Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.     

A structural service innovation approach for designing smart product service systems: Case study of smart beauty service

Smart Product-service systems (PSS) are the emerging type of PSS that offer market value and dynamic intelligence combining products and services as solutions to consumers based on digital technology. To design a smart PSS with an effective way, a structural design approach is required. Nevertheless, only a few existing researchers discussed this topic. Aiming to bridge this gap, an integrated way is proposed for smart PSS design. This study refines a generic approach for structural service innovation approach which integrates the advantage of PSS engineering and service engineering for designing smart product service systems. The structural design approach is based on the Theory of Inventive Problem Solving (TRIZ) incorporating with service blueprint named PRR method. Three design phases are demonstrated as verification of the PRR method by an elaborated case study of the smart beauty service. Based on PRR, three key design phases are integrated, i.e., (1) problem definition, (2) resolution generation, and (3) resolution design. Empirical results and implications are collected and discussed to obtain valuable insights for value creation.     

A hybrid intelligence approach for sustainable service innovation of smart and connected product: A case study

Nowadays, smart and connected product (SCP) is gradually replacing the traditional functional products, which has attracted widespread attention from the industry and academia. Service innovation, as a crucial part of SCP iterative improvement, is a multi-criteria decision-making (DM) process facilitated by intelligent automation (IA) and cognitive technologies. However, product user’s intelligence (e.g. physiological feeling) that can intuitively reflect and evaluate the product service satisfaction is rarely considered in the process of service innovation. Hence, it is difficult to measure the product users’ preferences with precise numerical terms to make a strategic decision. In order to fill this gap, a hybrid intelligence approach is proposed to perform the service innovation for SCP. The product-user data (e.g. subjective data and physiological data) and product-sensor data are collected and used for the process of service innovation. A smart group spinning bicycle system is presented as an elaborated case study to illustrate the proposed architecture and approach. The service innovation of real-time and dynamic monitoring, user participation improvement and smart feedback manners are achieved. In addition, an ergonomic experiment is conducted to validate the effectiveness of the proposed approach in implementing the service innovation for SCP.     

Comprehensive understanding of smart product service system from multi-dimension and multi-perspective: An innovative service model for Customer-product Interaction Life Cycle (CILC)

With the improvement of living standards and the rapid development of the Internet, customers gradually pursue personalized smart products and services. Traditional smart product service system is mainly aimed at the service of customers and products in one or several interactive links. Furthermore, in the mass personalization model, in order to give full play to the customer experience and participation satisfaction, it is necessary to study the smart product service system based on the interaction between customers and products. However, there is a gap in the research on integrating the whole life cycle of customer-product interaction. In order to meet this industrial need, the innovation of this paper is to construct an innovative service model for Customer-product Interaction Life Cycle (CILC) and analyse the service impact factors and countermeasures using fuzzy DEMATEL method. The study can provide enterprises with the top-level framework and decision-making of smart product service system based on CILC, and realize the effective integration, expansion and value-added of service resources. At the same time, the application of this research methods can shorten the distance between customers and products, improve customer satisfaction.     

A digital smart product service system and a case study of the mining industry: MSPSS

Mining equipment products and services no longer meet the needs of future development in the mining industry due to high safety and operational risk. The deep integration of the product-service system (PSS) and digitization is required in the mining industry to promote industry transformation and safe and efficient production without changing the traditional operation mode. This paper proposes a smart product-service system for the mining industry (MSPSS) consisting of a smart product subsystem, stakeholders, smart service subsystem, and smart decision-making subsystem. The analytic hierarchy process (AHP) and virtual reality (VR) are used for decision-making, product selection, operation, and maintenance. The smart product subsystem outputs reliable digital products using three stages: digital design, virtual simulation and planning, and virtual debugging. The smart service subsystem is driven by data and digital technology and provides fault diagnosis and online maintenance services for complex mining products. A case study indicates that all stakeholders can participate seamlessly in the design process. The smart product subsystem uses iterative optimization (more than 100 iterations) to obtain the design results interactively. The smart service subsystem provides digitalized services throughout the entire process. Thus, a stable, reliable, and comprehensive product and service solution is provided for complex mining conditions. The output is used to guide the design, debugging, and operation of physical equipment. The MSPSS has higher design quality and efficiency, a shorter design time, and lower design cost (key performance indicator (KPI)) than the traditional design method.     

A computerized team approach for concurrent product and process design optimization

Concurrent product and process design (CPPD) can be handled by developing a computerized team approach. In the team approach, CPPD at the top-level abstraction is treated as a two-objective optimization problem with a view to two teams: design and manufacture. The design team aims to optimize the overall product functionality (or performance) and the manufacture team to minimize the total manufacturing cost. Satisfaction metrics are used to characterize the degree of how each team prefers a design in seeking the most favorite that best fulfills the goal of a team. Using min and geometric mean operators as a baseline, the satisfaction levels of two teams can be aggregated with respect to strategic team paradigms derived from game theory. With satisfaction metrics, three dual-team based CPPD models are developed, along with the computational algorithms, in an attempt to reveal team activities in the context of design. The procedures to simulate and implement various team design models are demonstrated through a design example.     

一个基于Web服务的在线优化和监控系统

在流程工业,许多信息集成系统通过CORBA／Agent技术来实现,这些集成方案在实时性,安全性,以及数据通信的稳定性等方面尚待改进。为了研究更加合理的集成方案,实现了一个基于Web服务的在线优化和监控系统,在这个系统中,专业软件Matlab,G2,GAMS被分别封装成Web服务并在UDDI注册中心注册,然后实现了这些Web服务的集成。最后,通过在尼泊丁乙酯合成过程中的应用．证明了该在线优化和监控平台的有效性和适用性。     

A multicriteria optimization model for cloud service provider selection in multicloud environments

Multicloud computing is a strategy that helps customers to reduce reliance on any single cloud provider (known as the vendor lock-in problem). The value of such strategy increases with proper selection of qualified service providers. In this paper, a constrained multicriteria multicloud provider selection mathematical model is proposed. Three metaheuristics algorithms (simulated annealing [SA], genetic algorithm [GA], and particle swarm optimization algorithm [PSO]) were implemented to solve the model, and their performance was studied and compared using a hypothetical case study. For the sake of comparison, Taguchi's robust design method was used to select the algorithms' parameters values, an initial feasible solution was generated using analytic hierarchy process (AHP)—as the most used method to solve the cloud provider selection problem in the literature, all three algorithms used that solution and, in order to avoid AHP limitations, another initial solution was generated randomly and used by the three algorithm in a second set of performance experiments. Results showed that SA, GA, PSO improved the AHP solution by 53.75%, 60.41%, and 60.02%, respectively, SA and PSO are robust because of reaching the same best solution in spite of the initial solution.     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

A new hybrid fuzzy-goal programming scheme for multi-objective topological optimization of static and dynamic structures under multiple loading conditions

This work presents a hybrid fuzzy-goal multi-objective programming scheme for topological optimization of continuum structures, in which both static and dynamic loadings are considered. The proposed methodology fortopological optimization first employs a fuzzy-goal programming scheme at the top level for multi-objective problems with static and dynamic objectives. For the static objective with multi-stiffness cases in the fuzzy-goal formulation, a hybrid approach, involving a hierarchical sequence approach or a hierarchical sequence approach coupled with a compromise programming method, is especially suggested for the statically loaded multi-stiffness structure at the sublevel. Concerning dynamic optimization problems of freevibration cases, nonstructural mass, oscillation of the objective function, and repeated eigenvalues are also discussed. Solid Isotropic Material with Penalization density–stiffness interpolation scheme is used to indicate the dependence ofmaterial modulus upon regularized element densities. The globally convergent version of the method of moving asymptotes and the sequential linear programming method areboth employed as optimizers. Several applications have been applied to demonstrate the validation of the presented methodologies.     

A digital twin-enhanced system for engineering product family design and optimization

Engineering product family design and optimization in complex environments has been a major bottleneck in today’s industrial transformation towards smart manufacturing. Digital twin (DT), as a core part of cyber-physical system (CPS), can provide decision support to enhance engineering product lifecycle management workflows via remote monitoring and control, high-fidelity simulation, and solution generation functionalities. Although many studies have proven DT to be highly suited for industry needs, little has been reported on the product family design and optimization capabilities specifically with context awareness, which could be leaving many enterprises ambivalent on its adoption. To fill this gap, a reusable and transparent DT capable of situational recognition and self-correction is essentially required. This paper develops a generic DT architecture reference model to enable the context-aware product family design optimization process in a cost-effective manner. A case study featuring asset re-/configuration within a dynamic environment is further described to demonstrate its in-context decision-aiding capabilities. The authors hope this study can provide valuable insights to both academia and industry in improving their engineering product family management process.     

A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

To assess sustainability of power plants, this paper presents a novel hybrid method. To this end, self‐organizing map method of artificial neural networks is employed. Then, a double frontier data envelopment analysis is developed to rank power plants in each cluster of decision‐making units. Because outputs of power plants might be uncertain, a robust optimization approach is incorporated into proposed double frontier data envelopment analysis model to present ranks that are robust against different uncertainties. A case study is given to validate the proposed model. The case study shows that the proposed model can present improvement solutions that guide power plants towards efficient frontier and far from inefficient frontier. Given the results, decision makers can decide on which power plants should be closed and which power plants should be expanded.     

A function-oriented biologically analogical approach for constructing the design concept of smart product in Industry 4.0

The development of the Industry 4.0 paradigm and the advancement of information technology have aroused new consumer requirements for smart products that are capable of context awareness and autonomous control. Nature holds huge potential for inspiring innovative design concepts that can meet the ever-growing need for smart products since biology perceive and interact with their living environment for survival. However, to date, very few studies have explored the application of natural wisdom in building innovative design concepts for smart products. This paper proposes a function-oriented design approach for smart products, by analogizing to biological prototypes. To do so, a unified functional representation, based on the Function–behavior–structure (FBS) ontology, is proposed to abstract biological prototypes, followed by a fuzzy triangular numbers-based algorithm designed to locate appropriate biological prototypes as analogical sources for smart product development. Moreover, functional innovative strategies and a hybrid design process are formulated to develop design concepts of smart products, by integrating several existing engineering design methods. Finally, an illustrative design case of a smart natural resource collecting system is used to demonstrate the workability of the proposed method.     

产品开发任务分配问题的多目标优化求解

针对目前产品开发任务分配问题研究存在的不足,给出了任务分配问题的数学描述和约束条件,提出了任务分配模型中的相关矩阵,并采用权重因子和极差变换法建立了多目标优化的目标函数.针对任务分配过程的动态性和不确定性,提出采用基于时序逻辑关系的动态分配蚁群算法进行优化计算,并分析了该方法的优点,给出了详细的算法步骤.最后通过仿真实验验证了所提出方法的可行性和有效性.     

Bi-objective optimization of the reliability-redundancy allocation problem for series-parallel system

The main objective of this paper is to solve the bi-objective reliability redundancy allocation problem for series-parallel system where reliability of the system and the corresponding designing cost are considered as two different objectives. In their formulation, reliability of each component is considered as a triangular fuzzy number. In order to solve the problem, developed fuzzy model is converted to a crisp model by using expected values of fuzzy numbers and taking into account the preference of decision maker regarding cost and reliability goals. Finally the obtained crisp optimization problem has been solved with particle swarm optimization (PSO) and compared their results with genetic algorithm (GA). Examples are shown to illustrate the method. Finally statistical simulation has been performed for supremacy the approach.     

Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design

Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance; in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.     

电力企业供应商服务大厅增值税专用发票业务优化研究

为解决当前供应商大厅增值税业务办理时出现的业务办理等待时间长、服务质量下降、大厅超负荷运转以及业务人员压力大的问题。本文提出了一种依托“移动互联”和“大数据”技术的业务优化方案,将发票业务运作模式进行全面革新：首先,将纸质发票信息采集流程由线下转移到线上,发票信息采集由现场扫描变革为网上登记;然后,通过设定安全隔离网闸解决内外网安全问题,将登记的信息传到至ERP系统,业务人员通过内网ERP系统即可完成对发票的远程预验审;推出现场业务办理预约模式,利用大数据技术智能分析海量发票信息和ERP合同数据,科学设定预约量上限和时段,引导供应商有计划地办理业务。通过与国税系统直连,实现后台批量自动化认证。解决了当前供应商服务大厅增值税发票业务办理时的诸多难题,促进营商环境的优化。同时,与未来发票的全面电子化完成超前对接。     

Synthesis and optimization of the recovery route for residual products under uncertain product demand

The present work describes an optimization model for managing the recovery of residual products that originate at industrial plants. The framework for the proposed general network superstructure, where all possible process transformations, storage, transports and auxiliary operations are accounted for, is modeled using a maximal state task network representation. This framework is combined with the evaluation of a set of environmental impacts, quantified by metrics (for air, water pollution, etc.) through the minimum environment impact analysis methodology and is associated with waste generation at utility production and transportation levels. The final model is described as a mixed-integer linear programming model, which, once solved, is able to suggest the optimal processing and transport routes, while optimizing a given objective function and meeting design and environmental constraints. For each solution obtained, a stochastic flexibility index is computed, allowing for the drawing of trade-off curves for investment decision support.     

A DEA benchmarking methodology for project planning and management of new product development under decentralized profit-center business model

In today’s highly competitive marketplace, technology-driven organizations widely adopt decentralized profit-center business model. In order to complete a series of new product development (NPD) activities on time and within budgetary constraints, the NPD managers need an objective benchmarking approach to gain accurate perception on the relations of resource allocations, profits, costs and times for each NPD activity. Thus, this study employs the data envelopment analysis (DEA) concept to put forward a benchmarking planning and management methodology to optimize the NPD activities within a profit center for achieving the goal of maximal profit and satisfying the resource constraints. By applying the real case of the electric motor scooter NPD project, this research demonstrates the method’s real case application with superior results, comparing to other existing approaches.     

Active learning strategy for smart soft sensor development under a small number of labeled data samples

This contribution proposes a new active learning strategy for smart soft sensor development. The main objective of the smart soft sensor is to opportunely collect labeled data samples in such a way as to minimize the error of the regression process while minimizing the number of labeled samples used, and thus to reduce the costs related to labeling training samples. Instead of randomly labeling data samples, the smart soft sensor only labels those data samples which can provide the most significant information for construction of the soft sensor. In this paper, without loss of generality, the smart soft sensor is built based on the widely used principal component regression model. For performance evaluation, an industrial case study is provided. Compared to the random sample labeling strategy, both accuracy and stability have been improved by the active learning strategy based smart soft sensor.