We model a production system with multiple machines, each serving a variety of jobs. The machines require setups to switch from one job to another. Setup operations are performed by a limited number of setup crews who are cross-trained to perform all setup operations. We develop an approximation model that takes into account the effect of delays caused by unavailability of the setup crews, and obtain average job waiting times in the system. A numerical study demonstrates that our approximation performs very well. Our study also provides insights into the importance of explicitly modeling the effects of cross-trained setup crews on performance measures, and whether and when the cross-training of setup crews improves system performance.
This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model.
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