About

Alan Scheller-Wolf is the Richard M. Cyert Professor of Operations Management and serves as the Head of the Ph.D. Program at the Tepper School of Business. His role involves leadership in academic and research activities within the field of operations management. The Tepper School of Business emphasizes a strategic vision to lead at the intersection of business, technology, and analytics, guided by its strategic plan Building The Intelligent Future, which focuses on AI for Business, Economic Prosperity, and Entrepreneurial Pursuit. As a faculty member, Alan Scheller-Wolf contributes to the school's mission of integrating data-informed, human-driven approaches to innovation and problem solving, aligning with the school's focus on thought leadership in artificial intelligence, machine learning, and management science.

Selected publications

• LookAhead: The Optimal Non-decreasing Index Policy for a Time-Varying Holding Cost problem

  arXiv (Cornell University) · 2026-01-13

  preprintOpen accessSenior author

  In practice, the cost of delaying a job can grow as the job waits. Such behavior is modeled by the Time-Varying Holding Cost (TVHC) problem, where each job's instantaneous holding cost increases with its current age (a job's age is the time since it arrived). The goal of the TVHC problem is to find a scheduling policy that minimizes the time-average total holding cost across all jobs. However, no optimality results are known for the TVHC problem outside of the asymptotic regime. In this paper, we study a simple yet still challenging special case: A two-class M/M/1 queue in which class 1 jobs incur a non-decreasing, time-varying holding cost and class 2 jobs incur a constant holding cost. Our main contribution is deriving the first optimal (non-decreasing) index policy for this special case of the TVHC problem. Our optimal policy, called LookAhead, stems from the following idea: Rather than considering each job's current holding cost when making scheduling decisions, we should look at their cost some $X$ time into the future, where this $X$ is intuitively called the ``lookahead amount." This paper derives that optimal lookahead amount.

  PublisherDOI

• LookAhead: the optimal non-decreasing index policy for a time-varying holding cost problem

  Queueing Systems · 2026-01-19

  articleOpen accessSenior author

  Abstract In practice, the cost of delaying a job can grow as the job waits. Such behavior is modeled by the time-varying holding cost (TVHC) problem, where each job’s instantaneous holding cost increases with its current age (a job’s age is the time since it arrived). The goal of the TVHC problem is to find a scheduling policy that minimizes the time-average total holding cost across all jobs. However, no optimality results are known for the TVHC problem outside of the asymptotic regime. In this paper, we study a simple yet still challenging special case: A two-class M/M/1 queue in which class 1 jobs incur a non-decreasing, time-varying holding cost and class 2 jobs incur a constant holding cost. Our main contribution is deriving the first optimal (non-decreasing) index policy for this special case of the TVHC problem. Our optimal policy, called LookAhead, stems from the following idea: Rather than considering each job’s current holding cost when making scheduling decisions, we should look at their cost some X time into the future, where this X is intuitively called the “lookahead amount." This paper derives that optimal lookahead amount.

  PublisherOA PDFDOI

• LookAhead: The Optimal Non-decreasing Index Policy for a Time-Varying Holding Cost problem

  ArXiv.org · 2026-01-13

  articleOpen accessSenior author

  In practice, the cost of delaying a job can grow as the job waits. Such behavior is modeled by the Time-Varying Holding Cost (TVHC) problem, where each job's instantaneous holding cost increases with its current age (a job's age is the time since it arrived). The goal of the TVHC problem is to find a scheduling policy that minimizes the time-average total holding cost across all jobs. However, no optimality results are known for the TVHC problem outside of the asymptotic regime. In this paper, we study a simple yet still challenging special case: A two-class M/M/1 queue in which class 1 jobs incur a non-decreasing, time-varying holding cost and class 2 jobs incur a constant holding cost. Our main contribution is deriving the first optimal (non-decreasing) index policy for this special case of the TVHC problem. Our optimal policy, called LookAhead, stems from the following idea: Rather than considering each job's current holding cost when making scheduling decisions, we should look at their cost some $X$ time into the future, where this $X$ is intuitively called the ``lookahead amount." This paper derives that optimal lookahead amount.

  PublisherOA PDF

• When does partial priority improve revenue?

  Queueing Systems · 2026-01-19

  articleOpen accessSenior author

  Abstract Priority queues have long been used to increase revenue by exploiting the fact that time-sensitive customers are willing to pay for shorter waiting times. This fact begs the question: Can one make even more revenue by relaxing the strictness of the priority policy? This paper answers this question under the unobservable queue setting, where customers are heterogeneous in their time-sensitivity; specifically the time-sensitivity of customers is allowed to follow an arbitrary distribution. In this paper, we prove necessary and sufficient conditions under which partial priority can increase the revenue. Specifically, we find a surprising result: Although partial priority offers much more flexibility than strict priority, partial priority only increases revenue if there are two additional constraints on the service provider, one setting a maximum price and the other setting a maximum waiting time. In the absence of either of these constraints, we prove that strict priority maximizes revenue. Finally, in situations where partial priority increases the revenue, we analytically characterize the amount of improvement.

  PublisherOA PDFDOI

• Split Liver Transplantation: An Analytical Decision Support Model

  Operations Research · 2025-04-17 · 1 citations

  article

  This research study introduces a decision support model for split liver transplantation (SLT). SLT is a procedure that can save two lives with one donated liver, thereby increasing the total benefit derived from the limited number of donated livers available. SLT may also improve equity by giving transplant candidates who are physically smaller, including children, increased access to liver transplants. However, SLT is rarely used in the United States. To help quantify the benefits of increased SLT utilization and provide decision support tools, this research presents a deceased-donor liver allocation model that incorporates both efficiency and fairness objectives. We formulate the liver waitlists as a multiqueue fluid system, incorporating specifics of donor-recipient size matching and patients’ dynamically changing health conditions. Leveraging a novel decomposition result, the study finds the exact optimal matching procedure, enabling policy makers to benchmark the performance of different allocation policies against the theoretical optimal. Numerical results, utilizing data from the Organ Procurement and Transplantation Network, show that increased utilization of SLT can significantly reduce patient deaths, increase total quality-adjusted life years, and improve fairness among different patient groups.

  PublisherDOI

• Multi-Armed Bandits with Endogenous Learning Curves: An Application to Split Liver Transplantation

  Manufacturing & Service Operations Management · 2025-02-06 · 2 citations

  article

  Problem Definition: Proficiency in many sophisticated tasks is attained through experience-based learning, in other words, learning by doing. For example, transplant centers’ surgical teams need to practice difficult surgeries to master the skills required. Meanwhile, this experience-based learning may affect other stakeholders, such as patients eligible for transplant surgeries, and require resources, including scarce organs and continual efforts. To ensure that patients have excellent outcomes and equitable access to organs, the organ allocation authority needs to quickly identify and develop medical teams with high aptitudes. This entails striking a balance between exploring surgical combinations with initially unknown full potential and exploiting existing knowledge based on observed outcomes. Methodology/results: We formulate a multi-armed bandit (MAB) model in which parametric learning curves are embedded in the reward functions to capture endogenous experience-based learning. In addition, our model includes provisions ensuring that the choices of arms are subject to fairness constraints to guarantee equity. To solve our MAB problem, we propose the L-UCB and FL-UCB algorithms, variants of the upper confidence bound (UCB) algorithm that attain the optimal [Formula: see text] regret on problems enhanced with experience-based learning and fairness concerns. We demonstrate our model and algorithms on the split liver transplantation (SLT) allocation problem, showing that our algorithms have superior numerical performance compared with standard bandit algorithms in a setting where experience-based learning and fairness concerns exist. Managerial implications: From a methodological point of view, our proposed MAB model and algorithms are generic and have broad application prospects. From an application standpoint, our algorithms could be applied to help evaluate potential strategies to increase the proliferation of SLT and other technically difficult procedures. Funding: The authors acknowledge the support of CMU Tepper’s Health Care Initiative Funding. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/msom.2022.0412 .

  PublisherDOI

• Improving Upon the generalized c-mu rule: a Whittle approach

  ACM SIGMETRICS Performance Evaluation Review · 2025-08-26 · 1 citations

  articleSenior author

  Scheduling a stream of jobs whose holding cost changes over time is a classic and practical problem. Specifically, each job is associated with a holding cost (penalty), where a job's instantaneous holding cost is some increasing function of its current age (the time it has spent in the system since its arrival) and its class. The goal is to schedule the jobs to minimize the time-average total holding cost across all jobs.

  PublisherDOI

• Improving Upon the generalized c-mu rule: a Whittle approach

  ArXiv.org · 2025-04-14

  preprintOpen accessSenior author

  Scheduling a stream of jobs whose holding cost changes over time is a classic and practical problem. Specifically, each job is associated with a holding cost (penalty), where a job's instantaneous holding cost is some increasing function of its class and current age (the time it has spent in the system since its arrival). The goal is to schedule the jobs to minimize the time-average total holding cost across all jobs. The seminal paper on this problem, by Van Mieghem in 1995, introduced the generalized c-mu rule for scheduling jobs. Since then, this problem has attracted significant interest but remains challenging due to the absence of a finite-dimensional state space formulation. Consequently, subsequent works focus on more tractable versions of this problem. This paper returns to the original problem, deriving a heuristic that empirically improves upon the generalized c-mu rule and all existing heuristics. Our approach is to first translate the holding cost minimization problem to a novel Restless Multi-Armed Bandit (R-MAB) problem with a finite number of arms. Based on our R-MAB, we derive a novel Whittle Index policy, which is both elegant and intuitive.

  PublisherOA PDFDOI

• Child Welfare Services in the United States: An Operations Research Perspective

  Springer series in supply chain management · 2025-01-01

  book-chapter
  PublisherDOI

• Greenness and its Discontents: Operational Implications of Investor Pressure

  SSRN Electronic Journal · 2024-01-01

  articleOpen access
  PublisherDOI