An integer programming approach for quick-commerce assortment planning

TL;DR

This paper presents an integer programming method with convex hull results for efficient assortment planning in quick-commerce, optimizing product selection for online and physical stores to maximize revenue.

Contribution

It introduces a novel integer programming approach with convex hull characterizations that improve computational efficiency in quick-commerce assortment optimization.

Findings

Convex hull results enable better representation of consumer choice models.

The proposed method achieves significant computational advantages over existing approaches.

Application of convex hulls extends to other assortment optimization problems.

Abstract

In this paper, we explore the challenge of assortment planning in the context of quick-commerce, a rapidly-growing business model that aims to deliver time-sensitive products. In order to achieve quick delivery to satisfy the immediate demands of online customers in close proximity, personalized online assortments need to be included in brick-and-mortar store offerings. With the presence of this physical linkage requirement and distinct multinomial logit choice models for online consumer segments, the firm seeks to maximize overall revenue by selecting an optimal assortment of products for local stores and by tailoring a personalized assortment for each online consumer segment. We employ an integer programming approach to solve this NP-hard problem to global optimality. In particular, we derive convex hull results to represent the consumer choice of each online segment under a general class of operational constraints, and to characterize the relation between assortment decisions and choice probabilities of products. Our convex hull results, coupled with a modified choice probability ordered separation algorithm, yield formulations that provide a significant computational advantage over existing methods. Finally, we illustrate how our convex hull results can be used to address other assortment optimization problems.