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This paper presents the results of a simulation-based performance comparison between dynamic cellular (DC) manufacturing systems and two other well-known systems, namely classical cells (CC) and job shop systems (JS). The performance comparison is made at different levels of turbulence. The experiment contains 13 independent variables, most of them related to demand turbulence, and 17 dependant variables related to performance measure. A stochastic simulation model developed on Microsoft Visual FoxPro 5.0 was used in combination with LINDO (a linear/integer programming software) to obtain the initial results. In view of the large number of variables and the time required to run each experiment, a Taguchi plan was used to optimize the model. The results obtained from the analysis of variance indicate that dynamic cellular manufacturing systems are generally more efficient than classical cellular systems or job shop systems, especially with respect to the average and maximum throughput time, mean and maximum work-in-process, mean and maximum tardiness, and the total marginal cost for a given horizon. [ABSTRACT FROM AUTHOR]

Using simulation, experimental design, and regression analysis, mathematical models are developed here to describe the effect that maintenance policy, machine unreliability, processing time variability, ratio of preventive maintenance time to processing time, ratio of minimal repair time to preventive maintenance time, and production line size have on various measures of performance, namely total production line output and production line variability of the just-in-time production system. The analysis of the data shows that under different situations, different maintenance policies do not have the same effect on the production line performance. The following conclusions were obtained; when the number of production machines is low (five machines or less), and/or when the ratio of minimal repair time to preventive maintenance time is high, maintenance policy III leads to a higher performance than maintenance policy 11. Otherwise maintenance policy IL, which is more sensitive to the change of the ratio of minimal repair time to preventive maintenance time, leads to a higher performance. The results of the study can be utilized in choosing a maintenance policy as a function of the production process parameters. Once a policy is chosen, the practitioner can select the most important factors to control under that policy in order to minimize the machine idle time, maximize the production process reliability, improve productivity, and therefore increase the production line performance. [ABSTRACT FROM AUTHOR]