🔬 Third lab

🛠 Task 3.1: Inventory model with deterministic demand

⏱ Expected work time 40 minutes

• ☑️ Follow instructions in the notebook lab3/inventory_deterministic.ipynb

• ☑️ Copy over all the code about the inventory model from the lecture notes

• ☑️ Run the code and “play with the model”

🛠 Task 3.2: Inventory model with stochastic demand

⏱ Expected work time 60 minutes

• ☑️ Follow instructions in the notebook lab3/inventory_stochastic.ipynb

• ☑️ Fill out the missing parts of the code using the specification in the lecture notes

In addition:

• ☑️ Using your complete code, develop a simulator for the model similar to the one we had for hte static logit model. Simulate a sequence of demands, and use your policy function to make optimal ordering decisions over time. Track the inventory levels, orders, costs, and stockouts over time.

🛠 Task 3.3 [optional]: Policy iterations algorithm

⏱ Expected work time 30 minutes

• ☑️ Only attempt if you have completed Task 3.1, and all previous labs

• ☑️ Study the notebook lab3/inventory_policy_iter.ipynb

• ☑️ Make sure you understand the policy iteration algorithm, and every line of the code

• ☑️ Write code to compare the VFI run time against the policy iteration run time for different sizes of the state space and other model specifications. Where are the biggest improvements from using the policy iteration algorithm?