Associate Professor
Operations Management, Singapore Management University
Singapore
oboyabatli@smu.edu.sg
Assistant Professor of Industrial Engineering
University of Houston
Houston, TX
tlee6@uh.edu
Developing trust with my clients and co-workers has been central to my career success since completing my MASc degree. I claim that automating my analytics contributed a surprising amount to developing trust in my work by making it repeatable, audit-able and alterable. In this talk, I’ll speak about those three features of automated analytics and their link to trust in the context of project examples, both successes and failures.
In creating a new partnership (“syndicate”), what division of uncertain future profit should the parties select? We consider partnerships with no substantial initial investment and no moral hazard. Parties may differ in risk attitudes and beliefs. The common approach is bargaining. We take a different approach: One party proposes to the other a contract, similarly to the principal-agent approach.
Accounting for the adverse impact of "non-average" events has become essential in many applications involving decision making under uncertainty. Its implementation through decision models, namely stochastic programs, requires careful measurement of risk that reflects one's concern about uncertain outcomes. Important theories such as convex risk measures outline conditions required for risk measurement but provide little guidance for cases not meeting the conditions. Unfortunately, such cases are more than common in real-life situations.
Cutting and Packing problems are hard combinatorial optimization problems that arise in the context of several manufacturing and process industries or in their supply chains. These problems occur whenever a bigger object or space has to be divided into smaller objects or spaces, so that waste is minimized.
It is generally well accepted that your position in the social network affects your ability to get information. But how do the network positions of those with whom you interact, influence you? This issue is explored using high dimensional network data. Drawing on theories of social influence and the generalized other, social network analytic and text analytic methods, and data science techniques for big data a series of complex socio-technical situation are assessed.
In this talk I will present an overview of my research on chronic care services. I will then focus on the problem of care delivery for complex patients, with multiple comorbidities. In this project, we develop a Markov Decision Process framework to manage care for individual patients with multiple chronic conditions through a complex care hub. Complex care provision influences the evolution of Patient Activation Measure (PAM), an indicator for healthy behavior, which affects the evolution of health state of patients.
We present a framework for a class of sequential decision-making problems in the context of max-min bi-level programming, where a leader and a follower repeatedly interact. At each period, the leader allocates resources to disrupt the performance of the follower (e.g., as in defender-attacker or interdiction problems), who in turn minimizes some cost function over a set of activities that depends on the leader’s decision.
This paper investigates the impacts of two environmental policies: pollution abatement subsidy and emission tax, on a three-tier supply chain, where the manufacturer distributes via multiple competitive retailers and invests in a pollution abatement technology in manufacturing. The government pursues social welfare maximization, while the manufacturer and retailers are profit driven. We find that the subsidy policy offers the manufacturer greater incentives to abate pollution and yields higher profits for channel members.