Multidisciplinary design optimization of vehicle chassis product family

In order to improve the performance of automotive product platforms and product families while keeping high development efficiency, a product family optimization design method that combines shared variable decision-making and multidisciplinary design optimization (MDO) is proposed. First, the basic concepts related to product family design optimization were clarified. Then, the mathematical description and MDO model of the product family optimization problem were established, and the improved product family design process was given. Finally, for the chassis product family optimization problem of an automotive product platform, the effectiveness of the proposed optimization method, and design process were exemplified. The results show that the collaboratively optimized product family can effectively handle the coordination between multiple products and multiple targets, compared to Non-platform development, it can maximize the generalization rate of vehicle parts and components under the premise of ensuring key performance, and give full play to the advantages of product platforms.

Multidisciplinary Analysis and Product Family Optimization of Front-Loading Washing Machines

In the past decade, the market share of front-loading washing machines has seen explosive growth in the United States. As a result, many companies are now offering families of front-loading washing machines with a variety of features and options. Understanding the tradeoffs within these product families is challenging as existing research has focused primarily on a single disciplinary analysis (e.g., dynamic analysis, strength analysis); few models exist for cleanliness, reliability, energy efficiency, etc. In this paper, we introduce a new integrated multidisciplinary analysis using simulations, mathematical models, and response surface models based on the ratings of product attributes. In order to determine feasible design solutions for a front-loading washer family, we formulate a product family design problem using deviation functions and a product family penalty function. A multi-objective genetic algorithm is applied to solve the proposed formulation, and the results indicate that designers can successfully determine solutions for the best performance, most common, and compromise families of front-loading washers.

Genetic Algorithm for Mixed Integer Nonlinear Bilevel Programming and Applications in Product Family Design

Many leader-follower relationships exist in product family design engineering problems. We use bilevel programming (BLP) to reflect the leader-follower relationship and describe such problems. Product family design problems have unique characteristics; thus, mixed integer nonlinear BLP (MINLBLP), which has both continuous and discrete variables and multiple independent lower-level problems, is widely used in product family optimization. However, BLP is difficult in theory and is an NP-hard problem. Consequently, using traditional methods to solve such problems is difficult. Genetic algorithms (GAs) have great value in solving BLP problems, and many studies have designed GAs to solve BLP problems; however, such GAs are typically designed for special cases that do not involve MINLBLP with one or multiple followers. Therefore, we propose a bilevel GA to solve these particular MINLBLP problems, which are widely used in product family problems. We give numerical examples to demonstrate the effectiveness of the proposed algorithm. In addition, a reducer family case study is examined to demonstrate practical applications of the proposed BLGA.

Validating the Generational Variety Index (GVI) Through Product Family Optimization: A Preliminary Study

Effective product platforms must strike an optimal balance between commonality and variety. Increasing commonality can reduce costs by improving economies of scale while increasing variety can improve market performance, or in our robot family example, satisfy various robot missions. Two metrics that have been developed to help resolve this tradeoff are the Generational Variety Index (GVI) and the Product Family Penalty Function (PFPF). GVI provides a metric to measure the amount of product redesign that is required for subsequent product offerings, whereas PFPF measures the dissimilarity or lack of commonality between design (input) parameters during product family optimization. GVI is examined because it is the most widely used metric applicable during conceptual development to determine platform components. PFPF is used to validate GVI because of its ease of implement for parametric variety, as used in this case. This paper describes a product family trade study that has been performed using GVI for a robot product family and compares the results to those obtained by optimizing the same family using PFPF. This work provides a first attempt to validate the output of GVI by using a complementary set of results obtained from optimization. The results of this study indicate that while there are sometimes similarities between the results of GVI and optimization using PFPF, there is not necessarily a direct correlation between these two metrics. Moreover, the platform recommended by GVI is not necessarily the most performance-optimized platform, but it can help improve commonality. In the same regard, PFPF may miss certain opportunities for commonality. The benefits of integrating the two approaches are also discussed.

A Decomposed Gradient-Based Approach for Generalized Platform Selection and Variant Design in Product Family Optimization

A core challenge in product family optimization is to jointly determine (1) the optimal selection of components to be shared across product variants and (2) the optimal values for design variables that define those components. Each of these subtasks depends on the other; however, due to the combinatorial nature and high computational cost of the joint problem, prior methods have forgone optimality of the full problem by fixing the platform a priori, restricting the platform configuration to all-or-none component sharing, or optimizing the joint problem in multiple stages. In this paper, we address these restrictions by (1) introducing an extended metric to account for generalized commonality, (2) relaxing the metric to the continuous space to enable gradient-based optimization, and (3) proposing a decomposed single-stage method for optimizing the joint problem. The approach is demonstrated on a family of ten bathroom scales. Results indicate that generalized commonality dramatically improves the quality of optimal solutions, and the decomposed single-stage approach offers substantial improvement in scalability and tractability of the joint problem, providing a practical tool for optimizing families consisting of many variants.