As an emerging technology, human-robot collaboration (HRC) has been implemented to enhance the performance of assembly lines and improve the safety of human workers. By integrating the advantages of human workers and collaborative robots (cobots), HRC enables production systems to process tasks consecutively, concurrently, or collaboratively. However, the introduction of cobots also makes the corresponding human-robot collaborative assembly line balancing problem more complex and difficult to solve. To solve this problem, we first propose an enhanced mixed integer program (EMIP) with various enhancement techniques and tighter bounds, and then, we develop an improved combinatorial Benders decomposition algorithm (Algorithm ICBD) with new local search strategies, Benders cuts, and acceleration procedures. To verify the effectiveness of our proposed model and algorithms, we conduct extensive computational experiments, and the results show that our proposed EMIP model is significantly better than the existing mixed integer program model; the percentages of instances that can obtain feasible and optimal solutions are increased from 82.42% to 100% and from 29.17% to 43.5%, respectively, whereas the average gap is decreased from 19.81% to 5.64%. In addition, our proposed Algorithm ICBD can get 100% of feasible solutions and 65.92% of optimal solutions for all of the test instances, and the average gap is only 1.49%. Moreover, compared with existing Benders decomposition methods for this problem, our approach yields comparatively better solutions in notably shorter average computational time when run in the same computational environment. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research was supported by the National Natural Science Foundation Council of China [Grants 72401214, 92167206, 7221101377, 72471169, and 72231005], the Ministry of Education of China [Grant 24YJC630078], and Computation and Analytics of Complex Management Systems (Tianjin University). This research was also supported by the Tianjin Natural Science Foundation Project [Grant 23JCQNJC01900] and the Tianjin Philosophy and Social Science Planning Project [Grant TJGL21-016]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0279 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0279 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

In credit fraud detection practice, certain fraudulent transactions often evade detection because of the hidden nature of fraudulent behavior. To address this issue, an increasing number of positive-unlabeled (PU) learning techniques have been employed by more and more financial institutions. However, most of these methods are designed for single data sets and do not take into account the heterogeneity of data when they are collected from different sources. In this paper, we propose an integrative PU learning method (I-PU) for pooling information from multiple heterogeneous PU data sets. A novel approach that penalizes group differences is developed to explicitly and automatically identify the cluster structures of coefficients across different data sets, thus offering a plausible interpretation of heterogeneity. Furthermore, we apply a bilevel selection method to detect the sparse structure at both the group level and within-group level. Theoretically, we show that our proposed estimator has the oracle property. Computationally, we design an expectation-maximization (EM) algorithm framework and propose an alternating direction method of multipliers (ADMM) algorithm to solve it. Simulation results show that our proposed method has better numerical performance in terms of variable selection, parameter estimation, and prediction ability. Finally, a real-world application showcases the effectiveness of our method in identifying distinct coefficient clusters and its superior prediction performance compared with direct data merging or separate modeling. This result also offers valuable insights for financial institutions in developing targeted fraud detection systems. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72071169, 72231005, 72233002, and 72471169], the Fundamental Research Funds for the Central Universities of China [Grant 20720231060], the National Social Science Fund of China [Grant 21&ZD146], and Shuimu Tsinghua Scholar Program. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0366 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0366 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Patients with motor control difficulties often "type" on a computer using a switch keyboard to guide a scanning cursor to text elements. We show how to optimize some parts of the design of switch keyboards by casting the design problem as mixed integer programming. A new algorithm to find an optimized design solution is approximately 3600 times faster than a previous algorithm, which was also susceptible to finding a non-optimal solution. The optimization requires a model of the probability of an entry error, and we show how to build such a model from experimental data. Example optimized keyboards are demonstrated.