Two-stage approach for machine-part grouping and cell layout problems

Cellular manufacturing system (CMS) which is based on the concept of group technology (GT) has been recognized as an efficient and effective way to improve the productivity in a factory. In recent years, there have been continuous research efforts to study different facet of CMS. Most of them concentrated on distinguishing the part families and machine cells either simultaneously or individually with the objective of minimizing intercellular and intracellular part movements. This is known as machine-part grouping problem (MPGP) which is a crucial process while designing CMS. Nevertheless, in reality some components may not be finished within only one cell, they have to travel to another cell(s) for further operation(s). Under this circumstance, intercellular part movement will occur. Different order/sequence of machine cells allocation may result in different total intercellular movement distance unit. It should be noted that if the production volume of each part is very large, then the total number of intercellular movement will be further larger. Therefore, the sequence of machine cells is particularly important in this aspect. With this consideration, the main aim of this work is to propose two-stage approach for solving cell formation problem as well as cell layout problem. The first stage is to identify machine cells and part families, which is the essential part of MPGP. The work in second stage is to carry out a macro-approach to study the cell formation problem with consideration of machining sequence. The impact of the sequencing for allocating the machine cells on minimizing intercellular movement distance unit will be investigated in this stage. The problem scope, which is a MPGP together with the background of cell layout problem (CLP), has been identified. Two mathematical models are formulated for MPGP and CLP respectively. The primary assumption of CLP is that it is a linear layout. The CLP is considered as a quadratic assignment problem (QAP). As MPGP and QAP are NP-hard, genetic algorithm (GA) is employed as solving algorithm. GA is a popular heuristic search technique and has proved superior performance on complex optimization problem. In addition, an industrial case study of a steel member production company has been employed to evaluate the proposed MPGP and CLP models, and the computational results are presented.