Product variety optimization under modular architecture

Fujita, Kikuo

doi:10.1016/s0010-4485(01)00149-x

Cited by 152 publications

(94 citation statements)

References 7 publications

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“…In many of these approaches, product platforms are known or specified a priori, i.e., before performing the optimization, whereas in other instances, platform-selection is determined during optimization (i.e., the platform is specified a posteriori.) In a similar manner, Fujita (2002) classified product family optimization problems into three classes (see Fig. 1): In Class I problems (boxes 1 and 2), product attributes are optimized under a fixed platform configuration (i.e., the platform is known a priori); Class II problems (boxes 3 and 4) find the optimal module selection from predefined sets of modules (i.e., the design of each module is known a priori); and finally, in Class III problems (boxes 5 and 6) the product attributes and platform configuration are optimized simultaneously.…”

Section: Classification: Product Family Optimizationmentioning

confidence: 88%

“…The hierarchical structure of product families can be exploited in this way. Fujita (2002) decomposed the Class III product family optimization problem into module combination and module attribute optimization sub-problems, and solved sub-problems in nested loops. Kokkolaras et al (2002), applied analytical target cascading (ATC) for decomposing a Class I problem by allocating each individual product design to a separate sub-problem and imposing commonality decisions by introducing subsystems with multiple parents.…”

Section: Decomposition Approachesmentioning

confidence: 99%

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Industrial application

2011

Ultracapacitor Applications

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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.

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Section: Classification: Product Family Optimizationmentioning

confidence: 88%

Section: Decomposition Approachesmentioning

confidence: 99%

Industrial application

2011

Ultracapacitor Applications

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“…When the common elements was fixed, through adding special elements and dynamically adjusting the values of the flexible element according to customer demand, firms can get a series of product variants and product family. During the process of flexible product platform design, according to Quality Function Deployment method, these elements can be modularized (Fujita, 2002). Then flexible product platform can be designed as a series of common modules and dynamically adjustable flexible modules.…”

Section: Modularity For Flexible Product Platformmentioning

confidence: 99%

The research for flexible product family manufacturing based on real options

Jin

Tian

2015

JIEM

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Abstract:Purpose: The goal of this paper is to find the best production strategy for product portfolio, which means the largest value of the options. And finally, give a case and find the solution of the optimal production strategy for product portfolio.Design/methodology/approach: This article, based on the production with characteristics of a call option and 0-1 integer programming model, build new-product portfolio strategy, and through case demonstrate that traditional method underestimates the value of the product portfolio.Finding: According to market being volatility and uncertainty and the production can being delayed, firms can flexibly arrange the best time for products to manufacture. Use real options theory to analyze product decision and the best production timing decision. Find the total options value is higher than the traditional methods. Research limitations/implications:We are not applied to real option pricing theory in modular flexible production system. We just applied real option pricing theory to the product platform. The basic model need to improve. While the thinking of this paper provides some research ideas for flexible production systems based on real option in further research.-72-Journal of Industrial Engineering and Management -http://dx

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“…In many of these approaches, the design variables that define the product platform within the family are known or specified a priori, i. 23,24 ), whereas in other instances, the platform variable selection is determined during optimization, i.e., the platform is specified a posteriori (Akundi et al 25 39 has divided product variety optimization problems into three classes: In Class-I problems, product attributes are optimized under a fixed platform configuration (i.e., the platform is known a priori); Class-II problems deal with finding the optimal module selection using predefined modules (i.e., the design of each module is known a priori); and finally, in Class-III problems, the product attributes and platform configuration are optimized simultaneously. We refer to this Class III, a posteriori problem as the joint product family optimization problem because it involves determining the optimal combination of 1) platform variable selection, 2) platform design and 3) variant design.…”

Section: Review Of Related Literaturementioning

confidence: 99%

A Decomposed Genetic Algorithm for Solving the Joint Product Family Optimization Problem

Khajavirad

Michalek

Simpson

2007

48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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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 allor-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 twodimensional commonality chromosome and decomposing the joint formulation into a twolevel 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.

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Product variety optimization under modular architecture

Cited by 152 publications

References 7 publications

Industrial application

Industrial application

The research for flexible product family manufacturing based on real options

A Decomposed Genetic Algorithm for Solving the Joint Product Family Optimization Problem

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