Product family flexibility design method based on hybrid adaptive ant colony algorithm

Wei, Wei; Tian, Zhenyu; Peng, Chong; Liu, Ang; Zhang, Zhinan

doi:10.1007/s00500-018-3622-y

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…But we also want to allow a partial standardization of components to a subset of product variants (generalized commonality). We do not want to prescribe a certain commonality pattern (Wei et al, 2019), but receive an optimized commonality pattern as an output. With the rim allowing only integer values, the problem statement includes discrete design variable values.…”

Section: Related Researchmentioning

confidence: 99%

Cost Optimization of Product Families Using Solution Spaces: Application to Early-Stage Electric Vehicle Design

2022

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Companies offer products in different variants to reach more customers. This increases internal variety and cost. However, reducing those cost is difficult due to complexity. Complexity arises from: combinatorics; many design variables interacting with each other; coupling of technical and economical perspectives. This paper presents an approach based on (1) building a complex system model of modular models; (2) identifying the potential for standardization from a technical perspective; (3) cost-optimizing the degree of standardization. A product family of electric vehicles was optimized.

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Section: Related Researchmentioning

confidence: 99%

Cost Optimization of Product Families Using Solution Spaces: Application to Early-Stage Electric Vehicle Design

2022

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“…Fellini et al (2006) first optimize each product individually and perform a cluster analysis to identify standardizations that are likely to be relevant. Wei et al (2019) compute the sensitivity of the product performances with respect to each design variables and chose the variables with little influence as standard variables. Besides the optimization heuristics they use, the optimization algorithms differ in the way they organize the optimization of the commonality scheme and the design variables.…”

Section: Approaches For Quantitative Optimization Of Scale-based Product Familiesmentioning

confidence: 99%

“…To model the adverse effect of a too high commonality, Simpson and D'Souza (2004), Fellini et al (2006), Li and Huang (2009), Khajavirad et al (2009), Liu et al (2011) or Chowdhury et al (2013) perform a multi-objective optimization to maximize the commonality as well as a measure of the product performance. Some of them build a Pareto-front between those objectives (Simpson and D'Souza, 2004;Chen and Wang, 2008;Li and Huang, 2009;Wei et al, 2019), while other aggregate them in a meta-objective function (Khajavirad et al, 2009;Liu et al, 2011;Chowdhury et al, 2013). In both cases, the cost or profit are not explicitly estimated and the decision making therefore heavily relies on human judgement.…”

Section: Approaches For Quantitative Optimization Of Scale-based Product Familiesmentioning

confidence: 99%

“…On the contrary, in one-level algorithms, both the commonality scheme and the design variables are optimized at the same time. Approaches that eliminate candidate solutions, such as Fellini et al (2006), Chen and Wang (2008), Wei et al (2019), Simpson and D'Souza (2004), Liu et al (2011) or Wei et al (2019) reduce the dimension of the problem, but also exhibit two weaknesses: First, the efficiency of the optimization algorithms used for such reduced problem formulations is not proven. Second, the mathematical modelling of the commonality scheme required to embed it within the decision vector is not suitable for a problem with a generalized commonality.…”

Section: Approaches For Quantitative Optimization Of Scale-based Product Familiesmentioning

confidence: 99%

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Two-Level Optimization of Product Families: Application to a Product Family of Water Hose Boxes

et al. 2021

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Increasing product complexity and individual customer requirements make the design of optimal product families difficult. Numerical optimization supports optimal design but must deal with the following challenges: many design variables, non-linear or discrete dependencies, and many possibilities of assigning shared components to products. Existing approaches use simplifications to alleviate those challenges. However, for use in industrial practice, they often use irrelevant commonality metrics, do not rely on the actual design variables of the product, or are unable to treat discrete variables. We present a two-level approach: (1) a genetic algorithm (GA) to find the best commonality scheme (i.e., assignment scheme of shared components to products) and (2) a particle swarm optimization (PSO) to optimize the design variables for one specific commonality scheme. It measures total cost, comprising manufacturing costs, economies of scales and complexity costs. The approach was applied to a product family consisting of five water hose boxes, each of them being subject to individual technical requirements. The results are discussed in the context of the product family design process.

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“…HKES consists of two subsystems: forward Kansei Engineering system and backward Kansei Engineering system [8]. Wu proposed a preference-based evaluation-fuzzy- Wei proposed a hybrid adaptive ant colony algorithm to realize product family multi-objective optimization design through scale-based product platform theory model [4]. Lei presented a Decision Support System (DSS) for market-driven product positioning and design, based on market data and design parameters.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimized Product Image Design Integrated Decision System Based on Kansei Engineering

Lei

Zhang³

2020

Applied Sciences

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In order to facilitate the development of product image design, the research proposes the optimized product image design integrated decision system based on Kansei Engineering experiment. The system consists of two sub-models, namely product image design qualitative decision model and quantitative decision model. Firstly, using the product image design qualitative decision model, the influential design elements for the product image are identified based on Quantification Theory Type I. Secondly, the quantitative decision model is utilized to predict the product total image. Grey Relation Analysis (GRA)–Fuzzy logic sub-models of influential design elements are built up separately. After that, utility optimization model is applied to obtain the multi-objective product image. Finally, the product image design integrated decision system is completed to optimize the product image design in the process of product design. A case study of train seat design is given to demonstrate the analysis results. The train seat image design integrated decision system is constructed to determine the product image. This shows the proposed system is effective and for predicting and evaluating the product image. The results provide meaningful improvement for product image design decision.

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Product family flexibility design method based on hybrid adaptive ant colony algorithm

Cited by 10 publications

References 20 publications

Cost Optimization of Product Families Using Solution Spaces: Application to Early-Stage Electric Vehicle Design

Cost Optimization of Product Families Using Solution Spaces: Application to Early-Stage Electric Vehicle Design

Two-Level Optimization of Product Families: Application to a Product Family of Water Hose Boxes

Research on Optimized Product Image Design Integrated Decision System Based on Kansei Engineering

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