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Aydin Alptekinoglu
Researcher at Pennsylvania State University
Publications - 31
Citations - 1225
Aydin Alptekinoglu is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Mass customization & Competition (economics). The author has an hindex of 14, co-authored 29 publications receiving 1006 citations. Previous affiliations of Aydin Alptekinoglu include Saint Petersburg State University & College of Business Administration.
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The Benefits of Advance Booking Discount Programs: Model and Analysis
TL;DR: The benefits of the ABD program are evaluated, the optimal discount price that maximizes the retailer's expected profit is characterized, and the optimal cost-effective order placed is characterized.
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A model for analyzing multi-channel distribution systems
TL;DR: A model of a general multi-channel distribution system subject to stochastic demand is developed and a decomposition scheme is proposed that enables a near-optimal distribution policy with minimum total expected distribution cost to be developed.
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Mass Customization Versus Mass Production: Variety and Price Competition
TL;DR: The subgame-perfect Nash equilibrium in this three-stage game, allowing firm-specific fixed and variable costs that together characterize their production technology, is analyzed, finding that an MP facing competition from an MC offers lower product variety than an MP monopolist to reduce the intensity of price competition.
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Mass Customization vs. Mass Production: Variety and Price Competition
TL;DR: In this paper, the authors study competition between two multiproduct firms with distinct production technologies in a market where customers have heterogeneous preferences on a single taste attribute, and they find that an MP facing competition from an MC offers lower product variety than an MP monopolist to reduce the intensity of price competition.
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Learning Consumer Tastes Through Dynamic Assortments
TL;DR: It can be optimal for the firm to alternate between exploration and exploitation, and even offer assortments that lead to losses in the current period in order to gain information on consumer tastes, and a Bayesian conjugate model is developed that reduces the state space of the dynamic program and study value of learning using this conjugates.