Article
Engineering, Multidisciplinary
Can Li, David E. Bernal, Kevin C. Furman, Marco A. Duran, Ignacio E. Grossmann
Summary: The study introduces a new algorithm for addressing nonconvex two-stage stochastic programming problems with any continuous or discrete probability distributions. The algorithm uses internal sampling and iterations between solving mixed-integer linear programming master problems and nonconvex nonlinear programming subproblems to obtain optimal solutions. In practice, an algorithm with confidence intervals is proposed to estimate sample sizes and update them dynamically during iterations.
OPTIMIZATION AND ENGINEERING
(2021)
Article
Computer Science, Software Engineering
Rui Chen, James Luedtke
Summary: The study shows that the feasibility likelihood of SAA solutions converges exponentially fast to zero with the sample size, especially for problems with a finite feasible region. For problems with a non-finite feasible region, modified padded SAA problems are proposed to generate solutions with a feasible recourse decision with high confidence.
MATHEMATICAL PROGRAMMING
(2022)
Article
Management
Dimitris Bertsimas, Shimrit Shtern, Bradley Sturt
Summary: The study introduces a simple approximation scheme based on overlapping linear decision rules for solving data-driven two-stage distributionally robust optimization problems, showing that this scheme is asymptotically optimal under certain conditions, and effective for two-stage stochastic problems without complete recourse.
OPERATIONS RESEARCH
(2022)
Article
Computer Science, Software Engineering
Thomas Kleinert, Veronika Grimm, Martin Schmidt
Summary: The paper investigates MIQP-QP bilevel optimization problems, transforming the lower level to yield an equivalent nonconvex single-level reformulation of the original problem and proposing cutting-plane algorithms based on outer-approximation. These methods are capable of solving bilevel instances with several thousand variables and constraints, outperforming traditional approaches significantly.
MATHEMATICAL PROGRAMMING
(2021)
Article
Operations Research & Management Science
Erik Diessel
Summary: This paper proposes a new algorithm for approximating the Pareto frontier of bicriteria mixed-integer programs with convex constraints. By adaptively creating patches of solutions with shared assignments for discrete variables, the algorithm quickly converges to the true Pareto frontier. The algorithm's efficiency is demonstrated through numerical results and competitive performance with other state-of-the-art approaches.
Article
Chemistry, Multidisciplinary
Soukaina Oujana, Lionel Amodeo, Farouk Yalaoui, David Brodart
Summary: This paper discusses a research project that aims to optimize the scheduling of production orders in the packaging field. The problem is modeled as an extended version of the hybrid and flexible flowshop scheduling problem with precedence constraints, parallel machines, and sequence-dependent setups. Two methodologies, mixed-integer linear programming (MILP) and constraint programming (CP), are used to tackle the problem. Resource calendar constraints are added to the models, and a novel heuristic is designed for quick solutions. The proposed problem can be easily modified to suit real-world situations involving similar scheduling characteristics.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Bahriye Akay, Dervis Karaboga, Beyza Gorkemli, Ebubekir Kaya
Summary: This paper reviews the use of Artificial Bee Colony algorithm for solving discrete numeric optimization problems, discussing various encoding types, search operators and selection operators integrated into ABC. It is the first comprehensive survey study on this topic and aims to benefit readers interested in utilizing ABC for binary, integer and mixed integer discrete optimization problems.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Chemical
Florian Joseph Baader, Andre Bardow, Manuel Dahmen
Summary: The increasing volatility of electricity prices highlights the importance of simultaneous scheduling optimization for production processes and their energy systems. In this study, we propose an efficient scheduling formulation that takes into account both process dynamics and binary on/off-decisions in the energy system. By considering three different aspects, we demonstrate the feasibility of achieving fast optimization for real-time scheduling.
Article
Computer Science, Software Engineering
Ilias Zadik, Miles Lubin, Juan Pablo Vielma
Summary: We investigate the structural geometric properties of mixed-integer convex representable (MICP-R) sets and compare them with the class of mixed-integer linear representable (MILP-R) sets. We provide examples of MICP-R sets that are countably infinite unions of convex sets with countably infinitely many different recession cones, and countably infinite unions of polytopes with different shapes. These examples highlight the differences between MICP-R sets and MILP-R sets.
MATHEMATICAL PROGRAMMING
(2023)
Article
Computer Science, Interdisciplinary Applications
Pieter Smet
Summary: Carsharing has become a viable and cost-effective mode of transportation that helps improve the environment and reduce traffic congestion. System operators face the challenge of designing an efficient system that meets user demand while controlling operational costs. Vehicle substitution in carsharing systems can increase user experience but also impact expected profit.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Peng Wu, Yun Wang, Junheng Cheng, Yantong Li
Summary: This paper investigates a new bi-objective parallel machine scheduling and location problem and proposes a more efficient solution method. Experimental results show that the proposed method obtains more Pareto-optimal solutions and is faster in computation.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Operations Research & Management Science
Wendel Melo, Marcia Fampa, Fernanda Raupp
Summary: In this paper, we present two new algorithms for convex mixed integer nonlinear programming, which are based on the well-known Extended Cutting Plane algorithm. The computational results show that these two algorithms are competitive with popular MINLP algorithms while maintaining the nice characteristic of being a first-order method.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Management
Maryam Haghi, Hossein Hashemi Doulabi, Ivan Contreras, Nadia Bhuiyan
Summary: This paper examines the integrated scheduling of consultation and treatment appointments for chemotherapy patients, taking into account the stochastic duration of injection. The objective is to minimize the clinic's overtime and patients' waiting time. Two-stage stochastic programming models and a sample average approximation algorithm are developed as solution methods to address the problem.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Automation & Control Systems
Jiao Liu, Yong Wang, Bin Xin, Ling Wang
Summary: This article proposes a two-phase method based on biobjective optimization to address the issue of local convergence caused by integer restrictions in mixed-integer programming problems. By utilizing a measure function and removing integer restrictions, the MIP problem is transformed into a constrained biobjective optimization problem, leading to better solutions.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Mathematics, Applied
Raghu Pasupathy, Yongjia Song
Summary: This study introduces an adaptive sequential SAA algorithm to solve large-scale two-stage stochastic linear programs, achieving favorable performance through a sequential framework with optimal sample size schedule and the use of warm starts. Extensive numerical tests demonstrate the success of the proposed algorithm, providing a solution with a probabilistic guarantee on quality.
SIAM JOURNAL ON OPTIMIZATION
(2021)
Article
Engineering, Multidisciplinary
Alex T. Masarie, Yu Wei, Erin J. Belval, Matthew P. Thompson, Iuliana Oprea, Maryam Tabatabaei, Dave E. Calkin
APPLIED MATHEMATICAL MODELLING
(2019)
Article
Forestry
Erin J. Belval, Yu Wei, Michael Bevers
Article
Forestry
Jude Bayham, Erin J. Belval, Matthew P. Thompson, Christopher Dunn, Crystal S. Stonesifer, David E. Calkin
Article
Forestry
Erin J. Belval, Crystal S. Stonesifer, David E. Calkin
Article
Environmental Sciences
Yu Wei, Matthew P. Thompson, Erin Belval, Benjamin Gannon, David E. Calkin, Christopher D. O'Connor
Summary: This study evaluates the effectiveness of contingency firelines in increasing fire containment probability, reducing losses, and enhancing firefighter safety. It highlights the importance of future data and model improvements in contingency planning, and suggests that contingency plans are most beneficial when fireline breaching risk is high. However, significant data and knowledge gaps must be addressed for operational use of the model.
NATURAL RESOURCE MODELING
(2021)
Article
Forestry
Crystal S. Stonesifer, David E. Calkin, Matthew P. Thompson, Erin J. Belval
Summary: Aircraft play a key role in wildfire suppression globally, but their use also comes with certain risks that require strategic risk management. To address this issue, a framework for risk-informed strategic aviation decision support system has been proposed, utilizing aircraft event tracking data and geospatial datasets to guide decision makers in strategic risk management.
Article
Multidisciplinary Sciences
Erin J. Belval, Jude Bayham, Matthew P. Thompson, Jacob Dilliott, Andrea G. Buchwald
Summary: Wildfire management in the US relies on a complex nationwide network, which may also provide pathways for transmission of infectious diseases between fire sites. In this study, an epidemiological model adapted to the interconnected fire system was developed to understand the potential impacts of infectious diseases on workforce capacity. The model simulated SARS-CoV-2 transmission under different intervention scenarios and found that vaccination and social distancing are effective at reducing transmission at fire incidents. This work sets the foundation for future modeling efforts focused on managing the spread of infectious diseases at wildland fire incidents.
SCIENTIFIC REPORTS
(2022)
Article
Forestry
Dung Nguyen, Yu Wei
Summary: This study proposes a multistage stochastic mixed integer program with recourse for optimizing prescribed burning decisions. The results show that using larger fire samples can lead to better solutions, but the benefit diminishes after reaching a certain threshold.
Article
Forestry
Erin Belval, Sarah McCaffrey, Trevor Finney, David Calkin, Shane Greer
Summary: In the 2020 fire season, the fire management community developed and tested new practices to address the challenges posed by the coronavirus pandemic. A survey conducted with Interagency Hotshot Crew (IHC) superintendents revealed that innovations in paperwork, briefings, and fire camp setup led to improved operational efficiency and crew health and wellbeing. Challenges primarily arose from logistical and communication issues. The implications of this study suggest that virtual paperwork, virtual briefings, and dispersed camp setups may have long-term benefits for large-scale fire suppression operations.
JOURNAL OF FORESTRY
(2023)
Article
Management
Erin J. Belval, Matthew P. Thompson
Summary: In recent years, Colorado has faced severe wildfires that have caused significant damage to forests, watersheds, and communities. With the increasing human development and changing climate, there is a growing need to improve the efficiency and capability of the state's dispatching system for wildfire management. This study describes a partnership with Rocky Mountain Coordinating Group (RMCG) and Rocky Mountain Area Fire Executive Council (RMA-FEC) to reorganize the dispatching system using a structured decision-making process.
Article
Ecology
Benjamin Gannon, Yu Wei, Erin Belval, Jesse Young, Matthew Thompson, Christopher O'Connor, David Calkin, Christopher Dunn
Summary: Decisions regarding fuel break construction, maintenance, and use in fire suppression lack sufficient information on their success rates and driving factors. This study analyzed the encounters between fuel breaks and recent large wildfires in Southern California, incorporating various characteristics such as biophysical factors, suppression strategies, weather conditions, and fire behavior. Statistical models were developed to determine the effectiveness of fuel breaks, and the results indicated that successful fuel break implementation is influenced by suppression efforts, weather conditions, and fire behavior. Factors related to fuel break placement, design, and maintenance are less significant, although wider and better maintained fuel breaks align with higher success rates and previous research findings that accessibility improves fuel break effectiveness. Additionally, fuel breaks that have experienced wildfire burn in the past decade are more likely to be effective, suggesting that combining fuel breaks with broader fuel reduction efforts may yield better results.
Article
Ecology
Erin J. Belval, Karen C. Short, Crystal S. Stonesifer, David E. Calkin
Summary: A severe outbreak of wildfire across the US Pacific Coast during August 2020 led to persistent fire activity through the end of summer. Visualizations revealed a significant gap between late-season resource demand and availability, highlighting the need for further assessment of suppression resource acquisition and allocation systems.
Article
Ecology
Matthew P. Thompson, Erin J. Belval, Jake Dilliott, Jude Bayham
Summary: The global pandemic in 2020 increased the complexity and uncertainty of wildfire incident response in the United States, emphasizing the importance of decision support to enhance coordination and communication efforts. Epidemiological modeling highlighted the substantial risk of COVID-19 outbreak at traditional large fire camps, necessitating the implementation of mitigation strategies and expanded networks to interface with public health agencies. The development and application of a COVID-19 Incident Risk Assessment Tool aimed to address the identified gap in assessing COVID-19 risks at the incident level, ultimately supporting risk-informed decision-making during wildfire response.
FRONTIERS IN FORESTS AND GLOBAL CHANGE
(2021)
Article
Ecology
Erin J. Belval, Christopher D. O'Connor, Matthew P. Thompson, Michael S. Hand
Article
Ecology
Matthew P. Thompson, Jude Bayham, Erin Belval