The cycle time-throughput curve is one of the most important analytical tools used to assess operating policies in manufacturing systems. Unfortunately, the generation of this curve is complicated and time consuming when generation is based upon extensive simulation analysis. This paper presents a simulation-based, fixed sample size strategy for generating a cycle time-throughput curve with minimal mean square error that mitigates the typical problems associated with a simulation-based cycle time-throughput curve. The strategy comprises two components, the sampling method and sampling weights. A queuing network of five workstations in series is used for validation of the approach. Results indicate that the sampling method using antithetic variates is effective in reducing the variance as well as bias of a cycle time-throughput curve. Furthermore, this method is robust to the sample size. Given a sufficiently large sample, the combination of common random numbers and antithetic variates is preferred. A reduction in the sample size and complexity of the system increases the significance of the sampling weights.
ASJC Scopus subject areas
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering