Assortment Optimization under Customer-driven Substitution
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Park, Jisoo
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Abstract
Retailers face growing complexity in assortment planning due to the rise of omnichannel retailing, the proliferation of product lines, and increasing uncertainty in supply chain operations. Motivated by these challenges, this dissertation focuses on two distinct problem contexts. The first is the transformation of offline stores into experiential showrooms, which requires new models for designing in-store assortments that shape customer purchase decisions beyond immediate product availability. The second is the need to integrate assortment decisions with upstream supply availability and production capacity constraints, as well as downstream fulfillment dynamics, to maintain responsiveness under volatile and uncertain operating conditions.
Customer substitution behavior—the propensity of customers to select alternative products when preferred items are unavailable—serves as a critical lever in both contexts, enabling retailers to better align supply with demand. However, substitution behavior is often simplified or underrepresented in supply chain optimization models. This dissertation addresses this gap by introducing a new form of substitution behavior in a novel retail context and developing frameworks that explicitly incorporate customer-driven substitution effects into assortment planning through optimization- and simulation-based decision-support models. These models are applied to proprietary industry data and validated through realistic case studies.
The first module of the dissertation (Chapter 2) introduces the showroom assortment optimization problem, a novel approach for showcasing optimization in omnichannel retail networks in which offline stores operate as experiential showrooms rather than traditional fulfillment centers. In this setting, customers interact with products in-store before making online purchase decisions, and substitution behavior is shaped by in-store experiences rather than immediate product availability. We define the showcasing optimization problem as selecting the assortment of products that maximizes the purchase confidence of an average customer following a store visit while adhering to capacity constraints. We formulate the underlying optimization problem as a mixed-integer program (MIP) that captures how customers gain purchase confidence through surrogate products—items that represent or stand in for other products—thereby modeling the customer substitution effect in the showroom environment. Using data from an industry partner, we demonstrate the practical applicability of our model in quantitatively designing effective showcasing strategies to improve customer purchase confidence and stimulate sales.
The second module (Chapters 3–4) addresses assortment planning under supply and demand uncertainty for a make-to-stock manufacturer–retailer operating in capacity-constrained production and fulfillment settings. Chapter 3 examines the profit-maximizing multi-period assortment planning problem. We develop a stochastic choice-based optimization model that endogenizes customer substitution behavior through a rank-based choice model with small consideration sets, enabling computationally tractable approximations of stockout-driven substitution probabilities. This model extends traditional assortment planning by explicitly linking assortment decisions with upstream supply availability and production capacity constraints, as well as downstream fulfillment performance. To solve realistically sized instances, we employ a rolling-horizon framework with multi-period lookahead and a two-stage stochastic program, where a Benders decomposition separates master assortment and sourcing decisions from scenario-specific subproblems that allocate production, inventory, and fulfillment over time. Case study results with a North America–based industry partner demonstrate substantial improvements in profit and service levels compared to baseline static or myopic tactical plans, with benefits amplified under volatile supply and demand conditions.
Chapter 4 develops a hybrid simulation–optimization framework to refine tactical assortments through operational adjustments at finer review intervals. Tactical plans, set during the sales and operations planning process, fix supplier and material commitments but allow limited assortment changes within operational review periods. The framework integrates a mixed-integer linear program (MILP) with a high-fidelity multi-agent system (MAS) that simulates detailed operational dynamics, including stochastic demand, inventory flows across multiple echelons, and demand fulfillment processes. In each review period, the MILP proposes state-contingent adjustments, which the MAS evaluates under realistic operating conditions. A feedback-driven neighborhood search iteratively improves solutions to ensure both operational feasibility and profitability. This matheuristic approach enables alignment between tactical assortment planning and operational execution, capturing stochastic and behavioral effects not fully represented in optimization models. Case study experiments show measurable gains in profit and service levels over static tactical plans, underscoring the importance of operationally informed assortment adjustments.
Collectively, this work advances the integration of customer choice modeling with assortment planning across diverse retail contexts. In the showroom setting, the proposed models capture how surrogate products influence in-person purchase confidence, enabling retailers to design in-store assortments that shape customer decisions beyond immediate product availability. In operational supply chain environments, the frameworks integrate substitution-driven demand modeling with capacity, procurement, and fulfillment constraints to hedge against uncertainty, improve demand fulfillment, and enhance resilience. These contributions demonstrate how substitution can serve as a unifying lever to align customer behavior with supply and operational realities. The models offer actionable guidance for retailers seeking to design assortments that balance profitability, customer experience, and operational robustness in increasingly complex and volatile environments.
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Date
2025-12
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Dissertation (PhD)