Integrated bi-objective model for cellular manufacturing system

Today’s globalized business environment has brought about new and greater challenges to manufacturers: product life cycles are shortened; consumer demands and expectations change rapidly, and manufacturing technologies have seen significant advances. Many attempts have been made to overcome these challenges and adopting the Cellular Manufacturing System (CMS) is one of the most successful. CMS is an efficient application of group technology, with the capability to overcome these problems. CMS and dynamic environment over recent years have gained a lot of attention. This study proposed a comprehensive intelligent nonlinear mathematical model, the nonlinear terms of which were linearized through linearization steps to identify machine cells, choose optimum machine layout for each cell and also assign part operations to the machines in each period to minimize total cost and completion time, with maintenance planning considered as one of the key advantages. This model can intelligently provide the most proper maintenance plan for each duplicate of machine type in each production period. This model is able to consider operation sequences, processing time, production volumes of parts, machine rotation, define distance between machines, duplicate machines, unequal-area facilities and machine relocations under the dynamic situation in CMS. This study offers five contributions namely, a relation to the comprehensive mixed-integer mathematical model formulated to define cell formation, design layout for unequal-area machines and also assign parts operations to the machines in DCMS as a first contribution. It is also capable of locating machines horizontally or vertically in each cell in each period to achieve the most efficient layout as a second contribution. A third contribution is its capability to consider the defined distance between machines for movement of parts and labor to achieve an applicable solution in DCMS. A fourth contribution is related to the model’s intelligent capability to decide smartly which of two available maintenance plans should be used for each machine type. The study’s fifth contribution is related to the model’s bi-objectives, concentrating simultaneously on minimization of total cost and completion time in contrast to previous studies which concentrated on only one objective function. The proposed model was coded and simulated in GAMS during five different steps and in each step one contribution was added for validation by applying the existing data in the literature. Besides the extracted data from the literature, a case study was considered to prove the model’s validity. The results showed that, by implementing the proposed model in the manufacturing system, the overhead cost of the system is reduced by 7.37% and also the operation cost was reduced from US$25473 to US$23180, which is equal to nine percent reduction and consequently the total cost is reduced by 2.6% (from US$191308 to US$186185). In addition, results clarified the cells, machines layout, parts operations assignment and also proper maintenance plan for each machine which were model objectives, plus, the obtained results indicated that cost and completion time were changed in line with demand changes. The proposed model is therefore able to help managers make decisions in designing and planning for an optimized DCMS in terms of total cost and completion time.

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