Multi-Echelon Inventory Optimization Using Deep Reinforcement Learning

Patric Hammler (), Nicolas Riesterer (), Gang Mu () and Torsten Braun ()
Additional contact information
Patric Hammler: Universität Bern
Nicolas Riesterer: F. Hoffmann-La Roche AG
Gang Mu: University of Zurich
Torsten Braun: Universität Bern

A chapter in Quantitative Models in Life Science Business, 2023, pp 73-93 from Springer

Abstract: Abstract In this chapter, we provide an overview of inventory management within the pharmaceutical industry and how to model and optimize it. Inventory management is a highly relevant topic, as it causes high costs such as holding, shortage, and reordering costs. Especially the event of a stock-out can cause damage that goes beyond monetary damage in the form of lost sales. To minimize those costs is the task of an optimized reorder policy. A reorder policy is optimal when it minimizes the accumulated cost in every situation. However, finding an optimal policy is not trivial. First, the problem is highly stochastic as we need to consider variable demands and lead times. Second, the supply chain consists of several warehouses incl. the factory, global distribution warehouses, and local affiliate warehouses, whereby the reorder policy of each warehouse has an impact on the optimal reorder policy of related warehouses. In this context, we discuss the concept of multi-echelon inventory optimization and a methodology that is capable of capturing both, the stochastic behavior of the environment and how it is impacted by the reorder policy: Markov decision processes (MDPs). On this basis, we introduce the concept, its related benefits and weaknesses of a methodology named Reinforcement Learning (RL). RL is capable of finding (near-) optimal (reorder) policies for MDPs. Furthermore, some simulation-based results and current research directions are presented.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spbchp:978-3-031-11814-2_5

Ordering information: This item can be ordered from
http://www.springer.com/9783031118142

DOI: 10.1007/978-3-031-11814-2_5

Access Statistics for this chapter

More chapters in SpringerBriefs in Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().