PhD Oral Exam - Basak Tozlu, Industrial Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Every day, people make decisions that involve multiple, often conflicting, criteria. The complexity of these decision-making problems arises from the need to balance competing factors such as fairness, efficiency, and individual preferences. This thesis focuses on a particular multi-criteria decision-making (MCDM) problem called two-sided matching (TSM). In TSM systems, two distinct sets of agents—such as employers and job seekers, schools and students, or donors and recipients—seek to be matched with members of the other set. Because these systems frequently involve human participants, they are significantly influenced by human factors. Therefore, the main challenge in matching lies in the selection of appropriate criteria to lead to fair, stable and effective decisions. Traditionally criteria have been determined by expert opinions, surveys, or historical data. However, these methods may be biased, cannot escape from past inequalities and fail to capture the complete criteria. To address this limitation, this thesis proposes a novel, systematic, and unbiased methodology for identifying matching criteria in TSM. The methodology developed based on environment-based design (EBD), environment-based life cycle analysis (eLCA) and recursive object model (ROM) ensures that the criteria are identified objectively and inclusively. A key contribution of this work is the ability to extract decision-making criteria directly from natural language descriptions of the matching problem. This allows for adaptive, domain-independent, and data-driven criteria formulation, eliminating the need for subjective inputs. To demonstrate the effectiveness of the proposed method, the thesis applies it to both static and dynamic matching scenarios. Static matching refers to problems involving fixed sets of participants, while dynamic matching accounts for changing sets, such as time-based arrivals. For the static case, an assignment-based optimization model is used to generate matches based on the identified criteria. For the dynamic case, a perishable capacity management optimization model is adopted, allowing the system to handle uncertainty and last-minute changes effectively. By bringing together unbiased criteria identification with customized optimization models, this research advances the field of operations research in two important ways: it strengthens the fairness and transparency of matching systems and refines how MCDM problems are formulated for optimization. The methodology developed in this thesis is highly adaptable, with applications across diverse domains such as labour markets, school admissions, and resource allocation. At its core, this work lays the groundwork for more equitable and human-centered decision-making processes, addressing a fundamental challenge in two-sided matching: ensuring that criteria are fair, inclusive, and free from bias.