Abstract

Traditionally, flow times distance is used as a surrogate for cost in facility design. However, this performance measure does not fully capture the impact of facility design decisions on operational performance measures such as cycle time and work-in-progress in the system, which are often more meaningful for managers, especially in a manufacturing environment. To better measure operational performance, modelling of material handling systems using a queueing network must be integrated in the facility design process. A number of approaches are discussed in the facility design literature for modelling material flow using queueing networks. In these approaches, Poisson arrival or Markovian job routing assumptions have been used. However, for many manufacturing environments, these assumptions lead to an inaccurate estimation of the material handling system's performance and thus lead to poor facility designs. Incorporating more general queueing results for non-Markovian systems is difficult, however, because the facility design process must investigate a large number of potential solutions and thus the results from the queueing models for the material handling system must be quickly obtained. In this paper, the need for more general queueing models of material handling systems in facility design is confirmed. Then, an approach based on multi-class queueing models is adapted to capture the change in variability of the system performance caused by both different arrangements of workstations in the facility and different arrival processes to the workstations due to the job routing in a computationally efficient manner. The proposed modelling approach is shown to provide more accurate results than previous methods used in facility design based on numerical comparisons with results from discrete-event simulation.

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