From Small to Large: A Graph Convolutional Network Approach for Solving Assortment Optimization Problems
arXiv:2507.10834v4 Announce Type: replace Abstract: Assortment optimization seeks to select a subset of substitutable products, subject to constraints, to maximize expected revenue. The problem is NP-hard due to its combinatorial and nonlinear nature and arises frequently in industries such as e-commerce, where platforms must solve thousands of such problems each minute. We propose a graph convolutional network (GCN) framework to efficiently solve constrained assortment optimization problems. Our approach constructs a graph representation of the problem, trains a GCN to learn the mapping from
The increasing complexity and scale of e-commerce and other combinatorial optimization problems are driving demand for more efficient AI-driven solutions.
This development allows businesses with large product catalogs to dynamically optimize assortments, potentially leading to significant revenue increases and operational efficiencies.
A new, more efficient AI technique is available for a class of NP-hard combinatorial optimization problems, moving towards broader application in real-time business operations.
- · E-commerce platforms
- · Retailers
- · AI/ML solution providers
- · Logistics companies
- · Traditional optimization software vendors
- · Businesses slow to adopt AI-driven optimization
Companies can optimize product offerings and pricing structures with greater speed and accuracy, directly impacting sales and profitability.
Enhanced optimization capabilities could lead to more competitive markets as businesses become more adept at matching supply with dynamic demand.
The widespread adoption of such AI models might further automate managerial decisions, reshaping roles in product management and strategic planning.
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Read at arXiv cs.LG