\r\npreliminary design stage introduces a level of complexity which

\r\ncannot be realistically handled by a single optimiser, be that a

\r\nhuman (chief engineer) or an algorithm. The design process is often

\r\npartitioned along disciplinary lines, with each discipline given a level

\r\nof autonomy. This introduces a number of challenges including, but

\r\nnot limited to: coupling of design variables; coordinating disciplinary

\r\nteams; handling of large amounts of analysis data; reaching an

\r\nacceptable design within time constraints. A number of classical

\r\nMultidisciplinary Design Optimisation (MDO) architectures exist in

\r\nacademia specifically designed to address these challenges. Their

\r\nlimited use in the industrial aircraft design process has inspired

\r\nthe authors of this paper to develop an alternative strategy based

\r\non well established ideas from Decision Support Systems. The

\r\nproposed rule based architecture sacrifices possibly elusive guarantees

\r\nof convergence for an attractive return in simplicity. The method

\r\nis demonstrated on analytical and aircraft design test cases and its

\r\nperformance is compared to a number of classical distributed MDO

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