I’m writing to inquire about the application of uncertainty on a supply chain model. My Supply chain model has three echelons that target the production, warehousing, and distribution of medication products. The deterministic version of my model works perfectly, but is far from reality as production equals demand, which is why uncertainty of the demand parameter has to be considered.
I was given a model (https://how-to.aimms.com/Articles/484/484-Uncertainty-productionplanningro.html) that briefly describes how to apply uncertainty to parameters, however, the given model was very small and didn’t help me in applying uncertainty to my desired project.
As stated earlier, I have several medications to be sold in several pharmacies, therefore, the medication demand is not a single number but a matrix. I used the BOX function to define the demand matrix to be between a lower and upper pre-defined parameter matrices.
The first problem occurred when I didn’t assign values to the uncertain parameter, which prompted me to assign original values for the deterministic model, and a basis for the robust model. Afterwards, I kept getting errors and warning messages and the robust counterpart of my deterministic model didn’t show any results.
I read the robust optimization pdf file, and found that variables affected by uncertain parameters can be set to “adjusted”, in addition, I’m not quite sure if I should set constraints containing that uncertain parameter to uncertainty constraints.
- Do I set every variable affected by the uncertain demand parameter to adjustable?
- In addition, do I set every constraint containing the uncertain parameter to be uncertainty constraints?
I’m asking for advice since I don’t want to mess my model by changing variables and constraints.