Cell formation problem with consideration of both intracellular and intercellular movements

The cellular manufacturing system (CMS) is a well-known strategy which enhances production efficiency while simultaneously cutting down the system-wide operation cost. Most of the researchers have been focused on developing different approaches in order to identify machine-cells and part-families more efficiently. In recent years, researchers have also focused their studies more scrupulously by collectively considering CMS with production volume, operation sequence, alternative routing or even more. However, very few of them have tried to investigate both the allocation sequence of machines within the cells (intra-cell layout) and the sequence of the formed cells (inter-cell layout). Solving this problem is indeed very important in reducing the total intracellular and intercellular part movements which is especially significant with large production volume. In this paper, a two-phase approach has been proposed to tackle the cell formation problem (CFP) with consideration of both intra-cell and inter-cell part movements. In the first phase, a mathematical model with multi-objective function is formed to obtain the machine cells and part families. Afterwards, in the second phase, another mathematical model with single-objective function is presented which optimizes the total intra-cell and inter-cell part movements. In other words, the scope of problem has been identified as a CFP together with the background objective of intra-cell and inter-cell layout problems (IAECLP). The primary assumption for IAECLP is that only linear layouts will be considered for both intra-cell and inter-cell. In other words, the machine within cells and the formed cells are arranged linearly. This paper studies formation of two mathematical models and used the part-machine incidence matrix with component operational sequence. The IAECLP is considered as a quadratic assignment problem (QAP). Since QAP and CFP are NP-hard, genetic algorithm (GA) has been employed as solving algorithm. GA is a widespread accepted heuristic search technique that has proven superior performances in complex optimization problems and further it is a popular and well-known methodology. The proposed algorithms for CFP and IAECLP have been implemented in JAVA programming language.