Thousands of Illicit Massage Businesses (IMBs) are estimated to be operating in the United States by disguising themselves as legitimate establishments while exploiting trafficked workers, harming both the victims and the massage industry. The increasing digital presence of these illicit businesses presents an opportunity for detection, a crucial task for law enforcement and social service agencies aiming to disrupt their operations. Our research leverages user-generated business reviews from Yelp.com, enriched with data from multiple sources, including RubMaps.ch, U.S. Census records, GIS data, and licensing information. We present a feasibility study of developing a graph convolutional network (GCN) for a novel application and exploring its benefits and drawbacks in identifying IMBs. The novelty of our approach lies in its ability to link and analyze businesses, reviews, and reviewers within a heterogeneous network and employ a relational GCN to capture their complex relationships.

We model the hierarchical and decentralized nature of product transitions using a mixed-integer bilevel program with two followers, a manufacturing unit and an engineering unit. The leader, corporate management, seeks to maximize revenue over a finite planning horizon. The manufacturing unit uses factory capacity to satisfy the demand for current products. The demand for new products, however, cannot be fulfilled until the engineering unit completes their development, which, in turn, requires factory capacity for prototype fabrication. We model this interdependency between the engineering and manufacturing units as a generalized Nash equilibrium game at the lower level of the proposed bilevel model. We present a reformulation where the interdependency between the followers is resolved through the leader’s coordination, and we derive a solution method based on constraint and column generation. Our computational experiments show that the proposed approach can solve realistic instances to optimality in a reasonable time. We provide managerial insights into how the allocation of decision authority between corporate leadership and functional units affects the objective function performance. This paper presents the first exact solution algorithm to mixed-integer bilevel programs with interdependent followers, providing a flexible framework to study decentralized, hierarchical decision-making problems.

We develop an exact value function-based approach to solve a class of bilevel integer programs with stochastic right-hand sides. We first study structural properties and design two methods to efficiently construct the value function of a bilevel integer program. Most notably, we generalize the integer complementary slackness theorem to bilevel integer programs. We also show that the value function of a bilevel integer program can be characterized by its values on a set of so-called bilevel minimal vectors. We then solve the value function reformulation of the original bilevel integer program with stochastic right-hand sides using a branch-and-bound algorithm. We demonstrate the performance of our solution methods on a set of randomly generated instances. We also apply the proposed approach to a bilevel facility interdiction problem. Our computational experiments show that the proposed solution methods can efficiently optimize large-scale instances. The performance of our value function-based approach is relatively insensitive to the number of scenarios, but it is sensitive to the number of constraints with stochastic right-hand sides. Summary of Contribution: Bilevel integer programs arise in many different application areas of operations research including supply chain, energy, defense, and revenue management. This paper derives structural properties of the value functions of bilevel integer programs. Furthermore, it proposes exact solution algorithms for a class of bilevel integer programs with stochastic right-hand sides. These algorithms extend the applicability of bilevel integer programs to a larger set of decision-making problems under uncertainty.

Sepsis is a condition that progresses quickly and is a major cause of mortality in hospitalized patients. Data-driven diagnostic and therapeutic interventions are essential to ensure early diagnosis and appropriate care. The Sequential Organ Failure Assessment (SOFA) score is widely utilized in clinical practice to assess septic patients for organ dysfunction. The SOFA score uses points between 0 and 4 to quantify the level of dysfunction in six organ systems. These points are determined based on expert opinion and not informed by data, thus their usefulness can vary among different medical institutions depending on the targeted use. In this study, we propose multiple strategies to adjust the SOFA score using mixed-integer programming to improve the in-hospital mortality prediction of septic patients based on Electronic Health Records (EHRs). We use the same variables and threshold values of the original SOFA score in each strategy. Thus, the proposed approach takes advantage of optimization and data analysis while taking into account the medical expertise. Our results demonstrate a statistically significant improvement ( ) in the prediction of in-hospital mortality among patients susceptible to sepsis when implementing our proposed strategies. Area under the receiver operator curve (AUC) and accuracy values of 0.8928 and 0.8904 are achieved by optimizing the point values of the SOFA score.

In Multiplayer Online Battle Arena (MOBA) games, teams of players compete in combat to complete an objective and defeat the opposing team. To stay alive, players must closely monitor their character's status, especially remaining health. Understanding how health may change in the near future can be vital in determining what tactics a player may use. We analyzed replay logs of the game Defense of the Ancients 2 (DotA 2) to discover methods to predict how players' health evolves over time. For DotA 2, our results suggest that forecasting changes in a player's health can be done by viewing gameplay as two separate processes: normal gameplay flow in which changes in health are smaller and more regular, and less frequent but higher-impact events in which players experience larger changes in their health, such as team battles. We accomplished this by considering health data as two separate, but interleaved, time series in which separate processes govern low magnitude changes in health from high magnitude changes. In this paper, we present a value-split approach to predicting changes in health and describe the results of our approach using autoregressive moving-average models for low magnitude health changes and a combination of statistical models for the larger changes.

The most widely used progress measure for branch-and-bound (B&B) algorithms when solving mixed-integer programs (MIPs) is the MIP gap. We introduce a new progress measure that is often much smoother than the MIP gap. We propose a double exponential smoothing technique to predict the solution time of B&B algorithms and evaluate the prediction method using three MIP solvers. Our computational experiments show that accurate predictions of the solution time are possible, even in the early stages of B&B algorithms.