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The former course is about inventories in general, and it focuses on multi-echelon production/inventory systems. The latter focuses on single-location models, and especially in settings with multiple items and joint replenishment costs and with multiple demand classes,
The current course focuses on the interplay between Markov Decision Processes (MDPs) and inventory control. It also demonstrates the relations of some basic facts on inventory control to simple concepts in calculus such as continuity and convexity of functions. In our days MDPs provide mathematical foundations to artificial intelligence, but for a long period of time inventory control applications were among the major factors for investigations of Markov decision processes.
Periodic review inventory control problems.
Problems with lost sales and back orders.
Unlimited and limited storage and limited and unlimited order sizes.
Markov decision processes (MDPs).
Relations of MDPs to inventory management.
Optimality criteria: finite horizon, infinite‐horizon expected discounted costs, average costs per unit time.
Continuous and semi-continuous functions.
MDPs with infinite state sets.
Optimality equations and their properties.
Algorithms for finding optimal policies.
Optimality equations for inventory management problems.
The structure of optimal policies for problems with lost sales and backorders; (s,S) policies.
Problems with lead times. Cash management problems. Problems with borrowing. Robust optimization of inventory management systems.
Simchi‐Levi, Xin Chen, Julien Bramel, The logic of Logistics, Second Edition, Springer, 2005, Part II: Inventory Models