Mixed-model sequencing optimization for an automated single-station fully flexible assembly system (F-FAS)

Abstract

Flexible automated assembly is an emerging need in several industries. In the case of a very wide set of models and a total medium/low derived production volume, the proper assembly system to use is a single cell with high flexibility capabilities. An innovative concept in flexible automated assembly has recently been introduced [28, 29]: the fully flexible assembly system (F-FAS). The F-FAS relies on a single-station robotized assembly system, where a unique fully flexible feeder is responsible for the delivery of the parts needed for assembly, guaranteeing a higher level of flexibility than the traditional automated FAS. The mixed-model sequencing (MMS) problem is typically related to the assembly line system. The aim of this paper is to introduce a new class of MMS problem: the single-station mixed-model sequencing problem that arises when the parts to assemble are randomly presented on the working plane, as in the F-FAS. The authors first define the MMS in such a single-station assembly system and then propose different sequencing algorithms in order to solve it. The authors first define the problem and then propose different sequencing algorithms. With the aim of finding the best sequencing approach to use in such an assembly system, the algorithms are compared through ad hoc developed benchmarking tests, using a dedicated software application that simulates the real behavior of the work cell.