Joint Assortment and Inventory Planning for Heavy Tailed Demand

Housni, Omar El; Goyal, Vineet; Humair, Salal; Mouchtaki, Omar; Sadighian, Ali; Wu, Jingchen

doi:10.2139/ssrn.3832909

Cited by 4 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledge, Dong et al (2009), Kim (2020), andEl Housni et al (2021) are the only other works that focus on a problem setting that is close to ours, albeit where demand is modeled via a particular choice model. Furthermore, the problem settings considered in the three aforementioned papers differ from ours in that the initial inventory decision is unconstrained, but the DM incurs a cost for each unit stocked.…”

Section: The Most Closely Related Workmentioning

confidence: 98%

Approximation schemes for the joint inventory selection and online resource allocation problem

Chen

Feldman

Kouvelis

2022

Production and Operations Management

View full text Add to dashboard Cite

In this paper, we introduce and study the joint inventory selection and online resource allocation problem, which is characterized by two sequential sets of decisions that are irrevocably linked. First, a decision maker (DM) must select starting inventory levels for a set of available resources. Subsequently, the DM must match arriving customers to available resources in an online fashion so as to maximize expected reward. We first study the problem in its most general form, before focusing on a specific version that arises at Anheuser Busch InBev (ABI). This particular application of our general setting is referred to as the ABI Trailer Problem, and it considers how ABI ships its beer to vendors via third‐party delivery trucks. In this problem, ABI must select the weights of preloaded trailers of beer, which are then matched in an online fashion to the arriving third‐party delivery trucks. For the general setting, we develop simple and easy‐to‐implement approaches that come with robust worst‐case performance guarantees. For the ABI setting, we reveal a simplifying structural property related to the optimal matching policy, which gives rise to a natural adaptation of our original approach. We test the efficacy of these policies through extensive numerical experiments, where we find that our approaches are either near‐optimal or improve upon state‐of‐the‐art benchmarks. In particular, using a data set from ABI, we are able to generate instances of the ABI Trailer Problem, on which our algorithm has the potential to yield revenue improvements in the range of millions of dollars per year.

show abstract

Section: The Most Closely Related Workmentioning

confidence: 98%

Approximation schemes for the joint inventory selection and online resource allocation problem

Chen

Feldman

Kouvelis

2022

Production and Operations Management

View full text Add to dashboard Cite

show abstract

Uncovering Critical Products in Retail Baskets: A Predictive Modelling Approach to Increase Order Fulfilment

Gopal,

Mudunuri,

Dutta

et al. 2023

The Third International Conference on Artificial Intelligence and Machine Learning Systems

View full text Add to dashboard Cite

Online Learning for Constrained Assortment Optimization Under Markov Chain Choice Model

Li,

Luo,

Huang

et al. 2024

Operations Research

View full text Add to dashboard Cite

Assortment optimization finds many important applications in both brick-and-mortar and online retailing. Decision makers select a subset of products to offer to customers from a universe of substitutable products, based on the assumption that customers purchase according to a Markov chain choice model, which is a very general choice model encompassing many popular models. The existing literature predominantly assumes that the customer arrival process and the Markov chain choice model parameters are given as input to the stochastic optimization model. However, in practice, decision makers may not have this information and must learn them while maximizing the total expected revenue on the fly. In “Online Learning for Constrained Assortment Optimization under the Markov Chain Choice Model,” S. Li, Q. Luo, Z. Huang, and C. Shi developed a series of online learning algorithms for Markov chain choice-based assortment optimization problems with efficiency, as well as provable performance guarantees.

show abstract

Joint Assortment and Inventory Planning for Heavy Tailed Demand

Cited by 4 publications

References 48 publications

Approximation schemes for the joint inventory selection and online resource allocation problem

Approximation schemes for the joint inventory selection and online resource allocation problem

Uncovering Critical Products in Retail Baskets: A Predictive Modelling Approach to Increase Order Fulfilment

Online Learning for Constrained Assortment Optimization Under Markov Chain Choice Model

Contact Info

Product

Resources

About