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Multi-Objective Design Optimization for Product Platform and Product Family Design Using Genetic Algorithms
Abstract
Many companies are using product families and platform-based product development to reduce costs and time-to-market while increasing product variety and customization. Multi-objective optimization is increasingly becoming a powerful tool to support product platform and product family design. In this paper, a genetic algorithm-based optimization method for product family design is suggested, and its application is demonstrated using a family of universal electric motors. Using an appropriate representation for the design variables and by adopting a suitable formulation for the genetic algorithm, a one-stage approach for product family design can be realized that requires no a priori platform decision-making, eliminating the need for higher-level problem-specific domain knowledge. Optimizing product platforms using multi-objective algorithms gives the designer a Pareto solution set, which can be used to make better decisions based on the trade-offs present across different objectives. Two Non-Dominated Sorting Genetic Algorithms, namely, NSGA-II and ε-NSGA-II, are described, and their performance is compared. Implementation challenges associated with the use of these algorithms are also discussed. Comparison of the results with existing benchmark designs suggests that the proposed multi-objective genetic algorithms perform better than conventional single-objective optimization techniques, while providing designers with more information to support decision making during product family design.
... In addition to improving economies of scale and scope, a product platform can facilitate customization by enabling a variety of products to be quickly and easily developed to satisfy the needs and requirements of distinct market niches 2 . I Most existing product family design approaches [3][4][5][6][7][8][9][10][11] are targeted at identifying the optimal commonality decision in order to minimize the manufacturing cost while meeting pre-specified performance goals. It should be noted, however, that while increasing commonality may reduce costs, it might also compromise the performance of some of the products in the family. ...
... The design of a family of universal motors is used to demonstrate the implementation of the proposed methodology. Motivated by Black & Decker's case study reported by Lehnerd 31 , an example problem involving the design of a family of universal electric motors was first created by Simpson 32 and subsequently used by a number of researchers in the community (e.g., see the work by Simpson et al. 3 , Messac et al. 4,5 , Nayak et al. 6 , Dai and Scott 9,10 ; Akundi et al. 11 ) A comparative study of product family design formulations that use the universal electric motor case study can be found in Simpson 33 . Existing formulations of the universal electric motor product family design problem are briefly discussed in the next section. ...
... Most of the aforementioned approaches require specifying the universal electric motor platform a priori. Some researchers do not impose this restriction and attempt to optimize the choice of platform variable(s) using a variety of formulations: the variation-based method 6 , penalty functions 5 , sensitivity analysis and cluster analysis 10 and a genetic algorithm-based approach 11 . However, these approaches only seek to minimize loss in motor performance (i.e., motor efficiency and mass) due to the commonality decisions without modeling manufacturing and market considerations explicitly. ...
In an effort to meet the diverse needs of today's highly competitive global marketplace better, many companies are utilizing product families and platform-based product development to increase variety, shorten lead-times, and reduce costs. Current research in the area of product family design mostly focuses on the cost benefits of the platform-based approach and does not sufficiently examine broader enterprise considerations (e.g., profit, market share). Few existing design methods integrate market considerations (e.g., customer preferences, competition) with product development efforts in their formulation. In this work, in addition to integrating market considerations with traditional product family concerns (e.g., modular design, decisions regarding shared parts and processes), the scope of the product family design problem is expanded to include the product line positioning problem, i.e., the problem of determining the appropriate market segment/niche for each product variant in the family. The novel Market-Driven Product Family Design (MPFD) methodology proposed here is introduced to systematically examine the impact of increasing the variety in product offerings across different market segments and explore the cost savings associated with commonality decisions. The demand modeling approach used in this paper models the dissimilar impacts of competition in different market segments and plays a significant role in determining the appropriate platform leveraging strategy. The design of a family of universal motors is used to demonstrate the proposed approach.
... The 55 system attributes can be considered to be candidate objectives; however, the product family design problem formulation includes too many objective functions, making it difficult to determine design solutions. In this work, we reduce the number of objective functions by using a goal programming approach with the aggregation of deviation functions, z, as follows (Akundi et al. 2005;Woodruff et al. 2013): ...
... In this figure, the values of NPV are plotted against z and PFPF values, and the NPV values are color-coded. z in (3) is employed to measure the aggregation of goal deviations for each attribute (i.e., smaller is better) (Akundi et al. 2005;Woodruff et al. 2013). The target values required to compute z, the goal deviation, are listed in Table 1. ...
Advanced product platform and product family design methods are needed to define and optimize the value they bring to a company. Maximizing platform commonality and individual product performance often fails to realize the most valuable product family during optimization; however, few examples exist in the literature to explore these trade-offs. This paper introduces a novel industry case study to explore the differences between “traditional” multidisciplinary design optimization (MDO) and value-driven design (VDD) approaches to product family design. The case study involves a family of five commercially-available washing machines and integrates multidisciplinary analyses, simulations, mathematical models, and response surface models to obtain ratings for individual product attributes. These attributes are then aggregated into a value function for the product family using a novel approach to estimate sales volume and a demand sensitivity curve derived from publicly available data. We then formulate and solve a “traditional” MDO product family design problem using a multi-objective genetic algorithm to minimize performance deviation and a product family penalty function. A novel VDD formulation is then introduced and solved to maximize the net present value (NPV) for the firm producing the family of products. Visualization and comparison of the results illustrate that the “traditional” MDO formulation can find several promising solutions for the product family, but it fails to find solutions that maximize the value to the firm. The results also provide a benchmark for researchers to explore alternative value function formulations and solution approaches for product family design using the novel industry case study.
... In addition to improving economies of scale and scope, a product platform can facilitate customization by enabling a variety of products to be quickly and easily developed to satisfy the needs and requirements of distinct market niches (Pine, 1993). Most existing product family design approaches (Simpson et al., 2001, Messac et al., 2002b, Messac et al., 2002a, Nayak et al., 2002, Fellini et al., 2002, Farrell and Simpson, 2003, Dai and Scott, 2006, Dai and Scott, 2004, Akundi et al., 2005) are targeted at identifying the optimal commonality decision in order to minimize the manufacturing cost while meeting pre-specified performance goals. It should be noted, however, that while increasing commonality may reduce costs, it might also compromise the performance of some of the products in the family. ...
... Most of the aforementioned approaches require specifying the universal electric motor platform a priori. Some researchers do not impose this restriction and attempt to optimize the choice of platform variable(s) using a variety of formulations: the variation-based method (Nayak et al., 2002), penalty functions (Messac et al., 2002b), sensitivity analysis and cluster analysis (Dai and Scott, 2004) and a genetic algorithm-based approach (Akundi et al., 2005). However, these approaches only seek to minimize loss in motor performance (i.e., motor efficiency and mass) due to the commonality decisions without modeling manufacturing and market considerations explicitly. ...
In an effort to meet the diverse needs of today's highly competitive global marketplace better, many companies are utilizing product families and platform-based product development to increase variety, shorten lead-times, and reduce costs. Current research in the area of product family design mostly focuses on the cost-savings benefits of the platform-based approach and does not sufficiently examine broader enterprise considerations such as profit and market share. Furthermore, very few existing design methods integrate market considerations (e.g., customer preferences, competition) with product development efforts in their formulation. In this work, in addition to integrating market considerations with traditional product family concerns (e.g., modular design, decisions regarding shared parts and processes), the scope of the product family design problem is expanded to include the product line positioning problem, i.e., the problem of determining the appropriate market niche for each product variant in the family. The novel market-driven product family design (MPFD) methodology proposed here is introduced to systematically examine the impact of increasing the variety in the product offerings across different market segments and explore the cost-savings associated with commonality decisions. A unique representation scheme is also introduced to enable us to integrate the qualitative market segmentation grid with mathematically rigorous demand models, and the demand modelling approach employed in this paper models the dissimilar impacts of competition in different market segments and plays a significant role in determining the appropriate platform leveraging strategy. The design of a family of universal motors is used to demonstrate the proposed approach.
... Several case studies focused on the following: the use of optimization techniques for designing mechanical parts or components including hooks, vessels, and gear boxes using metaheuristics optimization techniques (Alkan and Chinnathai 2021); the determination of minimum drawing force and maximum thickness in tube drawing processes using the Artificial Bee Colony algorithm (Salehi et al. 2016); the optimization of design parameters of motors using the Genetic Algorithm (Akundi et al. 2005); the design evaluation of piezoelectric transducer in an ultrasonic knife using Ant Colony Optimization. These studies successfully employed optimization techniques aiming to obtain the optimized design parameters based on a set of constraints without considering the environmental issues. ...
Purpose
In response to the public concerns on the performance regarding environmental conservation and energy saving, manufacturers have to perform environmental impact assessments for their products. Some eco-design tools have been introduced to support the development of greener products. However, most of them require decision-makers’ qualitative judgment during the evaluation processes. In this connection, this research aims to propose a practical approach that uses Cuckoo search and life cycle assessment (LCA) to support design decision-making from the environmental perspective in the initial design stage.
Methods
Aiming to develop a handy approach for evaluating the potential environmental impact during the initial design stage, the proposed approach combines Cuckoo search and LCA to determine the optimal design parameters by simultaneously considering multiple design constraints. A case application based on an electric kettle design is presented to demonstrate the applicability of the proposed approach.
Results and discussion
This approach provides a fast-track way to determine the optimal parameters for several key design decisions. The result is presented in a simple form with the corresponding environmental impact value, and thus, further interpretation of the results is not required.
Conclusions/implications
This approach offers an immediately applicable tool for the decision-makers to determine the key design parameters from the environmental perspective. A pilot case implementation on a simple electric kettle design is presented to showcase the applicability of the combined Cuckoo search and LCA approach. The proposed approach may provide a systematic way of evaluating multiple design combinations. The result obtained from the approach is reliable and able to support the decision-makers in selecting a suitable set of design parameters in view of lowering the potential environmental impact of the products.
... In this paper, for each design x, the penalty with respect to the constraints in (14) For the UEM design problem, if the preference between Mass(x) and −η(x) is known, a weighted sum of these two objective functions can be defined as the following expression: (15) where Mass normalized represents the normalized weight derived by dividing the original weight by the maximum allowable weight : Mass max = 2 [kg]). w 1 and w 2 are weight coefficients that are assumed to be equal (i.e., w 1 = w 2 = 0.5) in [49]. Through the weighted sum, the UEM design problem is transformed into a single-objective optimization problem. ...
As a well-known engineering practice, concurrent engineering (CE) considers all elements involved in a product’s life cycle from the early stages of product development, and emphasises executing all design tasks simultaneously. As a result, there exist various complex design problems in CE, which usually have many design parameters or require different disciplinary knowledge to solve them. To address these problems and enable concurrent design, different methods have been developed. The original problem is usually divided into small subproblems so that each subproblem can be solved individually and simultaneously. However, good decomposition, optimisation and communication strategies among subproblems are still needed in the field of CE. This paper attempts to study and analyse cooperative coevolution (CC) based design optimisation in concurrent engineering by employing a parallel CC framework. Furthermore, it aims to develop new concurrent design methods based on parallel CC to solve different kinds of CE problems. To achieve this goal, a new novelty-driven CC is developed for design problems with complex structures and a novel concurrent design method is presented for quasi-separable multidisciplinary design optimisation problems. The efficacy of the new methods is studied on universal electric motor design problems and a general multidisciplinary design optimisation problem, and compared to that of some existing methods. Additionally, this paper studies how the communication frequency among subpopulations affects the performance of the proposed methods. The optimal communication frequencies under different communication costs are reported as experimental results for both proposed methods on the test problems. Based on this study, an effective self-adaptive method is proposed to be used in both optimisation schemes, which is able to adapt the communication frequency during the optimisation process.
... The idea is to introduce an additional objective function with the goal of maximizing the common parts within the product family platform. In 2005, Akundi et al. used a genetic algorithm (GA) optimization method to optimize a product family of scale-based universal motors (Akundi, Simpson, and Reed 2005). On the other hand, several articles discuss the suitability and effectiveness of each optimization method for the platform-based design approach (Simpson and D'Souza 2004) (Cetin and Saitou 2004). ...
Unlike conventional approaches where optimization is performed on a unique component of a specific product, optimum design of a set of components for employing in a product family can cause significant reduction in costs. Increasing commonality and performance of the product platform simultaneously is a multi-objective optimization problem (MOP). Several optimization methods are reported to solve these MOPs. However, what is less discussed is how to find the trade-off points among the obtained non-dominated optimum points. This article investigates the optimal design of a product family using non-dominated sorting genetic algorithm II (NSGA-II) and proposes the employment of technique for order of preference by similarity to ideal solution (TOPSIS) method to find the trade-off points among the obtained non-dominated results while compromising all objective functions together. A case study for a family of suspension systems is presented, considering performance and commonality. The results indicate the effectiveness of the proposed method to obtain the trade-off points with the best possible performance while maximizing the common parts.
... Product family scaling design first needs to select platform variables, viz., to determine which design parameters that take common values (Simpson, 2004). While many existing methods assume that the platform architecture is known a priori (Fujita, Sakaguchi, & Akagi, 1999), some approaches attempt to determine platform variables along with scalable variables during optimization (Akundi, Simpson, & Reed, 2005). The subsequent task is to determine the optimal values of common and distinctive variables with the objective of satisfying performance and economic requirements. ...
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. (c) 2013 Elsevier B.V. All rights reserved.
... Thus here the averaged values of front and rear track are used to indicate the wheel track. 3 NR means "Not Recommended" 4 NA means "Not Available" ...
... Product family scaling design first needs to select platform variables, i.e., to determine which DPs take common values (Simpson 2004 ). While many existing methods assume that the platform architecture is known a priori (Fujita et al. 1999), some approaches attempt to determine platform variables along with scalable variables during optimization (Akundi et al. 2005 ). The subsequent task is to determine the optimal values of common and distinctive variables with the objective of satisfying performance and economic requirements (Knight and Kim 1991). ...
Leveraging product differentiation and mass production efficiency in mass customization basically entails a configure-to-order paradigm. In the engineer-to-order (ETO) business, however, companies build unique products in response to ‘foreseeable’ customer specifications. The key challenge of ETO mass customization lies in the complexity of accommodating future design changes when customers are involved in customizing design specifications. This paper proposes a two-stage, bi-level stochastic programming framework to tackle ETO mass customization. At the first stage, product platform configuration is integrated with production reconfiguration, which is formulated as a shortest path problem with resource constraints (SPPRC) to optimize production delays within the capabilities of the process platform. Benders’ decomposition algorithm is applied to solve this optimal configuration problem owing to its high computational efficiency. The second stage scrutinizes the optimal configuration resulting from the first stage for scaling optimization of design parameters (DPs) for each module. All DPs are differentiated by standard or customizable DPs. A bi-level stochastic program is implemented to leverage conflicting goals between the producer (leader) and consumer (follower) surpluses. As a result, ETO customization design is anchored with optimal values of standard DPs and optimal value ranges of customizable DPs. A case study of ship engine and power generator ETO design is presented, demonstrating the feasibility and potential of the ETO mass customization framework.
... The first one is platform selection-to determine which design parameters that take common values. While many existing methods assume that the platform architecture is known a priori (Fujita et al., 1999), some approaches determine platform variables along with scalable variables during optimization (Akundi, Simpson, & Reed, 2005;Dai & Scott, 2004). The subsequent task is to determine the optimal values of common and distinctive variables by satisfying performance and economic requirements. ...
Product family design and platform-based product development has received much attention over the last decade. This paper
provides a comprehensive review of the state-of-the-art research in this field. A decision framework is introduced to reveal
a holistic view of product family design and platform-based product development, encompassing both front-end and back-end
issues. The review is organized according to various topics in relation to product families, including fundamental issues
and definitions, product portfolio and product family positioning, platform-based product family design, manufacturing and
production, as well as supply chain management. Major challenges and future research directions are also discussed.
Bài báo trình bày nghiên cứu, tính toán, thiết kế và tối ưu hóa hiệu suất động cơ servo ứng dụng trong băng chuyền. Trước tiên, từ yêu cầu di chuyển của băng chuyền, các yêu cầu đặc tính của động cơ nghiên cứu được xác định sau các bước tính toán chi tiết. Sau đó, nghiên cứu sử dụng các thuật toán tối ưu để tìm giá trị điện áp và hệ số trượt nhằm tối ưu hóa hiệu suất tại các điểm làm việc của động cơ. Bài báo đã xem xét và thiết kế ba động cơ với cấu hình và mật độ dòng điện khác nhau, qua đó có thể đánh giá, so sánh về khối lượng, hiệu suất và độ tăng nhiệt. Kết quả thiết kế tối ưu động cơ servo có ý nghĩa quan trọng trong việc lựa chọn động cơ phù hợp nhất về chi phí, hiệu suất và thời gian đáp ứng cho một ứng dụng yêu cầu cụ thể.
This chapter proves the mediating effect of product platform strategies on the relationship between a firm's subject, environment, and resources and the performance of new product development in the perspective of platform leadership. The author analyzed the mediating role of product platform strategy by considering CEO propensity, competition and customers, and competitiveness of retained resources. Compared to the past, in the perspective of platform leadership, the product platform strategy has a critical effect on the relationship between the business scope of a platform leader, the external relationship with complementors, and the internal organization of a platform leader impact on the performance of new product development. As a result of hierarchical regression analysis with the data of Korean high technology companies, the product platform strategy would be mediating the relationship between the antecedents such as CEO propensity, competition and customers, and competitiveness of retained resources and NPD performance.
The ongoing need for better fuel economy and lower exhaust pollution of vehicles has increased the employment of electric power steering (EPS) in automotives. Optimal design of EPS for a product family reduces the development and fabrication costs significantly. In this paper, the TOPSIS method along with the NSGA-II is employed to find an optimum family of EPS for an automotive platform. A multi-objective optimization problem is defined considering road feel, steering portability, RMS of Ackerman error, and product family penalty function (PFPF) as the conflicting objective functions. The results for the single objective optimization problems and the ones for the multi-objective optimization problem, as well as two suggested trade-off design points are presented, compared and discussed. For the two suggested points, performance at one objective function is deteriorated by about 1%, while the commonality is increased by 20%–40%, which shows the effectiveness of the proposed method in first finding the non-dominated design points and then selecting the trade-off among the obtained points. The results indicate that the obtained trade-off points have superior performance within the product family with maximum number of common parts.
Functional commonalities across product families have been considered by a large body of product family design community but this concept is not widely used in design. For a designer, a functional family refers to a set of designs evaluated based on the same set of qualities; the embodiments and the design spaces may differ, but the semantics of what is being measured (e.g., strength of a spring) remain the same. Based on this functional behaviour we introduce a product family hierarchy, where the designs can be classified into phenomenological design family, functional part family and embodiment part family. And then, we consider the set of possible performances of interest to the user at the embodiment level, and use multi-objective optimisation to identify the non-dominated solutions or the Pareto-front. The designs lying along this front are mapped to the design space, which is usually far higher in dimensionality, and then clustered in an unsupervised manner to obtain candidate product groupings which the designer may inspect to arrive at portfolio decisions. We highlight and discuss two recently suggested techniques for this purpose. First, with help of dimensionality reduction techniques, we show how these clusters in low-dimensional manifolds embedded in the high-dimensional design space. We demonstrate this process on three different designs (water faucets, compression springs and electric motors), involving both continuous and discrete design variables. Second, with the help of a data analysis of Pareto-optimal solutions, we decipher common design principles that constitute the product portfolio solutions. We demonstrate this so-called ‘innovization’ principles on a spring design problem. The use of multi-objective optimisation (evolutionary and otherwise) is the key feature of both approaches. The approaches are promising and further research should pave their ways to better design and manufacturing activities.
For a successful business model the efficient development and design of a comprehensive product family plays a crucial part in many real world applications. A product family as it occurs, e.g., in the automotive domain consists of a product platform which covers the commonalities of product variants and the derived product variants. While product variants need to be fast and flexibly adjusted to market needs, from manufacturing and development point of view an underlying product platform with a large number of common parts is required to increase cost efficiency. For the design and evaluation of optimization methods for product family development, in the present paper the universal electric motor (UEM) family problem is considered, as it provides a fair trade-off between complexity and computational costs compared to real world application scenarios in the automotive domain. Since especially solving this problem without usage of pre-knowledge comes with high computational costs, a cascaded evolutionary multi-objective optimization based on NSGA-II with concatenation of product Pareto fronts is proposed in the present paper to efficiently reduce computational time. Besides providing sets of Pareto solutions to the unbiased UEM family problem the effects of considering solutions of prior platform optimizations as starting point for follow-up optimizations under changing requirements are evaluated.
Product families have become an effective way for companies to provide the variety of products that customers desire while keeping manufacturing costs relatively low. One of the most difficult tasks in designing a product family is the decision of which components to make common and which to make variant or unique. There have been several approaches to this problem, ranging from the use of expert knowledge to the use of optimization algorithms as the basis for commonality decision-making. This paper introduces an approach utilizing multidimensional data visualization in conjunction with optimization to create a new hybrid approach for making commonality decisions. The proposed approach involves five steps: (1) problem formulation, (2) optimization of all common and all unique solutions, (3) visualization of unique optimization designs with parallel coordinates, (4) optimization based on data visualization results, and (5) plotting of design envelope to visualize final results. Multidimensional data visualization is used with "on-the-fly" visual design steering to enable designers to see and interact directly with the product family optimization process. The approach also allows designers to identify subsets of commonality through the use of parallel coordinates, rather than limit decisions to make design variables either common across all products or unique to each. The final step in the approach provides designers with a plot of several platform options where trade-offs between commonality and performance can be easily visualized. A family of three General Aviation Aircraft (GAA) is used to demonstrate this approach. Although the family is limited to three products, the approach should scale to larger product families given the multidimensional data visualization tools employed.
Product platforms and platform-based product development provide a cost-effective means for generating sufficient product variety for the market to suit the varied demands of today’s customers. A genetic algorithm (GA)-based approach for product family design is adopted in this research for optimizing a product family to help resolve the inherent tradeoff between platform commonality and individual product performance within the family. One of the challenges encountered in our work is the enormous amount of information that is generated from the GA-based approach. In particular, the GA-based approach allows us to explore multiple families based on multiple platforms, and effective visualization strategies are needed to help identify the best solution. In such situations, multi-dimensional visualization becomes an effec tive method to synthesize large numbers of complex designs in a short time without losing any essential information. In this paper we explore visualization strategies are presented to support the product family design optimization process and their applications are demonstrated with a General Aviation Aircraft example.
With a highly fragmented market and increased competition, platform-based product family design is recognised as an effective method for constructing a product line that satisfies diverse customer demand while keeping design and production cost- and time-effective. Recognising the need for modularity and commonality in platform development, this paper presents a systematic framework to assist in implementing top-down platform and product family design, which aims to achieve system-level modularity for variety generation, and rationalise the commonality configuration for module instantiation. In the first phase of platform development, a robust and flexible product family architecture is constructed to accommodate variations by analysing the external varieties of the generic product architecture, and provide a modularity design space, wherein the design tasks are further decomposed into module instantiation. The second phase of detailed platform development aims to enhance commonality in terms of engineering efficiency by coordinating with the back-end product realisation stage. A tractable optimisation method is used to capture and resolve the trade-off between commonality configuration and individual product performance. A family of power tool designs is used to demonstrate the potential and feasibility of the proposed framework at the system level and detailed design stages.
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 restric-tions 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 con-sisting of many variants.
A network representation model of the system diagram and a computer algorithm and division system to address the modular division problem of an engine room are proposed. The design result of the modularization shows modular division plans that have high functional independence and are easy to combine with each other.
A critical step when designing a successful product family is to determine a cost-saving platform configuration along with an optimally distinct set of product variants that target different market segments. 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. However, the high dimensionality of an "all-in-one" algorithm for optimizing the joint problem of 1) platform variable selection, 2) platform design and 3) variant design makes most of these approaches impractical when a large number of products is considered. Many existing approaches have restricted the scope of the problem by fixing platform configuration a priori, limiting platform configuration to an all-or-none component sharing strategy, or by solving subsets of the joint problem in stages, sacrificing optimality. In this study, we propose a single-stage optimization approach for solving the joint product family problem with generalized commonality using an efficient decomposition solution strategy involving multi-objective genetic algorithms (MOGAs). The proposed approach overcomes prior limitations by introducing a generalized two-dimensional commonality chromosome and decomposing the joint formulation into a two-level GA, where the upper-level determines the optimal platform configuration while each lower-level designs one of the individual variants in the family. Moreover, all sub-problems run in parallel, and the upper-level GA coordinates consistency among the lower-levels using the MPI (Message Passing Interface) library. The proposed approach is demonstrated by optimizing a family of three general aviation aircraft, and results outperform those from a non-decomposed GA. Results also show that the commonality-performance Pareto front contains solutions with generalized commonality, suggesting the need to avoid all-or-none component sharing restrictions in order to avoid sub-optimality. Future work in scaling the decomposed GA to larger product families is also discussed.
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.
KeywordsProduct family–Design for manufacturing–Multi-platforming–Commonality index–Genetic algorithm
This paper discusses the problem of structural optimization of product families subjected to multiple load cases, evaluated
by computationally costly finite element analysis. Product families generally have a complex composition of shared components
that makes individual product optimization difficult as the relation between the shared variables is not always intuitive.
More optimal is to treat the problem as a product family optimization problem. Though, for product families subjected to multiple
and computationally costly crash loads, the optimization problem takes too long time to solve with traditional methods. Therefore,
a new optimization algorithm is presented that decomposes the family problem into sub-problems and iteratively reduces the
number of sub-problems, decouple and solve them. The algorithm is applicable for module based product families with predefined
composition of generalized commonality, subjected to multiple load cases that can be analyzed separately. The problem reduction
is performed by only considering the constraints that are critical in the optimal solution. Therefore the optimization algorithm
is called the Critical Constraint Method, CCM. Finally the CCM algorithm is evaluated by two product family optimization problems.
KeywordsProduct family optimization-Multiple crash loads-Nonlinear systems-Response surface methodology-Meta model approximations-Critical constraints
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.
Product family design has been recognized as an effective method to satisfy diverse customer's demands cost-effectively. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss compared to individual design. In this paper, a modified genetic algorithm using dynamic weighted aggregation is proposed to optimize a scale-based product family design while making the two-objective (performance-and-commonality) optimization tractable and efficient. The proposed method not only overcomes the drawbacks of conventionally fixed weight aggregation for product family design, but also maintains the computation expense at the economical level. An example of designing a family of planetary gear trains is presented to demonstrate the proposed method.
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