قانون بر پایه بهینه سازی مبتنی بر آزادی سفارش در کنترل حجم کار: یک مطالعه شبیه سازی از یک مکتب تولید کننده

The paper presents a simulation study that compares multi-period models for order release optimisation (Input/Output Control with fixed lead times and a clearing function model) with a traditional workload control-based order release mechanism (control of aggregate workload with periodic release). For the optimisation models the study assumes periodic re-planning and thus can also assess the effects of demand predictability. The simulation model is based on a practical case of a manufacturer of optical storage media. The results indicate that optimisation models for order release planning largely outperform traditional workload control-based order release mechanisms even in the case of poor demand forecasts.

We can conclude that order release planning using the Input/Output Control model largely outperforms the traditional release mechanism even if demand predictability is low, except in the case of largely constant demand and product mix. Eliminating the fixed lead time constraint as in the clearing function model potentially should further improve the results, although there are still some technical difficulties with this type of model. The results indicate that in a number of practical cases the (relatively complex) order release optimisation models perform better than the (relatively simple) traditional order release mechanisms. This indicates a trade-off between complexity and performance even in the case of customer order driven production that was simulated here. The study presented in this paper provides important insights, but we are aware of its limitations. Due to the long computation time (about 15 h per scenario) only two values for each experimental factor have been examined. Extending the forecast and planning horizon to at least one seasonal cycle might make sense, especially in a make-to-stock environment. A thorough comparison of the CF model with tradRM and IOC remains to be done. This requires a theoretically consistent logic to modify the parameters of the clearing function (e.g., a quantile regression). Asmundsson et al. (2009) use a “conservative clearing function” that lies below a certain percentage of the data points. Although many of the results are in line with the present literature and with what we would expect via intuition, the quantitative results are limited to the simulated case. The validity of the results for other product and material flow structures and for other demand processes must be assessed in future studies. Since the sensitivity of the optimization-based release procedures (IOC and CF) to the accuracy of the demand forecasts is crucial for the relative performance of the rule-based and the optimization-based approach, the question of which factors influence this sensitivity is of particular importance. Continuing this study to include these new experimental factors would be a logical extension.