arXiv subjects

Emily L. Tucker

Publications and source records attributed to Emily L. Tucker.

10 recordsLinked to original sources

arXiv2026

When Shortages Lead to Export Restrictions: A Computational Study

Globalization has enabled companies to leverage cost efficiencies; however, it has increased exposure to disruption risks that threaten supply stability. Among these, export bans have emerged as a systemic challenge, often arising as a secondary effect of conventional supply capacity disruptions. The pharmaceutical industry is particularly vulnerable to bans because of the criticality of keeping life-saving medications available, yet such disruptions also jeopardize patient health globally as well as challenge company operations. This paper proposes a supply chain design model that incorporates the relationship between conventional disruption risks (quality-related capacity failures and natural disasters) and export bans that may be induced when the drug is projected to be in short supply. The model is a two-stage stochastic mixed integer program, where the systemic setting yields a second-stage problem with one binary variable and continuous variables. The structure motivates the study of three tailored solution methods: the Alternating Integer L-shaped Cut, Alternating Disjunctive Cut, and Alternating Bilinear Cut methods. In the nearly-continuous second stage context, the Alternating Integer L-shaped cuts perform best. Using an oncology drug case study, we study effects of systemic, induced risk and quantify the value of incorporating disruptions into supply chain design.

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arXiv2025

A Non-Variational Quantum Approach to the Job Shop Scheduling Problem

Quantum heuristics offer a potential advantage for combinatorial optimization but are constrained by near-term hardware limitations. We introduce Iterative-QAOA, a variant of QAOA designed to mitigate these constraints. The algorithm combines a non-variational, shallow-depth circuit approach using fixed-parameter schedules with an iterative warm-starting process. We benchmark the algorithm on Just-in-Time Job Shop Scheduling Problem (JIT-JSSP) instances on IonQ Forte Generation QPUs, representing some of the largest such problems ever executed on quantum hardware. We compare the performance of the algorithm against both the Variational Quantum Imaginary Time Evolution (VarQITE) algorithm and the non-variational Linear Ramp (LR) QAOA algorithm. We find that Iterative-QAOA robustly converges to find optimal solutions as well as high-quality, near-optimal solutions for all problem instances evaluated. We evaluate the algorithm on larger problem instances up to 97 qubits using tensor network simulations. The scaling behavior of the algorithm indicates potential for solving industrial-scale problems on fault-tolerant quantum computers.

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arXiv2025

A Gateway to Quantum Computing for Industrial Engineering

Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers navigate the emerging field of quantum operations research, we provide a road map of the current field of quantum operations research. We introduce the foundational principles of quantum computing, outline the current hardware and software landscape, and survey major algorithmic advances relevant to IE/OR, including quantum approaches to linear algebra, optimization, machine learning, and stochastic simulation. We then highlight applied research directions, including the importance of problem domains for driving long-term value of quantum computers and how existing classical OR models can be reformulated for quantum hardware. Recognizing the steep learning curve, we propose pathways for IE/OR researchers to develop technical fluency and engage in this interdisciplinary domain. By bridging theory with application, and emphasizing the interplay between hardware and research development, we argue that industrial engineers are uniquely positioned to shape the trajectory of quantum computing for practical problem-solving. Ultimately, we aim to lower the barrier to entry into quantum computing, motivate new collaborations, and chart future directions where quantum technologies may deliver tangible impact for industry and academia.

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arXiv2024

Effects of Geopolitical Strain on Global Pharmaceutical Supply Chain Design and Drug Shortages

Emerging geopolitical risks have begun to threaten global supply chains, including those that produce life-saving drugs. Export bans may prevent a company from shipping products internationally, and it is unclear how these new dynamics may affect company plans and persistent, worldwide drug shortages. To address these questions, we present a global pharmaceutical supply chain design model that considers the risk of export bans that are induced by supplier capacity disruptions and corresponding price increases. The model takes the company's perspective as a decision-maker looking to locate plants and distribute drugs globally. It is a two-stage stochastic program that includes uncertainty in capacity, ability-to-export, and demand. The model is solved by integrating the Sample Average Approximation and L-shaped methods. We present conditions related to when demand will be met and a case study of a generic oncology drug. We find that preparing for geopolitical strain may increase resilience and profits as well as reduce shortages in the short term. At baseline, expected global shortages are high (17.2%) with disparities across country income levels (0.3%, 0.8%, 87.2%, and 87.6% for high, upper-middle, lower-middle, and low income countries, respectively). Pricing policies may improve drug access overall, back-shoring may slightly improve access for the country where it is implemented, and bilateral alliances may not be effective at improving access.

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arXiv2024

Developing Optimization Models with Cognitive Systems Engineering

One goal of applied operations research is to improve decisions in practice. This requires modelers and stakeholders to have a shared understanding of the system and for the developed model to reflect the system's core dynamics. There are four areas to address: the underlying problem must be understood, the mathematical formulation of the problem must be representative of the system at hand, the data must be appropriate, and the model-generated recommendations must be understandable by the stakeholders. While developing models, operations researchers may primarily rely on past experience in model development, rather than underlying theory, to guide decisions on how to include stakeholders in the modeling process. In parallel, the field of Cognitive Systems Engineering has developed methodologies and practices to understand systems, stakeholder needs, and environments. To improve the rigor of the "application" in applied operations research, we present a framework to integrate Cognitive Systems Engineering methods with optimization model development. We apply the integrated framework to a case study of locating hand sanitizer stations in response to COVID-19 at a large academic institution.

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arXiv2024

An Optimization Approach to Improve Equitable Access to Local Parks

Local parks are public resources that promote human and environmental welfare. Unfortunately, park inequities are commonplace as historically marginalized groups may have insufficient access. Platforms exist to identify the geographical areas that would benefit from future park improvements. However, these platforms do not optimize decisions nor include key features, such as budget and infrastructure, that are relevant to park location decisions. To support recreational and government agencies in addressing inequities in the distribution and quality of parks, we propose a mixed-integer program that minimizes insufficient access, defined as weighted deviations across multiple categories (distance, capacity, and environmental features). We consider an equity-focused min-max objective and an overall objective to minimize total weighted deviations. We apply the model to a case study of Asheville, North Carolina. We conduct extensive data collection to parameterize the model. In policy analyses, we consider the effects of available budget, planning horizons, strategic demographic priorities, and thresholds of access. The model reflects user-defined criteria and goals, and the results suggest that the framework may be generalizable to other cities. This study serves as a step in the development and incorporation of mathematical modeling to achieve social goals within the recreational setting.

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arXiv2023

Surrogate "Level-Based" Lagrangian Relaxation for Mixed-Integer Linear Programming

Mixed-Integer Linear Programming (MILP) plays an important role across a range of scientific disciplines and within areas of strategic importance to society. The MILP problems, however, suffer from combinatorial complexity. Because of integer decision variables, as the problem size increases, the number of possible solutions increases super-linearly thereby leading to a drastic increase in the computational effort. To efficiently solve MILP problems, a "price-based" decomposition and coordination approach is developed to exploit 1. the super-linear reduction of complexity upon the decomposition and 2. the geometric convergence potential inherent to Polyak's stepsizing formula for the fastest coordination possible to obtain near-optimal solutions in a computationally efficient manner. Unlike all previous methods to set stepsizes heuristically by adjusting hyperparameters, the key novel way to obtain stepsizes is purely decision-based: a novel "auxiliary" constraint satisfaction problem is solved, from which the appropriate stepsizes are inferred. Testing results for large-scale Generalized Assignment Problems (GAP) demonstrate that for the majority of instances, certifiably optimal solutions are obtained. For stochastic job-shop scheduling as well as for pharmaceutical scheduling, computational results demonstrate the two orders of magnitude speedup as compared to Branch-and-Cut (B&C). The new method has a major impact on the efficient resolution of complex Mixed-Integer Programming (MIP) problems arising within a variety of scientific fields.

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arXiv2022

A Framework for Operations Research Model Use in Resilience to Fundamental Surprise Events: Observations from University Operations during COVID-19

Operations research (OR) approaches have been increasingly applied to model the resilience of a system to surprise events. In order to model a surprise event, one must have an understanding of its characteristics, which then become parameters, decisions, and/or constraints in the resulting model. This means that these models cannot (directly) handle fundamental surprise events, which are events that could not be defined before they happen. However, OR models may be adapted, improvised, or created during a fundamental surprise event, such as the COVID-19 pandemic, to help respond to it. We provide a framework for how OR models were applied by a university in response to the pandemic, thus helping to understand the role of OR models during fundamental surprise events. Our framework includes the following adaptations: adapting data, adding constraints, model switching, pulling from the modeling toolkit, and creating a new model. Each of these adaptations is formally presented, with supporting evidence gathered through interviews with modelers and users involved in the university response to the pandemic. We discuss the implications of this framework for both OR and resilience.

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arXiv2022

Stochastic Optimization Models for Location and Inventory Prepositioning of Disaster Relief Supplies

We consider the problem of preparing for a disaster season by determining where to open warehouses and how much relief item inventory to preposition in each. Then, after each disaster, prepositioned items are distributed to demand nodes during the post-disaster phase, and additional items are procured and distributed as needed. There is often uncertainty in the disaster level, affected areas locations, the demand for relief items, the usable fraction of prepositioned items post-disaster, procurement quantity, and arc capacity. To address uncertainty, we propose and analyze two-stage stochastic programming (SP) and distributionally robust optimization (DRO) models, assuming known and unknown (ambiguous) uncertainty distributions. The first and second stages correspond to pre- and post-disaster phases, respectively. We also propose a model that minimizes the trade-off between considering distributional ambiguity and following distributional belief. We obtain near-optimal solutions of our SP model using sample average approximation and propose a computationally efficient decomposition algorithm to solve our DRO models. We conduct extensive experiments using a hurricane season and an earthquake as case studies to investigate these approaches' computational and operational performance.

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arXiv2022

Pharmaceutical Supply Chain Reliability and Effects on Drug Shortages

Drug shortages occur frequently and are often caused by supply chain disruptions. For improvements to occur, it is necessary to be able to estimate the vulnerability of pharmaceutical supply chains. In this work, we present the first model of pharmaceutical supply chain reliability. We consider three key approaches that companies may use to improve reliability: configuration, risk of disruptions, and speed of recovery. Key metrics include expected drug shortages, average time-to-shortage, and average time-to-recovery. We parametrize the model using data from major drug shortage databases and a case example of a generic injectable oncology drug. With a lean supply chain configuration, we observe that expected shortages at status quo conditions are 10%. By either doubling the speed of recovery or halving the disruption rate, expected shortages could drop to 5%. The most influential single change would be to add a back-up supplier to a lean configuration, leading to expected shortages of 4%. We also consider profitability and present the breakeven prices for different configurations. The results from our analyses could lead to immediate policy impact, providing evidence of the benefits of redundancy and improving facility quality.

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