We then present a nonlinear integer programming formulation for a multi-product, single machine, deterministic dynamic lot sizing problem with the objective of minimizing the total costs of WIP, finished goods inventory (FGI) with backlogging.
Since this model is computationally intractable even for small instances, an approximate solution is constructed by applying a simple myopic rounding scheme to the solution obtained from a continuous relaxation.
While this approach can, of course, make no claims of optimality, it permits computational experiments comparing the solutions thus obtained to those from exact integer programming models that do not con- sider congestion.
We find that our model provides lower cycle times and WIP levels than an alternative model that does not consider congestion.
In addition, discrepancies between planned and realized performance are considerably smaller in our model, despite the extremely simple approximation approach used.
These findings suggest that even with the very simple approximate solution procedure we use, the explicit consideration of congestion in lot sizing problems can be beneficial.
The next section presents a brief review of previous related work.
Section 3 introduces the functions used to represent the nonlinear relation between queue size and lead times of products.
Section 4 incorporates the functions developed in Section 3 into a multi-product dynamic lot sizing model.
Section 5 introduces the conventional lot sizing model without congestion that is used as a benchmark in our experiments.
Section 6 presents the computational experiments comparing the performance of the models.
We conclude the paper with a summary of the main conclusions and highlight some possible directions for future work.