Incorporating constraints in system modularization by interactive clustering of design structure matrices

Modularity is an approach to manage the design of complex systems by partitioning and assigning elements of a concept to simpler subsystems according to a planned architecture. Functional-flow heuristics suggest possible modules that have been demonstrated in past products, but using them still leaves it to the designer to choose which heuristics make sense in a certain architecture. This constitutes an opportunity for a designer to take other constraints and objectives into account. With large complex systems, the number of alternative groupings of elements into modular chunks becomes exponentially large and some form of automation would be beneficial to accomplish this task. Clustering algorithms using the design structure matrix (DSM) representation search the space of alternative relative positioning of elements and present one ideal outcome ordering which "optimizes" a modularity metric. Beyond the problems of lack of interactive exploration around the optimized result, such approaches also partition the elements in an unconstrained manner. Yet, typical complex products are subject to constraints which invalidate the unconstrained optimization. Such architectural partitioning constraints include those associated with external force fields including electric, magnetic, or pressure fields that constrain some functions to perform or not perform in different regions of the field. There are also supplier constraints where some components cannot be easily provided with others. Overall, it is difficult to simply embed all objectives of modular thinking into one metric to optimize. We develop a new type of interactive clustering algorithm approach considering multiple objectives and partitioning constraints. Partitioning options are offered to a designer interactively as a sequence of clustering choices between elements in the architecture. A designer can incorporate constraints that determine the compatibility or incompatibility of elements by choosing among alternative groupings progressively. Our aim is to combine computational capability of clustering algorithms with the flexibility of manual approaches. Through applying these algorithms to a MRI machine injector, we demonstrate the benefits of interactive cooperation between a designer and modularity algorithms, where constraints can be naturally considered.