Article
Engineering, Civil
Jiateng Yin, Xianliang Ren, Shuai Su, Fei Yan, Tang Tao
Summary: Metro managers have shifted their attention from prevention to recovery and resilience of urban rail systems, due to disruptions caused by natural disasters. This paper proposes a train rescheduling framework that helps the rail transit system recover quickly from disruptions, using pre-allocated rolling stocks and timetable rescheduling. A mixed-integer linear programming model is formulated to maximize the resilience of the urban rail line, and a branch-and-cut algorithm is developed to solve it. Numerical experiments based on real-world data of Beijing metro validate the effectiveness of the approach.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Mathematics
Rafael L. Patrao, Reinaldo C. Garcia, Joao M. da Silva
Summary: The increasing urban population worldwide puts pressure on public services, especially healthcare. This study proposes an integer linear programming model to improve the efficiency of surgical centers and reduce the waiting time for surgeries. The model considers case-mix planning and master surgical scheduling problems. Promising results were obtained using data from a hospital in Turin, Italy, showing a significant reduction in the surgical waiting list.
Article
Economics
Shuguang Zhan, Pengling Wang, S. C. Wong, S. M. Lo
Summary: Disruptions in daily train operations can lead to deviation from schedules, making efficient rescheduling critical. This study utilizes a space-time-speed network to embed energy-efficient train speed profiles. A multiple-phase optimal control model and integer linear programming model are used to solve the rescheduling problem.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Computer Science, Information Systems
Sairong Peng, Xin Yang, Shuxin Ding, Jianjun Wu, Huijun Sun
Summary: This paper addresses the problem of rescheduling trains with speed management in high-speed railway systems under uncertain disruptions. A mixed-integer linear programming model is formulated to optimize train traveling times and passenger comfort. A rolling horizon algorithm is applied to adapt to real-time disruptions and adjust rescheduling and speed control strategies accordingly. Numerical experiments based on the Beijing-Tianjin intercity high-speed railway line demonstrate the effectiveness and efficiency of the proposed approach.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jiahui Qian, Zhijing Zhang, Lingling Shi, Dan Song
Summary: This paper proposes a novel assembly timing planning method based on knowledge and mixed integer linear programming. By constructing a knowledge base and adopting a group planning strategy, the assembly timing planning for automatic assembly system is achieved. The proposed method significantly reduces assembly time, improves assembly efficiency, and provides guidance for assembly process design through the developed software for timing planning visualization.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Thermodynamics
Miguel Gonzalez-Salazar, Julia Klossek, Pascal Dubucq, Thomas Punde
Summary: Long-term portfolio optimization for district heating systems is challenging due to the need for high accuracy and computational speed. This paper investigates the advantages and disadvantages of using merit order (MO) models compared to mixed integer linear programming (MILP) models. Results suggest that MO models, especially those incorporating heat storage and detailed description of CHP plants, can significantly reduce computation time without sacrificing accuracy. Combining MO and MILP models offers a faster and more robust decision-making process.
Article
Transportation Science & Technology
Xin Hong, Lingyun Meng, Andrea D'Ariano, Lucas P. Veelenturf, Sihui Long, Francesco Corman
Summary: This paper addresses the train rescheduling problem in the event of large disruptions, proposing a novel mixed-integer linear programming formulation to optimize passenger reassignment strategies and minimize train delays. Numerical experiments based on the Beijing-Shanghai high-speed railway line validate the effectiveness of the model in achieving real-time traffic efficiency.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Management
Anne Zander, Stefan Nickel, Peter Vanberkel
Summary: This article focuses on balancing supply and demand for physicians and panel patients on a tactical level to ensure a manageable workload for the physician and access to care for patients. The proposed deterministic integer linear programs aim to minimize the deviation between expected panel workload and physician's capacity over time, considering future panel development. Through experiments with real-world data, the study shows that detailed classification of new patients can significantly reduce the expected differences between workload and capacity.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Thermodynamics
Karl Vilen, Erik O. Ahlgren
Summary: Most computer models used in energy systems optimization modeling studies are constructed using linear equations. However, linear equations may not adequately reflect real-world conditions and are less suitable for representing individual-scale technologies in local system studies. This study investigates the differences in heating solutions and model solution times for a local expanding heating system. The results show that the use of district heating is higher for cost structures that use mixed integer linear programming. On the other hand, the solution time is significantly shorter for linear formulations compared to mixed integer linear formulations.
Article
Medicine, General & Internal
Adrienne Mann, Ami N. Shah, Pari Shah Thibodeau, Liselotte Dyrbye, Adnan Syed, Maria A. Woodward, Kerri Thurmon, Christine D. Jones, Kimiko S. Dunbar, Tyra Fainstad
Summary: This study is a randomized clinical trial that examines the effects of professional coaching on improving well-being and reducing symptoms of burnout in women physician trainees. The results show that the intervention group experienced decreased emotional exhaustion, depersonalization, impostor syndrome, and moral injury, as well as increased self-compassion and flourishing.
Article
Management
Mirko Dahlbeck
Summary: The article introduces a new facility layout problem, TRFLP, aiming to minimize center-to-center distances with non-overlapping department assignments. A mixed-integer linear programming approach is used, successfully solving instances with up to 18 departments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Xudong Wang, Zeyu Liu, Xueping Li
Summary: The past decade has seen the rapid growth of drone-assisted delivery in e-commerce companies. This study extends the classic vehicle routing problem by optimally scheduling operations in a package depot center using a fleet of drones with different capacities, speeds, and maximum flight ranges. The problem is formulated as a mixed integer programming model and solved using FIFO-based and rescheduling-based genetic algorithms. Numerical experiments show the efficiency and practicality of the proposed algorithms for real-world implementations.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Economics
Sebastian Birolini, Alexandre Jacquillat, Mattia Cattaneo, Antonio Pais Antunes
Summary: The ANPSD model optimizes airline network planning by considering interactions between supply and passenger demand. By estimating a demand model and developing a cutting plane algorithm, excellent computational results have been achieved, providing stronger solutions compared to traditional benchmarks.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Management
Paul J. van Kessel, Floris C. Freeman, Bruno F. Santos
Summary: Airline maintenance task scheduling in a disruptive environment requires continuous adjustments due to stochastic arrival of tasks and changes in fleet and resource availability. This study proposes a practical and efficient modeling framework for disruption management in hangar maintenance task scheduling, which includes a mixed integer linear programming model constrained by resource availability. The framework is capable of creating and adjusting maintenance schedules dynamically based on new information. A case study comparing the proposed approach to the current practice of a large airline demonstrates its superior efficiency and stability, achieving a 3% decrease in ground time and over half reduction in schedule changes before operations.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Economics
Shuguang Zhan, S. C. Wong, Pan Shang, Qiyuan Peng, Jiemin Xie, S. M. Lo
Summary: The study focuses on train rescheduling and passenger rerouting in disrupted situations, using an Integer Linear Programming model and ADMM algorithm to decompose the integrated model into train rescheduling and passenger routing subproblems. The subproblems are further decomposed into a series of shortest path problems for trains and passengers, solved by a dynamic programming algorithm. The models and algorithms are tested on both a small hypothetical railway network and a part of the Chinese high-speed railway network.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Environmental Sciences
Maria Pilar Plaza, Franziska Kolek, Vivien Leier-Wirtz, Jens Otto Brunner, Claudia Traidl-Hoffmann, Athanasios Damialis
Summary: This study compared an automated biomonitoring system with a conventional technique and found that the automated system showed higher accuracy but had reliability issues, while the conventional technique had lower pollen abundances but more comparable seasonal traits.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Health Policy & Services
Steffen Heider, Jan Schoenfelder, Thomas Koperna, Jens O. Brunner
Summary: This study proposes a method to consider downstream units in surgery scheduling in order to better control patient flows and preserve the autonomy of each medical specialty. Through a simulation model and real data, it is shown that this method can significantly reduce the workload in the intensive care unit.
HEALTH CARE MANAGEMENT SCIENCE
(2022)
Article
Health Policy & Services
Mansour Zarrin, Jan Schoenfelder, Jens O. Brunner
Summary: This study proposes a framework for analyzing hospital performance by combining self-organizing map artificial neural network (SOM-ANN) and multilayer perceptron ANN (MLP-ANN) modeling approaches. The framework is empirically tested on a dataset of over 1,100 hospitals in Germany, allowing decision-makers to predict the best performance and explore the impact of hospital heterogeneity on relative efficiency scores.
HEALTH CARE MANAGEMENT SCIENCE
(2022)
Article
Management
Markus Seizinger, Jens O. Brunner
Summary: We investigate a problem in vocational school planning for nurses in countries with a dual vocational system, where theoretical and practical education are closely combined and regulated by federal legislation. We create two mixed-integer programming models to solve the planning problems of scheduling classes and assigning apprentices. We develop a heuristic decomposition procedure to efficiently solve the second model. Our computational study provides valuable insights for management based on real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Emergency Medicine
Christina C. Bartenschlager, Jens O. Brunner, Axel R. Heller
NOTFALL & RETTUNGSMEDIZIN
(2022)
Article
Management
Mansour Zarrin, Jens O. Brunner
Summary: This study investigates the efficiency of different variable returns to scale (VRS) DEA models using a Monte Carlo simulation-based data generation process. The results suggest that the Assurance Region (AR) and Slacks-Based Measurement (SBM) DEA models perform better than the widely used BCC model. Therefore, the use of AR and SBM models is recommended for DEA applications under the VRS regime.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Health Care Sciences & Services
Oliver Buchholz, Christopher Haager, Katja Schimmelpfeng, Jens O. Brunner, Jan Schoenfelder
Summary: In order to develop high-quality plans or planning systems in hospitals, it is crucial to have detailed knowledge of surgery durations. Surgeon experience is identified as the most significant influencing factor, and an increase in experience leads to shorter surgery durations on average. However, the impact of experience is influenced by the composition of the surgical team and is weakened during teaching activities. Additionally, the relationship between experience level and surgery duration varies across the distribution of durations, with the strongest correlation observed for shorter surgeries.
OPERATIONS RESEARCH FOR HEALTH CARE
(2023)
Article
Computer Science, Information Systems
Christina C. Bartenschlager, Stefanie S. Ebel, Sebastian Kling, Janne Vehreschild, Lutz T. Zabel, Christoph D. Spinner, Andreas Schuler, Axel R. Heller, Stefan Borgmann, Reinhard Hoffmann, Siegbert Rieg, Helmut Messmann, Martin Hower, Jens O. Brunner, Frank Hanses, Christoph Rommele
Summary: To accurately map the course of infection in the fight against COVID-19, especially in hospitals, the COVIDAL classifier proposes an AI-based diagnosis for symptomatic COVID-19 patients based on lab parameters. The algorithm shows high sensitivity, specificity, and accuracy of up to 90% and outperforms standard AI, PCR, and POC antigen testing in terms of performance, turnaround times, and cost when used in emergency departments. The algorithm's evaluation is based on multicenter data from 4,000 patients with a high ratio of SARS-CoV-2-positive cases.
ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS
(2023)
Article
Management
Sebastian Kraul, Jens O. Brunner
Summary: For educational purposes, medical residents often have to pass through many departments, which place different requirements on them. The impact of priorities on residents' annual planning based on department assignments is analyzed to combat uncertainty that might result in departmental changes. A novel two-stage formulation is presented that combines residents' tactical planning with duty and daily scheduling. Additional priorities can significantly reduce the number of unexpected department assignments in residents' annual planning.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Environmental Sciences
Benjamin Jin, Manuel Milling, Maria Pilar Plaza, Jens O. Brunner, Claudia Traidl-Hoffmann, Bjorn W. Schuller, Athanasios Damialis
Summary: Airborne pollen monitoring is important for various purposes, such as reconstructing historic climates and tracking climate change, forensic applications, and warning individuals with pollen-induced respiratory allergies. While automation of pollen classification exists, pollen detection is still done manually, which is considered the gold standard. In this study, a new-generation automated pollen monitoring sampler, the BAA500, was used along with raw and synthesised microscope images. By employing deep neural network object detectors and a semi-supervised training scheme, the performance of the deep learning algorithms was evaluated and compared to the commercial algorithm, and the results showed significant improvement. This study bridges the gap in pollen detection performance between manual and automated procedures.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Health Policy & Services
Christina Bartenschlager, Milena Grieger, Johanna Erber, Tobias Neidel, Stefan Borgmann, Joerg J. Vehreschild, Markus Steinbrecher, Siegbert Rieg, Melanie Stecher, Christine Dhillon, Maria Ruethrich, Carolin E. M. Jakob, Martin Hower, Axel Heller, Maria Vehreschild, Christoph Wyen, Helmut Messmann, Christiane Piepel, Jens Brunner, Frank Hanses, Christoph Roemmele, LEOSS Study Grp
Summary: The Covid-19 pandemic has led to hospitals being overwhelmed, resulting in the controversial discussion of patient triage from an ethical perspective. Triage involves various aspects such as treatment urgency, disease severity, pre-existing conditions, and patient classification. Determining these pathways is crucial for patient care and hospital capacity planning. The study evaluates a human-made triage algorithm for emergency departments in Germany using a large multicenter dataset of over 4,000 European Covid-19 patients. The algorithm shows limited accuracy and sensitivity, highlighting the potential of analytics, AI, and interactive techniques for improving triage performance.
HEALTH CARE MANAGEMENT SCIENCE
(2023)
Article
Anesthesiology
A. R. Heller, C. Bartenschlager, J. O. Brunner, G. Marckmann
Summary: With the implementation of the new German Triage Act, there has been a prolonged discussion which has left physicians, social associations, lawyers, and ethicists dissatisfied. The act prevents allocation decisions that prioritize new patients with better chances of success over those whose treatment has already begun, leading to a first come first served allocation and higher mortality rates. Despite evidence that age and frailty strongly determine short-term survival, the act prohibits their use as prioritization criteria. The only option left is the consistent termination of treatment desired by the patient, regardless of resource scarcity.
Article
Anesthesiology
Sara Garber, Jens O. Brunner, Axel R. Heller, Georg Marckmann, Christina C. Bartenschlager
Summary: The increase in patients during the COVID-19 pandemic poses challenges to the healthcare system, especially in the intensive care unit. Through infection control measures and logistical efforts, Germany successfully treated all patients needing intensive care without triage, even in regions with high patient pressure and low capacities. A study found that implementing a triage policy based on survival probabilities can reduce mortality in the intensive care unit for all patient groups.
Article
Computer Science, Interdisciplinary Applications
Sebastian Kraul, Markus Seizinger, Jens O. Brunner
Summary: This article presents a model that predicts the optimal dual variables for the cutting stock problem. The model analyzes the impact of different attributes on the optimal dual variables within problem instances. Two learning algorithms are developed to predict the best algorithm configuration based on the predicted optimal dual variables, eliminating the need for numerical tests. Computational studies show the effectiveness of both algorithms, with the choice depending on the variability in item quantities between instances.
INFORMS JOURNAL ON COMPUTING
(2023)
Article
Management
Jakob Heins, Jan Schoenfelder, Steffen Heider, Axel R. Heller, Jens O. Brunner
Summary: This study presents a scalable forecasting framework for predicting the short-term bed occupancy of COVID-19 patients. The framework was applied to different levels of granularity and geography, providing accurate forecasts despite data availability and quality issues.
INFORMS JOURNAL ON APPLIED ANALYTICS
(2022)