Decision Sciences Theses and Dissertations

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Browsing Decision Sciences Theses and Dissertations by Author "Pangburn, Michael"

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    CONSUMPTION PREFERENCES, TIME AND UNCERTAINTY: IMPACTS ON RETAIL PRICING TACTICS
    (University of Oregon, 2017-09-06) Jalili, Monire; Pangburn, Michael
    My dissertation is a collection of three essays with analytical models at the interface of marketing and operations with a focus on pricing. The common theme in this dissertation is studying the effect of the consumer-driven demand on the optimal operational decisions of a single firm. This dissertation includes co-authored material. In my first essay, I study the role of consumers' opposing perceptions of green quality on the optimal product line decisions, i.e., products, prices and quality by analyzing the firm's optimization problem and incorporating an endogenous demand model that emerges from the consumers' preferences while considering the cost implications of introducing a green product. My second essay is on optimal timing of price discounts. Delaying discounts, i.e., giving discounts on future spending based on current spending is a prevalent retail discounting practice. For a market of rational and forward-looking consumers who repeatedly visit and purchase with the firm, we analyze the relative efficacy of delayed credits vs. a natural alternative of immediate discounts. In my third essay, I explore a firm's optimal pricing strategy when it simultaneously rents and sells a product for which consumers have a priori valuation uncertainty.
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    ESSAYS ON SUSTAINABLE SUPPLY CHAIN, GROUP DECISION MAKING AND EXPERT-AUGMENTED FEATURE SELECTION
    (University of Oregon, 2022-10-04) Rabiee, Meysam; Pangburn, Michael
    My dissertation consists of an essay in sustainable supply chain management, an essay in group decision making, and an essay in expert-augmented feature selection. My first essay is an unpublished work co-authored with Prof. Nagesh Murthy and Dr. Hossein Rikhtehgar Berenji. In some supply chains, it is extraordinarily expensive for a buyer to audit all selected suppliers to guarantee compliance with the buyer's code of conduct for social and environmental responsibility. In this work, we provide insight to help such a buyer profit from judicious audits, despite the risk of revenue loss due to non-compliance. My second essay is co-authored with Babak Aslani and Dr. Jafar Rezaei; it has been published. The purpose of this article is to investigate the detection and handling of biased decision-makers in group decision-making processes. To address this issue, we developed three algorithms including extreme, moderate, and soft versions. The third essay is an unpublished work co-authored with Mohsen Mirhashemi, Prof. Michael Pangburn, Prof. Dursun Delen, and Dr. Saeed Piri. In this study, we enrich the conventional feature-selection method by incorporating the opinions of experts on the features, a technique we refer to as expert-augmented feature selection. To reflect the trade-off between explainability and prediction accuracy, we develop two very similar models: one for classification problems, and one for regression problems. Finally, we develop a posterior ensemble approach for quantifying each feature's accuracy-contribution degree. This algorithm's output helps us to discover features that are consistently rated, under-rated, or over-rated by experts.