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This paper addresses the suitability of cellular manufacturing under a variety of operating conditions. Queueing theoretic and simulation models of cellular and functional layouts are developed for various shop operating environments to investigate several factors believed to influence the benefits associated with a cellular manufacturing layout. The queueing models show how operations overlapping, which is more practical with a cellular layout, becomes more beneficial as the lot size increases. The simulation models are developed to study the performance of cellular and functional layouts in a wide variety of operating environments by varying the levels of four factors: (1) the degree to which natural part families occur, (2) the number of operations required to process the parts, (3) the processing times of the parts at each machine, and (4) the lot size. Two response variables are used to measure shop performance: the average time spent by a batch in the system, and the average work-in-process level. Statistically significant reductions in the average time in the system and average work-in-process measures were detected for the cellular layouts in all the operating environments studied.

The main goal of this paper is to describe a new multistage Monte Carlo (MMC) simulation method for solving influence diagrams using local computation. Global methods have been proposed by others that sample from the joint probability distribution of all the variables in the influence diagram. However, for influence diagrams having many variables, the state space of all variables grows exponentially, and the sample sizes required for good estimates may be too large to be practical. In this paper, we develop a MMC method, which samples only a small set of chance variables for each decision node in the influence diagram. MMC is akin to methods developed for exact solution of influence diagrams in that we limit the number of chance variables sampled at any time. Because influence diagrams model each chance variable with a conditional probability distribution, the MMC method lends itself well to influence diagram representations.decision analysis, approximations, sequential, simulation, applications, Monte Carlo methods, local computation

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