Solving a new bi-objective model for a cell formation problem considering labor allocation by multi-objective particle swarm optimization

Mathematical programming and artificial intelligence (AI) methods are known as the most effective and applicable procedures to form manufacturing cells in designing a cellular manufacturing system (CMS). In this paper, a bi-objective programming model is presented to consider the cell formation problem that is solved by a proposed multi-objective particle swarm optimization (MOPSO). The model contains two conflicting objectives, namely optimal labor allocation and maximization of cell utilization. The related results of the proposed MOPSO are compared with the results obtained  by a well-known evolutionary procedure, called NSGA-II, in order to verify its effectiveness.

Keywords:

cellular manufacturing system , Cell formation , Labor allocation , multi , objective particle swarm optimization