ESSAYS ON SUSTAINABLE SUPPLY CHAIN, GROUP DECISION MAKING AND EXPERT-AUGMENTED FEATURE SELECTION
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Date
2022-10-04
Authors
Rabiee, Meysam
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Journal ISSN
Volume Title
Publisher
University of Oregon
Abstract
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 My first essay is an unpublished work co-authored with Prof.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The third essay is an unpublished work co-authored with Mohsen Mirhashemi, Prof.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.
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Keywords
Bias, Expert-augmented, Group Decision Making, Supervised Feature Selection, Sustainability