination of the types of complexity in the system and the corresponding problems is then performed. By combining the sub-field model of TRIZ, the problems can be converted and solved, and the functional periodicity may be established to reduce system complexity. By using this development framework, the conflicts and potential problems of design attributes can be reduced and the possibility of achieving PSSs functional requirements (FRs) can be enhanced. This will maintain the long-term operation stability for the system and enhance the sustainability of the PSS. Finally, the feasibility of the development framework is verified here through the case analysis of a bicycle sharing service and management system.

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Base type selection of product service system based on convolutional neural network

Cited by 2 publications

References 11 publications

Product Service System Configuration Based on a PCA-QPSO-SVM Model

Product Service System Configuration Based on a PCA-QPSO-SVM Model

Design and Development of Sustainable Product Service Systems Based on Design-Centric Complexity

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