Tackling Optimization and System-Driven Engineering in Coupling Physical Constraints with MBSE: The Case of a Mobile Autonomous Line of Products

Abstract

The main goal of product line engineering is to build complex system architecture at the best quality, cost, resource <Q,R,T> ratio. The return on investment in terms of <Q,R,T> assessment is however not trivial, as systems to build rise in complexity. Moreover, the perspective of system of systems engineering that set up both historic and new systems in capabilities, and the introduction of more and more autonomous systems in architectures makes the anticipation of return on investment impossible to achieve without computer assistance. The necessary tools to assess <Q,R,T> as precisely as possible also involve a wide exploration of possibilities in an ever-changing context. The absence of mechanical or multi physical aspects in SysML-based tools, either in its version 1.3 or 2.0, makes it inefficient in representing or simulating robotic systems or system of system engineering. This article explains the benefits of tackling a classic Multi-Objective Knapsack Problem (MOKP) to the UGV product line items selection using a seamless system architecting toolchain. The association of MBSE (Model-based System Engineering), OR (Operation Research) and MBD (Model Based design) that generated various designs is presented. Our results in system engineering in UGV presents a Pareto Front of trade-offs that can count as numerous possibilities that sole MBSE or separate MBD simulation could not have represented as the best in the sense of <Q,R,T>. The simultaneous variations in both hardware and mechanical design show was entirely automated using standard tools with no redesign. This shows that seamless automations should pave the future of system engineering tools. With Operation research and Systems engineering tackling methods, our model upscale to real systems will shape System-Driven Engineering that will require new skills in system validation and verification and simulation analytics.