In the context of mass customization business activities, product configuration (PC) can assist companies in quickly occupying the market and improving their competitive advantage. In this paper, contractors are defined as outsourcing companies for producers and manufacturers that do not possess self-developed product module technology and who are more knowledgeable and discerning consumer representatives. This study focused on a PC approach that includes optional, redundant, and common modules. In this approach, three objective functions were considered and solved using NSGA-II: warranty profit, performance-price, and environmental emissions. Warranty profit is calculated by forcing the product failure probability to satisfy a power-law distribution and the user selection behavior to satisfy a multinomial logit (MNL) model. Kano’s model-based user satisfaction and the relative importance of the product module instance are used to calculate performance-price. The environmental emissions were determined from both production and assembly emissions of the module instance. Additionally, a chromosome encoding method and a chromosome crossover operation were designed for the PC problem. Finally, the feasibility and effectiveness of the proposed method were verified by taking an electric vehicle DC charging pile configuration project as an example, and discussing and explaining the practical significance of each parameter in the example.

Configuring products using NSGA-II: warranty profits, performance–price, and environmental emissions from the contractor perspective

Product configuration design combines the existing configurable components of enterprises to quickly form a new product that meets customised requirements. However, the representation, maintenance, and update of the configuration knowledge, and the mismatch problem restrict the rationality and efficiency of existing configuration design methods. In this paper, a recommendation model is developed for customised product configuration design, which takes personalised customer requirements and product component information as input and outputs a ranked list of component instances. It consists of two sub-models: a retrieval sub-model and a ranking sub-model. The retrieval sub-model selects a set of component instance candidates from all possible candidates, and then the ranking sub-model ranks them and selects the best possible candidate. To boost the ranking sub-model performance, we propose a novel interacting network, DualAdap, to extract meaningful low-order, high-order, and adaptive-order cross features. Based on learned cross features, the ranking sub-model computes the adoption scores of all candidates and then selects the best possible candidate according to adoption scores. The configuration design of the elevator traction machine is taken as a case study. Results verify that our recommendation model can identify similar component instances and then accurately pick out the best possible candidate that meets customer requirements. 

A factorisation-based recommendation model for customised products configuration design

Customized product design information feedback technology based on tentative design chain reconstruction


 The configuration of electronic products is an important means to meet the diverse and personalized needs of users and achieve mass customization, and one of its goals is to recommend an excellent bill of material to users according to users’ individualized needs and preferences. Current research describes the configuration of electronic products as a single-objective optimization model, which suffers from the problems of single recommended configuration and difficulty in meeting the dynamic adjustment of user preferences. Therefore, we describe it as a multi-objective optimization model and propose an NSGA-III-FR algorithm to solve the model. In order to balance the convergence and diversity of the algorithm, NSGA-III-FR has made two improvements on the basis of NSGA-III with information feedback models: introducing adaptive parameters to balance NSGA-III-F1 and NSGA-III-R1; and using Angle Penalized Distance (APD) to improve the niche technology. The experimental results show that our modified meth-od can achieve better performance compared with the other three algorithms.
 


The Configuration Design of Electronic Products Based on improved NSGA-III with Information Feedback Models

With the emergence of the fourth industrial revolution, market globalization, and growing customer demands, companies are being forced to rethink their ways of doing business to remain competitive. Small and medium-sized enterprises (SMEs) in the manufacturing sector must also adapt to personalized customer demands. This context forces companies to migrate towards mass customization. The literature proposes several strategies for adapting to this new paradigm but does not offer an implementation sequence for successfully operationalizing mass customization within an SME. Based on a systematic review of the themes surrounding Industry 4.0 and mass customization in the literature, this article aims to highlight the different strategies and factors to be put in place to successfully implement mass customization. This research reveals the lack of a prioritization of factors that favour the operationalization of mass customization. Lastly, the literature does not detail the tools and their levels of maturity resulting from the factors to be implemented. This article highlights the gaps in the literature related to mass customization.

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Operationalizing Mass Customization in Manufacturing SMEs—A Systematic Literature Review

Configuration design is a process of composing customized product from a set of predefined component types with a set of well-defined rules which decide how items can be selected and combined. The configuration solving process is to instantiate all component types into instances. However, the configurator faces the problem of how to determine the instantiation order of component types in the configuration solving process. An unreasonable instantiation order may reduce the accuracy of the configuration result and extend the time required to finish the configuration. In response to the above problem, a heuristic configuration solving process planning method by imitating the crystal crystallization process (CCP) was proposed. According to the product configuration rules, the frequent itemset mining algorithm was used to mine the correlation between the attribute parameters of the product configuration unit. By imitating the CCP and utilizing the calculated coupling strength, the configurator will determine a suitable instantiation sequence for component types. This method has been applied in the GB10 elevator produced by Canny Elevator. It is found that the configuration accuracy has been improved and the consumption of time has been reduced.

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Ge Wenqi

Papers

A heuristic configuration solving process planning method for mechanical product configuration by imitating the crystal crystallization process