Graph-based Multi-Objective Generation of Customised Wiring Harnesses

Electrical wires are a very important part of a vehicle as well of many other objects with electrical functions. These wires are summed up to harnesses. Engineers often manually create the virtual paths for wires by considering their expert knowledge. More variants of a product often require different wires and the criteria for cables do vary. Criteria, e.g. heat or the length, define the wire path and configuration and have to be taken into account. Engineers may not be able to consider all criteria, notably when there is an extensive amount of product variants, or when the object configuration is changing. This paper will evaluate if a multi-objective evolutionary algorithm can compute feasible wire paths where two of these criteria are optimised. We are showing that discretised free-space in a graph structure enables an evolutionary algorithm to compute wire suggestions by using customised reproduction operators for path planning. The developed crossover and mutation operators are suitable for multi-objective path finding, may be used in other fields as well and will be extended in future research.