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This article presents an engineering method based on the collaborative optimization consistency constraint function for selecting shared components in modular platforms; the method allows the operator to minimize the differences in design variables across various models simultaneously. A case study using the body-in-white information on three vehicles serves as an example. Static bending conditions and static torsion conditions are comprehensively considered with the mass and the maximum displacement as the optimization objectives in Isight and Hyperworks (both of which are commercially available software applications commonly used by automotive engineers.) The results suggest that this method can be easily and effectively utilized in the conceptual phase of car design.

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Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch stochastic alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed networks, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-controlcoding based stochastic incremental ADMM is investigated. Given an appropriate mini-batch size, we show that the minibatch stochastic ADMM based method converges in a rate of 𝑂 ( 1 √ 𝑘), where 𝑘 denotes the number of iterations. Through numerical experiments, it is revealed that the proposed algorithm is communication-efficient, rapidly responding and robust in the presence of straggler nodes compared with state of the art algorithms.

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