Conference Agenda

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Session Overview
Session
TD10 - RT8: Assortment planning 2
Time:
Tuesday, 28/June/2022:
TD 16:00-17:30

Session Chair: Arash Asadpour
Location: Forum 14


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Presentations

Assortment planning with n-pack purchasing consumers

Dorothee Honhon1, Ying Cao2

1University of Texas at Dallas, United States of America; 2Black School of Business Penn State Erie, The Behrend College

We study the assortment planning problem for a single product category when a retailer faces multi-item purchasing, so-called “n-pack” consumers as introduced by Fox et al (2017). We obtain interesting properties of the product demand functions and establish that the optimal assortment is a popular set. We evaluate our model on a real-life data set and find that the demand proportions predicted by our model can be made extremely close to the actual proportion of sales.



Optimizing retail assortment and replenishment

Lena Riesenegger1, Manuel Ostermeier2, Alexander Hübner1

1Technical University of Munich, Germany; 2University of Augsburg

Determining the assortment and inventory levels based on their shelf life is essential for retailers to maximize profits while avoiding food waste. Assortment and inventory decisions are interrelated by the limited shelf space. A joint approach is needed that defines the assortment size and the maximum possible inventory levels while considering product ages. We develop the first multi-period approach to integrate product shelf life and product outdating.



Sequential Submodular Maximization andApplications to Ranking an Assortment of Products

Arash Asadpour1, Rad Niazadeh2, Amin Saberi3, Ali Shameli4

1Zicklin School of Business, City University of New York,; 2Chicago Booth School of Business, University of Chicago; 3Management Science and Engineering, Stanford University; 4Facebook

Motivated by the product ranking in online retail, we introduce and study the "sequential submodular maximization problem". Given an ordered list of products, a user inspects first $k$ products in the list for a $k$ drawn from a given distribution, and decides whether to purchase an item based on a choice model. The goal is to find an ordering maximizing the probability of purchase. We design near-optimal approximation algorithms for this problem, with or without group fairness constraints.



 
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