Assessing Shop Floor Layouts in the Context of Process Plans
Read this article. The paper seeks to compare performance of three layouts. Do you agree or disagree with the findings of the research?
Description of the proposed method
Design of experiments
According to Sanchez, experimental design employs factors, which are independent variables, to test the impact on the outputs of the experiments, which are the response variables. As stated by Montgomery, this technique is useful to verify if a given factor influences, or not, the response of a system.
For Gomes and Costa, an experimental problem can be supported by the design of experiments and the statistical analysis of the data. There are several types of data collection strategies for DOE, and one of them is the complete factorial design.
Table 5 shows the control factors used in this paper.
Table 5 Control factors used in the proposed method.
Factor | Description | Level | Value |
---|---|---|---|
Features per part | Quantity of features in the part | Low | 1 to 3 features per part |
High | 7 to 10 features per part | ||
Duplicated Features | Quantity of duplications of the same feature in each part | Low | 1 to 3 features |
High | 7 to 10 features | ||
Batch Size | Quantity of parts in each batch | Low | 10 to 50 parts |
High | 200 to 500 parts | ||
Type of Demand | Type of demand which will be applied to the system | Low | Random |
High | Controlled | ||
Type of Plan | Process plan used in simulation (with or without alternatives) | Low | Plan With alternatives |
High | Plan Without alternatives | ||
Simulated Batches | Total quantity of simulated batches in each replication | Low | 50 batches |
High | 200 batches | ||
Arrival time | Arrival time from the current batch to the next batch | Low | 5% to 10% of processing time |
High | 20% to 30% of processing time | ||
Maintenance | Maintenance concepts applied in the machines | Low | With maintenance |
High | Without maintenanc |
The response variables that are considered: throughput (parts/hour), work in process (parts), resource utilization (%), final time (hours), total waiting time (hours) and lead time (hours).
In order to perform the analysis, three types of layout are considered: the job shop layout, the traditional cell layout and the virtual cell layout. Both process plans with alternatives as well as conventional plans (without alternatives) are considered. In the case of selecting an alternative operation, a penalty of 40% in the machining time is applied. An explanation for this penalty is that, if a hole needs to be made in a part, it may be performed in a milling machine or a drilling machine. Usually a milling machine will be chosen due to its versatility when compared with the drilling machine. But since the drilling machine can also be used to make the hole, it is included as an alternative in the process plan, but a less desirable one.
In order to reduce errors inherent to the variability in the execution of the simulation, 10 replications were performed for each combination of factors, that is, each experiment was replicated 10 times. Also, a full factorial design was used, meaning that all possible combinations were performed.
According to table 2 , there are eight control factors and, since only two levels were chosen in each factor (high and low), 2^8 = 256 experiments were tested. Since for each experiment ten simulations were run, a total of 2,560 experiments were performed. This set of 2,560 experiments was applied to each of the three different layouts (job shop, traditional cell and virtual cell), totaling 7,680 experiments. The total simulation times were one of the control factors of the experiment, and the warm-up time was 1h. That is, the first hour of each simulation was excluded from the statistical analysis.