Product Family Configuration Research Articles

Self-X heterogeneous attributed graph embedding-based product configuration framework for cognitive mass personalization

Cognitive mass personalization (CMP) is a promising manufacturing paradigm; equipped with cognitive capabilities like reasoning, CMP satisfies changeable needs via configuring personalized products at scale. In CMP, knowledge graphs (KGs) are exploited by smart product-service systems (SPSS) to support cognitive configuration/reconfiguration processes. However, the extant KG-enabled SPSSs are built upon fixed configurations and hybrid frameworks due to lacking a graph embedding (GE) model to render cognitive configuration decisions. In fact, GE is scarcely used in SPSS configuration, because it is not only compromised by the heterogeneity of KGs entailed by content-related specifications and complex structures but also influenced by the feature randomness and feature drift problems, which are triggered by accumulative errors and inconsistent objectives due to noisy assignments and different configuration tasks, separately. To address these limitations, a Self-X Heterogeneous Attributed Graph Embedding (SXHAGE) model is proposed in a Self-X architecture, which includes 1) self-attention graph attention networks, 2) a self-adaptive autoencoder, and 3) self-optimizing training objectives, to present heterogeneous data through jointly optimizing heterogeneous attributed entities and relations. A systematic SXHAGE-based configuration framework, in which product family design and configuration recommending are enabled by graph clustering and link prediction, is developed as a continuous updating loop to proactively configure personalized products. A real-world case study, i.e., configure personalized electric clippers via a web-based sustainable configuration platform, is performed to validate the applicability of the proposed framework in the CMP context. Moreover, extensive experiments on the case study dataset demonstrate the superiority of SXHAGE over the state-of-the-art algorithms, e.g., surpassing Deep Neighbor-Aware Embedding (DNENC) by 18 % in F1-score for graph clustering and by 5 % in ROC-AUC for link prediction.

Product family modeling technology for customized cosmetic packaging design based on basic-element theory

BackgroundAs the market demands change, SMEs (small and medium-sized enterprises) have long faced many design issues, including high costs, lengthy cycles, and insufficient innovation. These issues are especially noticeable in the domain of cosmetic packaging design. ObjectiveTo explore innovative product family modeling methods and configuration design processes to improve the efficiency of enterprise cosmetic packaging design and develop the design for mass customization. MethodsTo accomplish this objective, the basic-element theory has been introduced and applied to the design and development system of the product family. ResultsBy examining the mapping relationships between the demand domain, functional domain, technology domain, and structure domain, four interrelated models have been developed, including the demand model, functional model, technology model, and structure model. Together, these models form the mechanism and methodology of product family modeling, specifically for cosmetic packaging design. Through an analysis of a case study on men’s cosmetic packaging design, the feasibility of the proposed product family modeling technology has been demonstrated in terms of customized cosmetic packaging design, and the design efficiency has been enhanced. ConclusionThe product family modeling technology employs a formalized element as a module configuration design language, permeating throughout the entire development cycle of cosmetic packaging design, thus facilitating a structured and modularized configuration design process for the product family system. The application of the basic-element principle in product family modeling technology contributes to the enrichment of the research field surrounding cosmetic packaging product family configuration design, while also providing valuable methods and references for enterprises aiming to elevate the efficiency of cosmetic packaging design for the mass customization product model.

Toward sustainable joint optimisation for product family and supply chain configuration with smart contracting consideration

This article underscores the necessity for sustainable and environmentally friendly manufacturing practices in product family configuration (PFC) projects, which are paramount to the global economy. Nevertheless, conventional approaches often fixate solely on design aspects, overlooking downstream supply chain configuration (SCC) considerations and the corresponding environmental benefits. Consequently, there is an escalating demand for an integrated optimisation approach that encompasses both PFC and SCC to realise economic and environmental advantages. This study delves into a methodology that integrates blockchain smart contracts as binary 0–1 variables with waste recycling and utilisation, yielding a comprehensive multi-objective model. The proposed methodology seamlessly incorporates considerations for both PFC and SCC. Furthermore, a nested leader-follower optimisation algorithm, based on the non-dominated sorting genetic algorithm-II (NSGA-II), has been devised with the objective of achieving triple benefits: augmented profits, maintenance revenue, and diminished environmental emissions. In conclusion, this research contributes to the advancement of sustainable collaborative optimisation through the innovative utilisation of blockchain smart contracts and multi-level modelling. To demonstrate the effectiveness of the proposed methodology, it is applied to a 60 KW DC electric vehicle (EV) charging piles, accompanied by a sensitivity analysis to assess its management implications.

An optimization model for low-carbon product family design considering component reliability and warranty period

Due to the considerations of the carbon footprint of the product, low-carbon product family design has attracted more and more attention. The existing low-carbon product family design methods do not adequately take into account the component reliability and warranty period. At present, providing warranty for key components of the product is an important marketing tool, and it is widely adopted by manufacturers. In fact, when component reliability and warranty period are considered in a low-carbon product family design, they affect not only the profit of the product family, but also the GHG emission. To this end, the article proposes an optimization model that optimizes product family design, supplier selection and component warranty period in parallel. The objective of the model is to maximize the profit of the product family and minimize GHG emission while satisfying various constraints. The developed model is solved using a genetic algorithm. The method’s effectiveness is verified through a case study, and a sensitivity analysis is performed. The method can assist manufacturers in simultaneously determining the product family configuration, supplier selection and the component warranty period based on their goal preferences.

Joint optimization of low-carbon product family configuration and smart production line selection

To meet the requirements of small batch orders and rapid production, many enterprises began to use robots, artificial intelligence, cloud computing, and other new technologies to construct smart production lines, which are high-flexible and capable of autonomously diagnosing their health and autonomously designing continuous improvement projects. In this study, a joint optimization model is established for concurrently determining the optimal product family configuration and the optimal construction plan for a smart production line. Greenhouse gas emissions are considered in the joint optimization model to realize the low-carbon design concept. A quantum evolutionary algorithm and a genetic algorithm are developed to solve the established optimization model. A case study of the electronic dictionary product family is provided to illustrate the proposed method. The numeric experiments show that the proposed model can reap more profit from a systematic perspective compared with the traditional two-stage approach, and the proposed quantum evolutionary algorithm has a good performance in solving the joint optimization problem.

Product family configuration optimisation considering after-sale service: an adaptive quantum evolutionary algorithm approach

ABSTRACT By providing after-sale services, manufacturing companies can gain more chances to interact with customers, improve brand preference and loyalty, occupy new bases in the value chain, and create more cross-selling opportunities. Considering after-sale service, we propose a new method for product family configuration optimisation in this study. Mathematical models for product family configuration optimisation are established with the objective of maximising the overall profit of the family of products and after-sale services. An adaptive quantum evolutionary algorithm (AQEA) is developed to solve the established optimisation models. An example of an e-book reader product is used to illustrate the proposed method in a case study. An industry case study shows that: (1) the proposed method outperforms the traditional method; (2) the proposed AQEA has better performance when compared with the other three meta-heuristic algorithms; and (3) the proposed method is more useful for industrial cases where the proportion of service profit is relatively large.

Risk-Averse Co-Decision for Lower-Carbon Product Family Configuration and Resilient Supplier Selection

Due to global pandemics, political unrest and natural disasters, the stability of the supply chain is facing the challenge of more uncertain events. Although many scholars have conducted research on improving the resilience of the supply chain, the research on integrating product family configuration and supplier selection (PCSS) under disruption risks is limited. In this paper, the centralized supply chain network, which contains only one major manufacturer and several suppliers, is considered, and one resilience strategy (i.e., the fortified supplier) is used to enhance the resilience level of the selected supply base. Then, an improved stochastic bi-objective mixed integer programming model is proposed to support co-decision for PCSS under disruption risks. Furthermore, considering the above risk-neutral model as a benchmark, a risk-averse mixed integer program with Conditional Value-at-Risk (CVaR) is formulated to achieve maximum potential worst-case profit and minimum expected total greenhouse gases (GHG) emissions. Then, NSGA-II is applied to solve the proposed stochastic bi-objective mixed integer programming model. Taking the electronic dictionary as a case study, the risk-neutral solutions and risk-averse solutions that optimize, respectively, average and worst-case objectives of co-decision are also compared under two different ranges of disruption probability. The sensitivity analysis on the confidence level indicates that fortifying suppliers and controlling market share in co-decision for PCSS can effectively reduce the risk of low-profit/high-cost while minimizing the expected GHG emissions. Meanwhile, the effects of low-probability risk are more likely to be ignored in the risk-neutral solution, and it is necessary to adopt a risk-averse solution to reduce potential worst-case losses.

Joint decision-making of product family configuration and order allocation by coordinating suppliers under disruption risks

Although simultaneous configuration of product family and supply chains under disruption risks has been studied, the protection strategy to mitigate the impact of disruption and the supplier’s capacity constraint was still not well considered. This paper formulates a bi-level programming model for joint decision-making of product family configuration and order allocation (PCOA), in which the coordination strategy and the protection strategy are used to coordinate capacity-constrained suppliers and reduce supply chains risks, respectively. The upper level seeks for an optimal product configuration by maximising the equivalent utility value per cost of an entire product family. The lower level optimises the order allocation and the protection scheme to minimise the unit supply chain cost. Then, a bi-level optimisation algorithm GA-BAGA, which nested a two-stage method integrated Backtracking Algorithm and Genetic Algorithm in the upper Genetic Algorithm evolution, is developed to solve the proposed PCOA model. A case study of AGV forklift clarifies that the bi-level optimisation is feasible and effective for PCOA problem, and use of the coordination strategy and the protection strategy can reduce the supply chain cost and increase the utility value per cost.

Customization-Oriented Product Flexible Manufacturing Experience System Design Based on VR

It has gradually become the trend of the world to integrate the individualized and diversified needs of customers into products, which has brought new pressure to enterprises, and the diversification of users’ needs has led to the diversification of the market. Manufacturing industry must adjust the existing production mode to adapt to these changes. This paper puts forward the research and development of product flexible customization experience system based on VR considering users’ customization requirement. Through the research of data modularization modelling and dynamic product family model and configuration for product flexible customization, the functions and operation processes of typical industries are analysed. By using modularization idea, the general rules are found out from customer requirements, and the multi-layer module division and model from production system planning to equipment design are studied. Group coupling technology, explore customer modular customization methods and technologies for equipment manufacturing industry. Combining with a case of customization of logistics equipment system, this paper explores how to combine VR technology application with production system simulation technology, build a digital personalized flexible configuration experience system, provide an immersive visual environment of interactive design for customers, and improve the market competitiveness of enterprises.

A Simultaneous Optimization Framework for Product Family Configuration and Supply Chain Planning

In this paper, we address a simultaneous optimization framework for product family configuration and supply chain planning. The customer determines the product family configuration and the manufacturer determines the supply chain planning. A conceptual model is developed for the simultaneous optimization of product family configuration and supply chain planning incorporating customer’s behavior. The effectiveness of the proposed model is investigated.

Co-evolution of product family configuration and supplier selection: a game-theoretic bilevel optimisation approach

ABSTRACTJoint decision making of configuration design and supplier selection has been well recognised for successful development of modular product families. However, the prevailing practice for integrating supplier selection into product family design is limited to only one family generation and neglects the changes in customer demands and technological progress over time, which calls for a co-evolution design across product generations from a systematic perspective, instead of separating optima.In this paper, the co-evolution of configuration design and supplier selection for a product family is regarded as a distributed collaborative decision making, for which a Stackelberg game theoretic decision framework is proposed to model the interactions between product family planning in the current generation and product family re-planning in the next generation. Consistent with the Stackelberg game, a nonlinear bilevel optimisation model with a common deviation objective function, compromising an upper-level planning optimisation and a lower-level re-planning optimisation, is formulated. A corresponding nested bilevel genetic algorithm is developed to solve the optimisation model. A case study of mobile phone product family co-evolution with supplier selection is reported to illustrate the feasibility and potential of the proposed collaborative game theoretic approach.

Product Family Configuration Price Prediction Based On Neural Network

In order to quickly and effectively respond to customer enquiry in the dynamic changeable market, a new method for product family configuration price prediction is proposed based on neural network with adjustable number of hidden layer nodes. Based on product family configuration history of various product variants and the corresponding prices, the relationship model between the product configuration and price is established using neural network, which is utilized to predict the market price of the newly configured product variant for some random order.

Sustainable product family configuration based on a platform strategy

ABSTRACTA product family has been applied to minimise development costs and maximise profits. Since products in the product family have similar functions and product architecture, the product family has high potential for sustainable product in order to prolong the lifespan of the products by sharing new and remanufactured modules. Therefore, this paper proposes a method to identify sustainable product family configuration by integrating sustainability performance and a platform strategy. A concept of a sustainable platform is introduced to enhance reusability based on high compatibility in terms of space, function, and interface. The sustainable platform and remanufacturable modules are used as constraints in the proposed method. A multi-objective design optimisation approach is then applied to determine the optimal configuration of the product family in terms of sustainability. The sustainability is mathematically modelled to quantify three kinds of performance: environmental impact as environmental performance, profits as economic performance, and customer demand as social performance. To demonstrate the effectiveness of the proposed method, a case study is performed with a family of electric shavers. This paper shows how a sustainable product family can reduce negative environmental impact while satisfying customer demand and sustaining profit by using remanufactured modules.

Bilevel joint optimisation for product family architecting considering make-or-buy decisions

Product family architecting (PFA) aims at identification of common modules and selective modules to enable product family configuration for mass customisation. Due to nowadays manufacturers moving more towards assembly-to-order production throughout a distributed supply chain, the common practice of outsourcing of certain modules entails make-or-buy (MOB) decisions that must be taken into account in PFA. While the PFA and MOB decisions are enacted for different concerns of the manufacturer and the suppliers, it is important to deal with joint optimisation of the PFA and MOB problems. The prevailing decision models for joint optimisation are mainly originated from an ‘all-in-one’ approach that assumes both PFA and MOB decisions can be integrated into one single-level optimisation problem. Such an assumption neglects the complex trade-offs underlying two different decision-making problems and fails to reveal the inherent coupling of PFA and MOB decisions. This paper proposes to formulate joint optimisation of the PFA and MOB problems as a Stackelberg game, in which a bilevel decision mechanism model is deployed to reveal the inherent coupling and hierarchical relationships between PFA and MOB decisions. A nonlinear bilevel optimisation model is developed with the PFA problem acting as the leader and each MOB problem performing as a follower. A nested genetic algorithm is developed to solve the bilevel optimisation model. A case study of power transformer PFA subject to MOB considerations is presented to illustrate the feasibility and effectiveness of bilevel joint optimisation.

Joint optimization for coordinated configuration of product families and supply chains by a leader-follower Stackelberg game

Product family design by module configuration is conducive to accommodating product variety while maintaining mass production efficiency. Effective fulfillment of product families necessitates joint decision making of product family configuration (PFC) and downstream supply chain configuration (SCC), due to nowadays manufacturers’ moving towards assembly-to-order production throughout a distributed supply chain network. Existing decision models for joint optimization of product family and supply chain configuration are originated from an “all-in-one” approach that assumes both PFC and SCC decisions can be integrated into one optimization problem by aggregating two different types of objectives into a single objective function. Such an assumption neglects the complex tradeoffs underlying two different decision making problems and fails to reveal the inherent coupling of PFC and SCC.This paper formulates joint configuration of a product family and its supply chain as a leader-follower Stackelberg game that is enacted through a bi-level hierarchical optimization mechanism to model the coordination between two self-interested decision makers for PFC and SCC. The PFC decisions are modeled as an upper-level optimization problem (called leader) for optimal selection of modules, module instances, and product variants. The SCC decisions are modeled as a lower-level optimization problem (called follower), which responds to decisions of the upper level in order to determine an optimal supply chain configuration and inventory policies. A nonlinear, mixed integer programming model is formulated for the bi-level joint decision in the leader-follower game. To solve the nonlinear optimization model, a bi-level, nested genetic algorithm with constraint reasoning is developed and implemented. A case study of a power transformer product family and supply chain configuration is reported to demonstrate the feasibility and potential of the proposed leader-follower game-theoretic method.

Configuration Model Research Based on Product Family Configuration Unit

Explaining how to set up configuration model from the Angle of product family, for the composition of product configuration model of product family structure was analyzed, put forward the definition of product family configuration unit, and used it as the basic elements of product family configuration model. Using various granularity of product family configuration units set to do the unified expression for function, the principle and structure of all products contained in the product family. And did further analyze for the characteristic, types, and associated constraints of product family configuration units. Finally, described the modeling process of product family configuration model. Do verification by the dumper.

Joint optimization of product family configuration and scaling design by Stackelberg game

Product family design is generally characterized by two types of approaches: module-based and scale-based. While the former aims to enable product variety based on module configuration, the latter is to variegate product design by scaling up or down certain design parameters. The prevailing practice is to treat module configuration and scaling design as separate decisions or aggregate two design problems as a single-level, all-in-one optimization problem. In practice, optimization of scaling variables is always enacted within a specific modular platform; and meanwhile an optimal module configuration depends on how design parameters are to be scaled. The key challenge is how to deal with explicitly the coupling of these two design optimization problems.This paper formulates a Stackelberg game theoretic model for joint optimization of product family configuration and scaling design, in which a bilevel decision structure reveals coupled decision making between module configuration and parameter scaling. A bilevel mixed 0–1 non-linear programming model is developed and solved, comprising an upper-level optimization problem and a lower-level optimization problem. The upper level seeks for an optimal configuration of modules and module attributes by maximizing the shared surplus of an entire product family. The lower level entails parametric optimization of attribute values for optimal technical performance of each individual module. A case study of electric motors demonstrates that the bilevel joint optimization model excels in leveraging optimal scaling in conjunction with optimal module configuration, which is advantageous over the existing paradigm of product family scaling design that cannot change the product family configuration.

Extended DCSP Approach on Product Configuration with Cost Estimation

Constraint satisfaction problem (CSP) has been considered as an effective way to represent and solve product family configuration tasks. However, classical CSP-based configuration models are not powerful enough to capture the inherent nature of configuration tasks such as undetermined component number and component structure. We propose an extended dynamic constraint satisfaction (DCSP) scheme that captures and takes advantage of the hierarchy of constituents and derivation relationships among constituents from the view of a product family such that the constraint network of DCSP-based configuration extends incrementally. As cost is a critical criterion for successful configuration, we integrate cost estimation into our DCSP-based configuration scheme to provide enhanced support for product configuration. We also discuss mapping relationships among product data models and the concepts of our DCSP-based configuration scheme to facilitate data conversion. An augmented backtracking searching process is also proposed based on the extended DCSP-based configuration system.

Module Selection and Supply Chain Optimization for Customized Product Families Using Redundancy and Standardization

In this paper, we study the problem of configuring a product family which has to satisfy diversified customer requirements. Modular design strategies allow a bill of materials to be generated for various finished products from a limited subset of modules. Simultaneously, a production location must be selected for the manufacture of each module. From a product point-of-view, the strategies adopted are often extreme, proposing either the manufacturing of the total diversity (all possible products) or a limited set of standardized products (only a few products are proposed). The objective of this paper is to investigate intermediate cases on this continuum, in order to better understand the potential for profit. Our comparison is based on the product family configuration (selection of the best modules) that minimizes production and transportation costs under time constraints.

Configuration Design Method for Product Family Based on Extension Case Reasoning

To improve the efficiency and accuracy in the process of case retrieval for the product family configuration design, a case retrieval method based on the extension distance is presented. Extension distance contains numeric to numeric, numeric to set and between sets. The difference between the retrieval features is the main standard. Then the extension similarity model is built on the basis of extension distance to meet the configuration demand. And the standard deviation related with the similarity is introduced to distribute the dynamic attribute’s weights which also considers the customer’s preference. A chain saw is used as a case to verify the usefulness and effectiveness. It is demonstrated that the method can process the precise and imprecise information effectively in case retrieval. With VC++6.0 and Solidworks, the configuration system is designed to realize the method mentioned.