Product Family Design Problem Research Articles

Value-driven design for product families: a new approach for estimating value and a novel industry case study

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.

Decentralized determination of design variables among cooperative designers for product platform design in a product family

Establishing a product family enables companies leverage mass customization to meet demand from different market segments. Product family design environment can be inherently decentralized and requires a number of entities to achieve a common goal. Issues related to product platform design can be modeled as multi-objective optimization with respect to design variables, production cost and other factors. A common drawback in decentralized environments is information sharing which can reduce the quality of the decision especially under partial information or information privacy restrictions. In this paper, we present an agent-based collaborative design model and propose a decentralized coordination mechanism to facilitate decision-making. This is achieved by modeling a collaborative design environment and applying a two-phase augmented Lagrangian-based algorithm such that the decision makers achieve a common goal without having a global view of the entire design space. We demonstrate the efficiency of the proposed mechanism using a General Aviation Aircraft and a Universal Electric Motor product family design problem. The product designers and the platform designer act independently and each has no knowledge of the decision functions of other designers. The computational results showed that quality solutions can be obtained in decentralized environments.

Scale-Based product family optimization design based on the improved AMPSO in selecting optimum strategy

Abstract The main contribution of this paper is to propose a two-stage scale-based product family design method (TPFDM) based on an improved adaptive mutation particle swarm optimization (AMPSO) algorithm. To do that, the problem involved in scale-based product family design is analyzed and modelled. A two-stage optimized design process is built to solve the problem. To build and optimize the product family platform, a multi-objectives model about the product family design problem is established. The multi-objectives problem and each parameter's sensitivity as well as variation index about objectives are solved via an intelligent optimization algorithm. The platform constants and non-platform variables are discriminated by sensitivity and variation index in the first stage. The main work of the second stage is to optimize products based on product platform via the intelligent optimization algorithm. Moreover, in order to overcome the early maturity phenomenon of the particle swarm optimization algorithm, an improved AMPSO algorithm is proposed to introduce the concept of the non-individual optimum strategy and the dominate strategy. Finally, aerial magnetorheological fluid damper family design is used as an example to demonstrate effectiveness and feasibility of the proposed method.

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.

Bi-objective optimization for low-carbon product family design

Consumers, industry, and government entities are becoming increasingly concerned about the issue of global warming. With this in mind, manufacturers have begun to develop products with consideration of low-carbon. In recent years, many companies are utilizing product families to satisfy various customer needs with lower costs. However, little research has been conducted on the development of a product family that considers environmental factors. In this paper, a low-carbon product family design that integrates environmental concerns is proposed. To this end, a new method of platform planning is investigated with considerations of cost and greenhouse gas (GHG) emission of a product family simultaneously. In this research, a low-carbon product family design problem is described at first, and then a GHG emission model of product family is established. Furthermore, to support low-carbon product family design, an optimization method is applied to make a significant trade-off between cost and GHG emission to implement a feasible platform planning. Finally, the effectiveness of the proposed method is illustrated through a case study. We present a new planning method of product platform, called low-carbon product family design.We establish a bi-objective optimization model that minimizes costs and greenhouse gas emission of a product family.An optimization method is proposed to solve the bi-objective optimization problem.Our case study shows that there is a tradeoff between cost and GHG emission in product platform planning.

An Approach to Identify Six Sigma Robust Solutions of Multi/Many-Objective Engineering Design Optimization Problems

In order to be practical, solutions of engineering design optimization problems must be robust, i.e., competent and reliable in the face of uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this study focuses on problems where uncertainties emanate from the design variables. While approaches to identify robust optimal solutions of single and multi-objective optimization problems have been proposed in the past, we introduce a practical approach that is capable of solving robust optimization problems involving many objectives building on authors’ previous work. Two formulations of robustness have been considered in this paper, (a) feasibility robustness (FR), i.e., robustness against design failure and (b) feasibility and performance robustness (FPR), i.e., robustness against design failure and variation in performance. In order to solve such formulations, a decomposition based evolutionary algorithm (DBEA) relying on a generational model is proposed in this study. The algorithm is capable of identifying a set of uniformly distributed nondominated solutions with different sigma levels (feasibility and performance) simultaneously in a single run. Computational benefits offered by using polynomial chaos (PC) in conjunction with Latin hypercube sampling (LHS) for estimating expected mean and variance of the objective/constraint functions has also been studied in this paper. Last, the idea of redesign for robustness has been explored, wherein selective component(s) of an existing design are altered to improve its robustness. The performance of the strategies have been illustrated using two practical design optimization problems, namely, vehicle crash-worthiness optimization problem (VCOP) and a general aviation aircraft (GAA) product family design problem.

Linear programming embedded genetic algorithm for product family design optimization with maximizing imprecise part-worth utility function

Product family design optimization is an important decision task in the early stages of product development. The extant optimization models for product family design assume that the known information for modeling can be determined precisely. However, many collected information for product design are prone to be imprecise due to the inherent uncertainty of human knowledge and expression. For example, when human experts estimate market demand of a product, imprecise information may be involved and influence the results of optimization. In this research, an optimization model with maximizing imprecise part-worth utility function is established for product family design problem. A linear programming embedded genetic algorithm is proposed to solve the proposed fuzzy optimization model. An industrial case of printing calculator product is used to illustrate the proposed approach. Experiments and sensitivity analysis based on the case study are also performed to analyze the relationship among the parameters and to explore the characteristics of the optimization model.

Many objective visual analytics: rethinking the design of complex engineered systems

Many cognitive and computational challenges accompany the design of complex engineered systems. This study proposes the many-objective visual analytics (MOVA) framework as a new approach to the design of complex engineered systems. MOVA emphasizes learning through problem reformulation, enabled by visual analytics and many-objective search. This study demonstrates insights gained by evolving the formulation of a General Aviation Aircraft (GAA) product family design problem. This problem's considerable complexity and difficulty, along with a history encompassing several formulations, make it well-suited to demonstrate the MOVA framework. The MOVA framework results compare a single objective, a two objective, and a ten objective formulation for optimizing the GAA product family. Highly interactive visual analytics are exploited to demonstrate how decision biases can arise for lower dimensional, highly aggregated problem formulations.

The multi-single-objective problem and its solution by way of evolutionary algorithms

In this paper, a new type of engineering problems is introduced and formulated. It concerns a requirement to search for solutions for various design problems defined as single-objective problems and involving component sharing. Sharing components among products is an effective way to cut costs. Expenses are decreased through the reduction in components design time as well as through savings in manufacturing costs and inventory. The difference between the current problem and the product family design problem, lies in the fact that here, the products’ problems, might be involved with different objective spaces. The problem also differs from problems that may be decomposed to sub-problems, because here the problem is inherently decomposed and its composition is artificial. In the study, several computational-based approaches to search for the common components are discussed, tested, and compared. These include a sequential approach, posing the search as a multi-objective problem, and posing it as a unified single-objective problem. Both academic and engineering problems are suggested to explain the methodology and to demonstrate its applicability to engineering design. Furthermore, the paper highlights the differences between the newly suggested problem and previously introduced design problems including; family of designs and the multi-multi-objective optimization problem. In this paper, evolutionary algorithms are utilized for implementing the considered approaches.

A market-driven approach to product family design

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.

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 generic genetic algorithm for product family design

Product family design (PFD) has been well recognized as an effective means to satisfy diverse market niches while maintaining the economies of scale and scope. PFD essentially entails a configuration problem by “combination," where combinatorial explosion always occurs and is known to be mathematically intractable or NP-hard. Although genetic algorithms (GAs) have been proven to excel in solving combinatorial optimization problems, it is difficult to adopt the traditional GA to deal with the complex data and interrelationships inherent in the PFD problem. This paper proposes a generic genetic algorithm (GGA) for PFD. A generic encoding scheme is developed to adapt to diverse PFD scenarios. A hybrid constraint-handling strategy is proposed to handle complex and distinguishing constraints at different stages along the evolutionary process. The design and implementation procedures of the GGA are discussed in detail. An application of the proposed GGA to motor family design is reported. The GGA efficiency is also tested through efficiency analysis in terms of the probability of generating feasible solutions, as well as through analysis of the GGA complexity.

Modular product family design: Agent-based Pareto-optimization and quality loss function-based post-optimal analysis

The advent of mass customization and increased manufacturing competition has necessitated that many companies offer platform-oriented multiple product variants. Various design strategies such as Design for Variety and product family design have become critical in this respect. This paper provides a two-step approach to tackle the modular product family design problem. The first step performs a multi-objective optimization using a multi-agent framework to determine the Pareto-design solutions for a given module set. The proposed multi-agent framework is new and has built in flexibility to handle various constraints such as module compatibility during the optimization process. The second step performs post-optimization analysis that includes a novel application of the quality loss function to determine the optimal platform level for a related set of product families and their variants. The proposed method is applied to a product family design example to demonstrate its validity and effectiveness.

A Model-Based Approach for Planning and Developing a Family of Technology-Based Products

In this paper, we address the product-family design problem of a firm in a market in which customers choose products based on some measure of product performance. By developing products as a family, the firm can reduce the cost of developing individual product variants due to the reuse of a common product platform. Such a platform, designed in an aggregate-planning phase that precedes the development of individual product variants, is itself expensive to develop. Hence, its costs must be weighted against the benefits of its reuse in a family. We offer a model for capturing costs of product development when the family consists of variants based on a common platform. It is shown that the model can be converted into a network-optimization problem, and the optimal product-family can be identified under fairly general conditions by determining the shortest path of its network formulation. We also analytically examine the effect of alternative product designs on product-family composition, and discuss the implications of investing in new-product technology. Finally, we illustrate our model and managerial insights with an application from the electronics industry.