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Research in Engineering Design

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01-10-2015 | Original Paper

A simulation-based method to evaluate the impact of product architecture on product evolvability

Author: Jianxi Luo

Published in: Research in Engineering Design | Issue 4/2015

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Abstract

Products evolve over time via the continual redesigns of interdependent components. Product architecture, which is embodied in the structure of interactions among components, influences the ability for the product to be subsequently evolved. Despite extensive studies of change propagation via inter-component connections, little is known about the specific influences of product architecture on product evolvability. Related metrics and methods to assess the evolvability of products with given architectures are also under-developed. This paper proposes a simulation-based method to assess the isolated effect of product architecture on product evolvability by analyzing a design structure matrix. We define product evolvability as the ability of the product’s design to subsequently generate heritable performance-improving variations, and propose a quantitative measure for it. We demonstrate the proposed method by using it to investigate a wide spectrum of model-generated DSMs representing products with varied architectures, and show that modularity and inter-component influence cycles promote product evolvability. Our primary contribution is a repeatable method to assess and compare alternative product architectures for architecture selection or redesign for evolvability. A second contribution is the simulation-based evidence about the impacts of two particular product architectural patterns on product evolvability. Both contributions aim to aid in designing for evolvability.

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Biological and technological evolution processes are not exactly the same. One major difference is that technologies are indeed consciously “designed” by “intelligent designers,” whereas biology evolution relies on natural selection. The technology evolution process may experience more occasions of non-sequential inheritance and leaps then biological evolution because of the role and decisions of designers on technologies, even though both processes are incremental and generally slow. Readers interested in contrasting the biological and technology evolution processes may refer to Kelly (2010) and Beesemyer et al. (2011). In the present paper, we do not study processes, dynamics and influences from designer’s choices, but the evolvability of a product at a time, given by its architecture at the time. Our analogy focuses on (1) variation of elements (genes vs. components), (2) how inter-element interactions constrain variations (to make use of the NK model) and (3) preferential selection of fitness-improving variations (to define our evolvability metric). Section 2 provides more detailed review of related concepts of biological and product evolutions.

For example, if the design choice of component A influences the working of B, which influences C, which influences A, components A, B and C form a cycle.

The number of design choices for individual components affects the size of the design space, but does not affect the qualitative results on the isolated influences of different product architectures on evolvability. The pioneers and leading scholars of NK model had written about the consequences of using ω _i = 2 for all i. Kauffman and Weinberger (1989), who first published the NK model, wrote that, “although it is difficult to draw a picture of such high dimensional spaces, a sense of their structure can be captured by considering proteins with only two amino acids, e.g., alanine and glycine.” Levinthal (1997), who introduced NK model to the field of organization sciences, wrote that, “the model can be extended to an arbitrary finite number of possible values of an attribute, but the qualitative properties of the model are robust to such a generalization.” In this paper, the focus is to assess the isolated impact of product architecture rather than the size of design space, setting ω _i = 2 provides the simplest and most tractable model for this purpose.

That means the component design choices in consideration are those that do not alter the pattern of interactions among the components. In real-world design practices, potential design choices of a component may require new interactions or eliminate existing interactions with other components. Such design choices are not included in the design space resulting from a given architecture that we focus on to assess. That is, the design space given by architecture only constitutes of those design choices of individual components complying with the architecture.

Our method follows the NK model specifically to use the random fitness function to simulate the fitness landscape. In theory, the fitness function can have other forms. If the engineer has a deterministic fitness function, he can obtain a fixed landscape given specific product architecture. The fixed landscape, rather than a sample of random landscapes, will be assessed using the evolvability metric in Eq. (2). In addition, if the engineer has total knowledge of the fixed fitness landscape, he/she can choose the global optimal design directly. However, this is normally not the case of real engineering practices. Often engineers are unable to have a deterministic fitness function. In such most cases, random fitness functions can be used to assess the influence of product architecture on evolvability.

K _i of each component is still preserved, whereas the component’s influence links are now totally nonspecific to any predefined niche. In addition, the resulted networks with D = 1 are not purely random. The components that are primarily in more upstream of a product hierarchy have a broader scope of influence than components that are more downstream. By a simple robustness checking simulation exercise, we found that if the networks are rewired without preserving each component’s preassigned number of outgoing influences (K _i) when rewiring, the main conclusions of the paper still hold.

The model is repeatedly run to simulate many network samples. For each given combination of inputs (N, K, G), we simulate 2,000 networks and calculate the average cyclic degree of each sample of 2,000 networks. To improve the fitness of the randomly generated networks, only the ones with the given N components fully connected and K within 3% of the target value were accepted as valid trials.

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Title: A simulation-based method to evaluate the impact of product architecture on product evolvability
Author: Jianxi Luo
Publication date: 01-10-2015
Publisher: Springer London
Published in: Research in Engineering Design / Issue 4/2015
Print ISSN: 0934-9839
Electronic ISSN: 1435-6066
DOI: https://doi.org/10.1007/s00163-015-0202-3

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