An efficient decomposed multiobjective genetic algorithm for solving the joint product platform selection and product family design problem with generalized commonality

Product family optimization involves not only specifying the platform from which the individual product variants will be derived, but also optimizing the platform design and the individual variants. Typically these steps are performed separately, but we propose an efficient decomposed multiobjective genetic algorithm to jointly determine optimal (1) platform selection, (2) platform design, and (3) variant design in product family optimization. The approach addresses limitations of prior restrictive component sharing definitions by introducing a generalized two-dimensional commonality chromosome to enable sharing components among subsets of variants. To solve the resulting high dimensional problem in a single stage efficiently, we exploit the problem structure by decomposing it into a two-level genetic algorithm, where the upper level determines the optimal platform configuration while each lower level optimizes one of the individual variants. The decomposed approach improves scalability of the all-in-one problem dramatically, providing a practical tool for optimizing families with more variants. The proposed approach is demonstrated by optimizing a family of electric motors. Results indicate that (1) decomposition results in improved solutions under comparable computational cost and (2) generalized commonality produces families with increased component sharing under the same level of performance. A preliminary version of this paper was presented at the 2007 AIAA Multidisciplinary Design Optimization Specialists Conference.     

Product platform two-stage quality optimization design based on multiobjective genetic algorithm

Product platform design (PFD) has been recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. Numerous optimization-based approaches have been proposed to help resolve the tradeoff between platform commonality and the ability to achieve distinct performance targets for each variant. In this study, we propose a two-stage multiobjective optimization-based platform design methodology (TMOPDM) for solving the product family problem using a multiobjective genetic algorithm. In the first stage, the common product platform is identified using a nondominated sorting genetic algorithm II (NSGA-II); In the second stage, each individual product is designed around the common platform such that the functional requirements of the product are best satisfied. The design of a family of traction machine is used as an example to benchmark the effectiveness of the proposed approach against previous approachs.     

A manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm

With highly fragmented market and increased competition, platform-based product family design has been recognized as an effective method to construct a product line that satisfies diverse customer’s demands while aiming to keep design and production cost-effective. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss. In this paper, a systematic multi-platforming product family approach is proposed to design a scale-based product family. In the light of the basic premise that increased commonality implies enhanced manufacturing efficiency, we present an effective platform decision strategy to quantify family design configuration using a commonality index that couples design varieties with production variation. Meanwhile, unlike many existing methods that assume a single given platform configuration, the proposed method addresses the multi-platforming configuration across the family, and can generate alternative product family solutions with different levels of commonality. A modified genetic algorithm is developed to solve the aggregated multiobjective optimization problem and an industrial example of a planetary gear train for drills is given to demonstrate the proposed method.     

Product platform design through sensitivity analysis and cluster analysis

Scale-based product platform design consists of platform configuration to decide which variables are shared among which product variants, and selection of the optimal values for platform (shared) and non-platform variables for all product variants. The configuration step plays a vital role in determining two important aspects of a product family: efficiency (cost savings due to commonality) and effectiveness (capability to satisfy performance requirements). Many existing product platform design methods ignore it, assuming a given platform configuration. Most approaches, whether or not they consider the configuration step, are single-platform methods, in which design variables are either shared across all product variants or not shared at all. In multiple-platform design, design variables may be shared among variants in any possible combination of subsets, offering opportunities for superior overall design but presenting a more difficult computational problem. In this work, sensitivity analysis and cluster analysis are used to improve both efficiency and effectiveness of a scale-based product family through multiple-platform product family design. Sensitivity analysis is performed on each design variable to help select candidate platform design variables and to provide guidance for cluster analysis. Cluster analysis, using performance loss due to commonization as the clustering criterion, is employed to determine platform configuration. An illustrative example is used to demonstrate the merits of the proposed method, and the results are compared with existing results from the literature.     

Multiple platforms design and product family process planning for combined additive and subtractive manufacturing

Industry 4.0 promotes the utilization of new exponential technologies such as additive manufacturing in responding to different manufacturing challenges. Among these, the integration of additive and subtractive manufacturing technologies can play an important role and be a game changer in manufacturing products. In addition, using product platforms improves the efficiency and responsiveness of manufacturing systems and is considered an enabler of mass customization. In this paper, a model to design multiple platforms that can be customized using additive and subtractive manufacturing to manufacture a product family cost-effectively is proposed. The developed model is used to determine the optimal number of product platforms, each platform design (i.e. its features set), the assignment of each platform to various product variants, and the macro process plans for customizing the platforms while minimizing the overall product family manufacturing cost.The multiple additive/subtractive platforms and their process plans are determined by considering not only the commonality between the product variants but also their various manufacturing cost elements and the customer demand of each variant. The design of multiple product family platforms and their process plans is NP-hard problem. A genetic algorithm-based model is developed to reduce the computational complexity and find optimal or near optimal solution. Two case studies are used to illustrate the developed multiple platform model. The model results were compared with a single platform model in literature and the results demonstrate the multiple platform model superiority in manufacturing product families in lower cost. The use of the developed model enables manufacturing product families cost efficiently and allows manufacturers to manage diversity in products and market demands.     

Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

A method to optimize the scope of a software product platform based on end-user features

ContextDue to increased competition and the advent of mass customization, many software firms are utilizing product families – groups of related products derived from a product platform – to provide product variety in a cost-effective manner. The key to designing a successful software product family is the product platform, so it is important to determine the most appropriate product platform scope related to business objectives, for product line development.AimThis paper proposes a novel method to find the optimized scope of a software product platform based on end-user features.MethodThe proposed method, PPSMS (Product Platform Scoping Method for Software Product Lines), mathematically formulates the product platform scope selection as an optimization problem. The problem formulation targets identification of an optimized product platform scope that will maximize life cycle cost savings and the amount of commonality, while meeting the goals and needs of the envisioned customers’ segments. A simulated annealing based algorithm that can solve problems heuristically is then used to help the decision maker in selecting a scope for the product platform, by performing tradeoff analysis of the commonality and cost savings objectives.ResultsIn a case study, PPSMS helped in identifying 5 non-dominated solutions considered to be of highest preference for decision making, taking into account both cost savings and commonality objectives. A quantitative and qualitative analysis indicated that human experts perceived value in adopting the method in practice, and that it was effective in identifying appropriate product platform scope.     

Product platform design to improve commonality in custom products

Many companies find it difficult to maintain commonality and economies of scale in products with strict customer design requirements that may vary greatly from contract-to-contract or piece-to-piece. These strict and varied requirements typically result in highly customized products that are costly to manufacture, involve small production runs, and require long delivery times. In this paper, we discuss how the strategic incorporation of product platforms into the design process can leverage the design effort of individually customized products. The example involves the design of cross-sections for yokes used to mount valve actuators in the nuclear power industry. Through this example we demonstrate the process of creating a market segmentation grid, choosing a targeted segment, creating a product platform for the yoke cross-section, and subsequently defining the yoke product family using the product platform concept exploration method. The end result is a platform-based product family that will improve response to customer requests, reduce design and manufacturing costs, and improve time to market for companies that make small production runs of highly customized products.     

A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration

Product family design is a popular approach adopted by manufacturers to increase their product varieties in order to satisfy the needs of various markets. In recent years, because of increasing environmental concerns in societies and strict regulations of environmental protection, quite a number of manufacturers adopted remanufacturing strategy in their product development in response to the challenges. Remanufacturing of used products unavoidably involves a closed-loop supply chain system. To achieve the best outcomes, the supply chain design should be considered in product family design process. In this research, a multi-objective optimization model of integrated product family and closed loop supply chain design is formulated based on a cooperative game model for minimizing manufacturer’s total cost and maximize suppliers’ total payoffs. Since the optimization problem could be a large- scale one and involves mixed continuous-discrete variables, a new version of nondominated sorting genetic algorithm-II (NSGA-II), namely cooperative negotiation embedded NSGA-II (NSGA-CO), is proposed to solve the optimization model. Simulation tests are conducted to validate the effectiveness of the proposed NSGA-CO. The test results indicate that the proposed NSGA-CO outperforms NSGA-II in solving various scale of multi-objective optimization problems in terms of convergence. With the formulated optimization model and the proposed NSGA-CO, a case study of integrated product family and supply chain design is conducted to investigate the effects of environmental penalty, quantity of demand and marginal cost of remanufacturing on used product return rate, manufacturers’ and suppliers’ profits and joint payoff.     

Progressive sharing of modules among product variants

Recent market transition from mass production to mass customization forces manufacturers to design products that meet individual requirements. In order to address the high cost of this practice, manufacturers develop product families with a common platform, whose variants are designed to meet different customer demands. Parallel to this transition, the dynamics of the market forces designers to develop products composed of modules that are standardized as much as possible across products, thus can be more resilient than complete designs in a changing world.Starting from an original set of different components, our method designs a modular common platform and additional modules, shared by subsets of the designs, from which variants are composed.We applied the method to the layout design of a set of products. Consequently, the geometric aspect of the product family optimization is emphasized, but functional aspects related to the product features and to customer needs are also addressed due to their manifestation in the layout. The design search space is explored using shape grammar rules that alter component geometry and therefore, functionality. The search for optimal design is performed using simulated annealing. Given different objective formulations or parameter settings, the method can be used to explore the solution space. A simple example problem demonstrates the feasibility of the method.     

聚类分析法在产品族设计中的应用研究

研究应用聚类分析的手段解决模块化产品族设计中模块和核心平台（Product Platform)稳定的问题。通过分析产品开发过程以及部件关联矩阵，以部件之间关联的紧密程度作为聚类指标。应用聚类分析法，求得部件对不同类别的相对隶属度，从而为产品模块化和核心平台的划分提供科学分类依据。聚类分析法在产品族设计中的应用是模糊数学应用的新尝试，通过这种方法的运用，为产品族设计的数学分析和描述提价节定量的科学支持以及规范化的产品族设计过程。     

面向公共产品平台通用化的聚类分析方法研究

提出了一个平台体系结构的分层构造框架及一种平台元素的获取方式——基于图论的聚类分析方法(GTBCA)．针对企业现有相似产品进行通用性和标准化分析，并以此作为构筑公共平台的基础，辅助设计人员对平台进行合理规划和设计，从而有效地开发产品族，满足大批量定制生产的要求．最后，用一个抽油机的实例说明了文中方法的可操作性和有效性．     

Investigating the commonality attributes for scaling product families using comprehensive product platform planning (CP3)

A product family with a platform paradigm is expected to increase the flexibility of the manufacturing process to market changes, and to take away market share from competitors that develop one product at a time. The recently developed Comprehensive Product Platform Planning (CP³) method (i) presents a generalized model, (ii) allows the formation of product sub-families, and (iii) provides simultaneous identification and quantification of platform/scaling design variables. The CP³ model is founded on a generalized commonality matrix that represents the platform planning process as a mixed-binary nonlinear programming (MBNLP) problem. This MBNLP problem is high-dimensional and multimodal, owing to the commonality constraint. In this paper, the complex MBNLP model is reduced to a tractable MINLP problem without resorting to limiting approximations; along the reduction process, redundancies in the original commonality matrix are also favorably addressed. To promote a better understanding of the importance of a reduced MINLP, this paper also provides a uniquely comprehensive formulation of the number of possible platform combinations (or commonality combinations). In addition, a new commonality index (CI) is developed to simultaneously account for the inter-product commonalities (based on design variable sharing) and the overlap between groups of products sharing different platform variables. To maximize the performance of the product family and the commonality index yielded by the new CP³ model, we apply an advanced mixed-discrete Particle Swarm Optimization algorithm. The potential of the new CP³ framework is illustrated through its application to design scalable families of electric motors. Maximizing the new CI produced families with more commonality among similar sets of motor variants (compared to maximizing the conventional CI), which can be a beneficial platform attribute for a wide range of product families.     

可调节产品族的自底向上优化再设计方法

在建立可调节产品族优化模型的基础上,基于独立优化、平台规划、实例产品设计和方案评价的产品族自底向上设计流程,提出基于敏感度分析和模糊聚类的产品族优化再设计方法.在产品平台规划阶段,首先使用变量敏感度分析初步划分可能的平台常量和可调节变量集合;然后对实例产品间由于变量通用所造成的性能、成本和约束满足变化进行模糊C均值聚类,并引入模糊覆盖指数决定最优的聚类数目,合理规划多平台常量的共享.通过建立平台非通用性指数以定量评价产品族方案的通用程度.最后采用通用电机产品族的再设计实例,证明了该方法的正确性与高效性.     

A sensitivity-based commonality strategy for family products of mild variation, with application to automotive body structures

Identification of the product platform is a key step in designing a family of products. This article presents a methodology for selecting the product platform by using information obtained from the individual optimization of the product variants. Under the assumption that the product variety requires only mild design changes, a performance deviation vector is derived by taking into consideration individual optimal designs and sensitivities of functional requirements. Commonality decisions are based on values of the performance deviation vector, and the product family is designed optimally with respect to the chosen platform. The proposed methodology is applied to the design of a family of automotive body structures. Variants are defined by changing the functional requirements they need to satisfy and/or the geometry of the associated finite element models.     

Product platform configuration for product families: Module clustering based on product architecture and manufacturing process

Product family design utilizes platform-based modularity to enable product variety and efficient mass-production. While product platform issues have attracted much attention from both academia and industry, traditional product platform design for product families emphasized the platform-based modularity that focuses on product structure dimension (functional or non-functional) to realize cost reductions during the design stage. Both the design architecture and manufacturing process are objectives that define product family modularity (PFM). They should be closely coupled with each other for the planning and configuration of platforms. This paper focuses on the product platform configuration by recognizing and utilizing shared product modules for product families. Instead of clustering product modules only based on their design structure, this approach differentiates each product variant, and considers the inherent relationship between product architecture and processing activities. The advantage is that similar components can be grouped and produced on a shared platform, thus benefitting from lower cost and shorter production time. First, both the architecture and manufacturing information of the product variety are captured in matrix format. Then, hierarchical clustering is applied over the components to generate PFM. Finally, a set of platforms are constructed to efficiently process most components of variants.     

Integrated product line design and supplier selection: A multi-objective optimization paradigm

Product line design is commonly used to provide higher product variety for satisfying diversified customer needs. To reduce the cost and development time and improve quality of products, companies quite often consider sourcing. Conventionally, product line design and supplier selection are dealt with separately. Some previous studies have been attempted to consider product line design and supplier selection simultaneously but two shortcomings were noted. First, the previous studies considered several objectives as a single objective function in the formulation of optimization models for the integrated problem. Second, positions of product variants to be offered in a product line in competitive markets are not clearly defined that would affect the formulation of marketing strategies for the product line. In this paper, a methodology for integrated product line design and supplier selection is proposed to address the shortcomings in which a multi-objective optimization model is formulated to determine their specifications and select suppliers for maximizing the profit, quality and performance as well as minimizing the cost of the product line. In addition, joint-spacing mapping is introduced to help estimate market share of products and indicate positions of product variants. The proposed methodology can provide decision makers with a better tradeoff among various objectives of product line design, and define market positions of product variants explicitly. The results generated based on the methodology could help companies develop product lines with higher profits, better product quality and larger market share to be obtained. A case study of a product line design of notebook computers was performed to illustrate the effectiveness of the proposed methodology. The results have shown that Pareto optimal product line designs and the specifications of product variants can be determined. Suppliers of components and modules can be selected with considerations of minimum sourcing cost, and maximum performance and quality of product variants. Prices and positions of the product variants can also be determined.     

Scalable platforms using ant colony optimization

Mass customization necessitates increased product variety at the customers’ end but comparatively lesser part variety at the manufacturer’s end. Product platform concepts have been successful to achieve this goal at large. One of the popular methods for product platform formation is to scale one or more design variables called the scaling variables. Effective optimization methods are needed to identify proper values of the scaling variables. This paper presents a graph-based optimization method called the scalable platforms using ant colony optimization (SPACO) method for identifying appropriate values of the scaling variables. In the graph-based representation, each node signifies a sub-range of values for a design variable. This application includes the concept of multiplicity in node selection because there are multiple nodes corresponding to the discretized values of a given design variable. In the SPACO method, the overall decision is a result of the cumulative decisions, made by simple computing agents called the ants, over a number of iterations. The space search technique initially starts as a random search technique over the entire search space and progressively turns into an autocatalytic (positive feedback) probabilistic search technique as the solution matures. We use a family of universal electric motors, widely cited in the literature, to test the effectiveness of the proposed method. Our simulation results, when compared to the results reported in the literature, prove that SPACO method is a viable optimization method for determining the values of design variables for scalable platforms.