Multi-objective machine-component grouping in cellular manufacturing: a genetic algorithm

Abstract

The cellular manufacturing system (CMS) is an important group technology (GT) application. The first step of CMS design is cell formation, generally known as machine-cell formation (MCF) or machine-component grouping (MCG). A genetic algorithm (CA) is a robust adaptive optimization method based on principles of natural evolution and is appropriate for the MCG problem, which is an NP complete complex problem. In this study, we propose a GA-based procedure to solve the MCG problem. More specifically, this study aims to minimize (1) total cost, which includes intercell and intracell part transportation costs and machines investment cots; (2) intracell machine loading imbalance; and (3) intercell machine loading imbalance under many realistic considerations. An illustrative example and comparisons demonstrate the effectiveness of this procedure. The proposed procedure is extremely adaptive, flexible, efficient and can be used to solve real MCG problems in factories by providing robust manufacturing cell formation in a short execution time.