The discrete two-dimensional assortment problem
Operations Research
Single-Period Multiproduct Inventory Models with Substitution
Operations Research
Management of Multi-Item Retail Inventory Systems with Demand Substitution
Operations Research
Stocking Retail Assortments Under Dynamic Consumer Substitution
Operations Research
A Modeling Framework for Category Assortment Planning
Manufacturing & Service Operations Management
Centralized and Competitive Inventory Models with Demand Substitution
Operations Research
Assortment Planning and Inventory Decisions Under a Locational Choice Model
Management Science
Product Line Selection and Pricing with Modularity in Design
Manufacturing & Service Operations Management
Retail Assortment Planning in the Presence of Consumer Search
Manufacturing & Service Operations Management
Inventory Models for Substitutable Products: Optimal Policies and Heuristics
Management Science
Manufacturing & Service Operations Management
Optimal Dynamic Assortment Planning with Demand Learning
Manufacturing & Service Operations Management
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We present an efficient dynamic programming algorithm to determine the optimal assortment and inventory levels in a single-period problem with stockout-based substitution. In our model, total customer demand is random and comprises fixed proportion of customers of different types. Customer preferences are modeled through the definition of these types. Each customer type corresponds to a specific preference ordering among products. A customer purchases the highest-ranked product, according to his type (if any), that is available at the time of his visit to the store (stockout-based substitution). We solve the optimal assortment problem using a dynamic programming formulation. We establish structural properties of the value function of the dynamic program that, in particular, help to characterize multiple local maxima. We use the properties of the optima to solve the problem in pseudopolynomial time. Our algorithm also gives a heuristic for the general case, i.e., when the proportion of customers of each type is random. In numerical tests, this heuristic performs better and faster than previously known methods, especially when the mean demand is large, the degree of substitutability is high, the population is homogeneous, or prices and/or costs vary across products.