Action reward, a framework for inventory optimization

The action reward is a vast generalization of the newsvendor problem featuring: nonstationary demand, incoming purchase orders, lead times, halfway through stockouts, future decisions and more.

******
******

Timestamps:
0:00:05 - Introduction
0:00:23 - The inventory control problem
0:00:54 - An uncertain demand
0:01:44 - Choosing a model and a reward function
0:03:20 - Single-period inventory control
0:04:09 - Quantifying the order decisions 1/2
0:06:23 - Quantifying the order decisions 2/2
0:07:39 - Multi-period inventory control
0:10:55 - Quantifying the order decisions 1/4
0:12:05 - Quantifying the order decisions 2/4
0:13:22 - Quantifying the order decisions 3/4
0:14:41 - Quantifying the order decisions 4/4
0:15:21 - Action reward implementation in Envision
0:16:37 - Envision plot of the action reward
0:17:12 - Limited budget: Optimizing ROI
0:19:47 - Envision code example
0:20:23 - Prioritized purchase list
0:21:20 - Conclusion

[dijin]

Thanks for sharing! Like the video and concept very much. I just wonder how to we take ordering cost into account in this framework. Suppose there are two replenishment options:
Option I: restock 10 every two reorder time cycles
Option II: restock 5 every one reorder time cycles
Suppose both options can cover the demand. This rewards function will prefer Option II instead of Option I. But if we take the ordering (shipment...) cost into account, Option I might be the right Option. Could you please comment on that? Thanks!

[AdolphVogel]

Interesting concept. How does the lead time uncertainty influence the responsibility window?