A multi-objective Bi-level leader-follower joint optimization for concurrent design of product family and assembly system

Abstract

Incorporating interface modularity into the traditional structural–functional modular-based product family architecture (PFA) is of paramount importance for efficient assembly configuration. The structural–functional modularity for PFA only ensures technical system modularity (TSM) with the increasing complexity of assembly and leads to a high redesign cost. In particular, the individual modules and components are assumed to have equal and/or fixed connection values, thereby overlooking the impact of modularity on the assembly configuration. The major challenge for integrating assemblability into a PFA is determining the optimal granularity of modules under the coherent framework of TSM and interface modularity which is often widely different. Responding to the challenge, a decision hierarchy is presented to describe the concurrent decisions of the PFA and assembly configuration through a multi-objective bi-level leader–follower joint optimization (LFJO). The main objective is to maximize profit by ensuring modules with higher commonality and lower interface complexity, which facilitates the design and assembly processes. Consistent with the bi-level optimization, a multi-objective nested bi-level genetic algorithm (M-NBGA) is developed to solve the bi-level LFJO problem efficiently. Numerical examples (i) demonstrate the applicability of the proposed model and algorithm, (ii) manifest the benefit of considering the level of interactions to deal with the product architecture, (iii) validate the contribution of interface modularity in reducing assembly complexity, and (iv) reveal the effect of modular granularity on design and assembly cost.