Simulation of Intermittent (job shop) Systems
Simulation techniques can be used to evaluate various sequencing rules in job shop facilities. The following is list of data the modeler must be able to specify in order to stimulate the sequencing problem. The modeler can use historical data and patterns for the purpose and during simulation can use the Monte Carlo method to randomly select portions of the historical data that the simulation requires as it runs.
- Work centers- The number of work centers in the shop must be specified.
- Job arrivals- The pattern and timing of jobs “arriving” at the facility must be specified.
- Job classification- The processing requirements or routing of jobs must be specified.
- Processing time- The time it takes to process jobs must be specified.
- Performance parameters-Any number of parameters that gauge the performance at the facility can be incorporated into the simulation; the quantification of these parameters must be specified. Options include percent idle time, amount of inventory, average lateness of jobs, average job flow, and so on.
- A Sequencing rule must be specified
The simulation known as a simulation run is conducted over time. The simulation runs through a very large number of jobs, say 10,000 or more. The simulation generates new jobs arriving at various times, determines their routings, loads them to the appropriate work centers, sequences them according to the sequence rule, and determines their processing times. When a work center completes one job, it begins processing the next job in the queue, according to the sequence rule.
After all jobs have been processed, the simulation evaluates the performance of the facility according to the parameters specified. The performance statistics are saved for later comparison. The modeler may now run the simulation again, specifying a different sequence rule.
When the simulation evaluates the performance of the facility accordingly, the results of both simulation runs can be compared. Any number of sequence rules may be evaluated and compared in this way.
Simulation Results for Job Flow Time
One study tested ten sequence rules in six different job shop configurations using computer simulation. The results are based on processing over 2 million jobs through the simulated system. Our main interest in the results has to do with the job flow performance of the rules, an important concern to shop managers. Job flow is commonly measured in two ways: as the average flow time of jobs through the system; and as the dispersion of job flow times through the system (measured by a standard deviation or variance).
The simulation study found that average (mean) flow time per job was lowest (0.99) using the SPT rule; using other rules it was as high as 2.54. The standard deviation of flow time ranged from 1.55 to 5.43 using the various rules. Although the standard deviation of flow time was lower using two of the other rules, SPT did well on this parameter also. These results are not surprising when you consider how the SPT rule works. Since the highest priority job is the one whose processing time is the highest priority job is the one whose processing time is shortest, this job does not have to wait long in the queue; its flow time (waiting plus processing time) is low.
Simulation results for Job Lateness and Work- in- process Inventories: Using a computer simulation, another researcher examined how well 39 sequencing rules performed in terms of job lateness and inventories. Z In term of percentage of jobs late, SPT performed far better than most other rules tested. This same study found that the SPT rule was not optimal for minimizing in-process inventory, although its performance was still relatively good. The optimal rules were found to be compound rules. They require somewhat more complex calculations than does the SPT rule. These compound rules, all combined into one. In short, the SPT, although not optimal, performed well, and it did so without although not optimal, performed well, and it did so without requiring the extensive calculations of the more complex rules.
