Article
Thermodynamics
Yannick Wack, Sylvain Serra, Martine Baelmans, Jean-Michel Reneaume, Maarten Blommaert
Summary: This paper compares two nonlinear topology optimization methods for District Heating Networks in terms of computational cost and optimality gap. The benchmark demonstrates that the density-based approach has subquadratic scaling in computational cost, making it suitable for large-scale problems, while the combinatorial approach has exponential scaling. The density-based method optimized a network for 600 streets in only 35 minutes, compared to 29 hours required by the combinatorial approach. Resolving the integer constraint on pipe placement does not necessarily lead to a superior design, but makes optimization of large-scale problems intractable. Further study highlights the importance of initialization strategies when solving the nonlinear topology optimization problem.
Article
Operations Research & Management Science
Ksenia Bestuzheva, Antonia Chmiela, Benjamin Mueller, Felipe Serrano, Stefan Vigerske, Fabian Wegscheider
Summary: This paper discusses the recent changes and extensions made to the SCIP framework for solving convex and nonconvex mixed-integer nonlinear programs (MINLPs). It provides an overview of the specific features in SCIP for MINLP solving and addresses the challenges in benchmarking global MINLP solvers. The paper also includes a comparison with several state-of-the-art global MINLP solvers.
JOURNAL OF GLOBAL OPTIMIZATION
(2023)
Article
Operations Research & Management Science
Jacek Gondzio, E. Alper Yildirim
Summary: This paper investigates how to reformulate a standard quadratic program as a mixed integer linear programming problem, proposing two alternative formulations. By utilizing binary variables and valid inequalities, the formulations significantly outperform other global solution approaches in extensive computational results.
JOURNAL OF GLOBAL OPTIMIZATION
(2021)
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
Computer Science, Software Engineering
Ashutosh Mahajan, Sven Leyffer, Jeff Linderoth, James Luedtke, Todd Munson
Summary: The study introduces a flexible MINLP framework called Minotaur, which allows for algorithm exploration and structure exploitation while maintaining high computational efficiency. Efficient implementations of standard MINLP techniques and structure-exploiting extensions are demonstrated to have a significant impact on solution times. Global solutions to difficult nonconvex MINLP problems may be unreachable without a flexible framework that enables structure exploitation.
MATHEMATICAL PROGRAMMING COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
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
Energy & Fuels
Roymel R. Carpio, Thiago C. dAvila, Daniel P. Taira, Leonardo D. Ribeiro, Bruno F. Viera, Alex F. Teixeira, Mario M. Campos, Argimiro R. Secchi
Summary: The study evaluates the performance of two approaches for oil production optimization on offshore platforms, highlighting the limitations of each strategy and proposing a hybrid two-stage optimization strategy to achieve global optimal operating conditions.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
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
Multidisciplinary Sciences
Tomas Thorbjarnarson, Neil Yorke-Smith
Summary: Recent research has shown potential in optimizing certain aspects of neural networks (NNs) using Mixed Integer Programming (MIP) solvers. However, the approach of training NNs with MIP solvers has not been explored thoroughly. This article introduces new MIP models for training integer-valued neural networks (INNs) and provides two methods to improve the efficiency and data handling capabilities of MIP solvers. Experimental results demonstrate the superiority of our approach in terms of accuracy, training time, and data usage compared to previous state-of-the-art methods. Our methodology is particularly proficient at training NNs with minimal data and memory requirements, making it valuable for deployment on low-memory devices.
Article
Operations Research & Management Science
Hacene Ouzia, Nelson Maculan
Summary: This work introduces new mixed integer nonlinear optimization models for the Euclidean Steiner tree problem in high dimensions (d >= 3), featuring nonsmooth objective functions with convex continuous relaxations. Four convex mixed integer linear and nonlinear relaxations derived from these models are considered, each having the same feasible solutions as their respective original models. Preliminary computational results discussing the main features of these relaxations are presented.
JOURNAL OF GLOBAL OPTIMIZATION
(2022)
Article
Engineering, Civil
Chen Chen, Xiaodong Zhang, Shuguang Wang, Huayong Zhang
Summary: A PFRWE model was developed to support regional-scale integrated planning of water and energy, incorporating multiple programming methods to improve robustness. Application to a real-world case study showed optimal decisions and tradeoffs between robustness and profit, with the PFRWE model found to have advantages in maintaining feasibility and optimizing objective value.
JOURNAL OF HYDROLOGY
(2021)
Article
Operations Research & Management Science
Xin Cheng, Xiang Li
Summary: This paper introduces a method based on discretization and mixed-integer linear programming relaxations for global optimization of mixed-integer bilinear programs. By proposing new discretization formulations and an adaptive discretization global optimization algorithm, better computational efficiency and solution quality are achieved.
JOURNAL OF GLOBAL OPTIMIZATION
(2022)
Article
Mathematics, Applied
Bo Zhang, Yuelin Gao, Xia Liu, Xiaoli Huang
Summary: This paper proposes a new global optimization algorithm for solving the mixed-integer quadratically constrained quadratic fractional programming problem. By converting the problem into an equivalent generalized bilinear fractional programming problem and using linear fractional relaxation technique and rectangular adjustment-segmentation technique, combined with the branch-and-bound procedure, an efficient algorithm is proposed.
JOURNAL OF COMPUTATIONAL MATHEMATICS
(2023)
Article
Operations Research & Management Science
Andrew Allman, Qi Zhang
Summary: This work aims to combine the strengths of global mixed-integer nonlinear optimization and branch-and-price, solving a class of nonconvex mixed-integer nonlinear programs effectively. The study shows that using discretization of integer linking variables can lead to the application of Dantzig-Wolfe reformulation and branch-and-price method for solution, which has been underutilized in literature.
JOURNAL OF GLOBAL OPTIMIZATION
(2021)
Article
Engineering, Civil
Caroline Blocher, Filippo Pecci, Ivan Stoianov
JOURNAL OF HYDRAULIC ENGINEERING
(2020)
Article
Management
Dimitrios Nerantzis, Filippo Pecci, Ivan Stoianov
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2020)
Article
Engineering, Civil
Filippo Pecci, Panos Parpas, Ivan Stoianov
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2020)
Article
Computer Science, Information Systems
Aly-Joy Ulusoy, Filippo Pecci, Ivan Stoianov
IEEE SYSTEMS JOURNAL
(2020)
Article
Engineering, Civil
Alexander Waldron, Filippo Pecci, Ivan Stoianov
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2020)
Article
Engineering, Multidisciplinary
Aly-Joy Ulusoy, Filippo Pecci, Ivan Stoianov
Summary: This manuscript investigates the design-for-control problem of minimizing pressure induced leakage and maximizing resilience in existing water distribution networks, proposing a method to approximate the non-dominated set of the problem with guarantees of global non-dominance. By simultaneously selecting locations for installing new valves and/or pipes, and optimizing valve control settings, the challenging optimization problem belongs to the class of non-convex bi-objective mixed-integer non-linear programs. The proposed method outperforms state-of-the-art global optimization solvers in two case study networks.
OPTIMIZATION AND ENGINEERING
(2022)
Article
Engineering, Civil
Filippo Pecci, Ivan Stoianov, Avi Ostfeld
Summary: This paper investigates the problem of optimal placement and operation of valves and chlorine boosters in water networks. The objective is to minimize average zone pressure while penalizing deviations from target chlorine concentrations. The problem formulation includes nonconvex quadratic terms within constraints representing the energy conservation law for each pipe, and discretized differential equations modeling advective transport of chlorine concentrations. Moreover, binary variables model the placement of valves and chlorine boosters. The resulting optimization problem is a nonconvex mixed integer nonlinear program, which is difficult to solve, especially when large water networks are considered. We develop a new convex heuristic to optimally place and operate valves and chlorine boosters in water networks, while estimating the optimality gaps for the computed solutions. We evaluate the proposed heuristic using case studies with varying sizes and levels of connectivity and complexity, including two large operational water networks. The convex heuristic is shown to generate good-quality feasible solutions in all problem instances with bounds on the optimality gap comparable to the level of uncertainty inherent in hydraulic and water quality models. (C) 2021 American Society of Civil Engineers.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2022)
Article
Engineering, Civil
Caroline Blocher, Filippo Pecci, Ivan Stoianov
Summary: Hydraulic model-based leak localisation in water distribution networks is challenging due to limited hydraulic measurements, a wide range of leak properties, and uncertainties in models and data. Prior assumptions were investigated to improve leak localisation in the presence of uncertainties, comparing the effectiveness of different methods.
WATER RESOURCES MANAGEMENT
(2021)
Article
Engineering, Environmental
Aly-Joy Ulusoy, Herman A. Mahmoud, Filippo Pecci, Edward C. Keedwell, Ivan Stoianov
Summary: This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN) while minimizing pressure induced pipe stress and leakage. The authors propose a sequential hybrid method that combines evolutionary algorithms and gradient-based mathematical optimization for optimal design-for-control of large-scale WDNs. The results show that the proposed method increases the resilience of the network and efficiently improves the initial approximation computed by the evolutionary algorithm search.
Article
Engineering, Environmental
Alexander Waldron, Aly-Joy Ulusoy, Filippo Pecci, Ivan Stoianov
Summary: The calibration and continuous maintenance of hydraulic models are crucial for optimizing and managing water distribution networks. This paper proposes a novel sampling method based on principal component analysis (PCA) to evaluate the significance of newly observed hydraulic data for model calibration and maintenance, allowing for different sized batches of data to be utilized.
Article
Computer Science, Interdisciplinary Applications
Filippo Pecci, Ivan Stoianov
Summary: This paper presents a new bi-objective optimization problem formulation to investigate the trade-offs between conflicting objectives. We propose a convex heuristic to approximate the Pareto front and compute guaranteed bounds to discard portions of the criterion space without non-dominated solutions. Our method relies on a Chebyshev scalarization scheme and convex optimization.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Engineering, Environmental
Bradley Jenks, Filippo Pecci, Ivan Stoianov
Summary: This paper proposes a new optimal design-for-control problem to maximize the self-cleaning capacity (SCC) of water distribution networks (WDNs) by controlling diurnal flow velocities. A heuristic algorithm is proposed to solve the nonconvex mixed integer nonlinear programming (MINLP) optimization problem. The algorithm combines convex relaxations, a randomization technique, and a multi-start strategy to compute feasible solutions.
Review
Automation & Control Systems
Bradley Jenks, Aly-Joy Ulusoy, Filippo Pecci, Ivan Stoianov
Summary: This paper investigates the integration of optimal pressure management and self-cleaning controls in dynamically adaptive water distribution networks. The study reviews existing valve placement and control problems for minimizing average zone pressure (AZP) and maximizing self-cleaning capacity (SCC). A bi-objective design-for-control problem is formulated to jointly optimize the locations and operational settings of pressure control and automatic flushing valves. The results suggest that significant improvements in SCC can be achieved with minimal trade-offs in AZP performance, and a hierarchical design strategy is capable of yielding good quality solutions to both objectives. Moreover, an adaptive control scheme is investigated for dynamically transitioning between AZP and SCC controls.
ANNUAL REVIEWS IN CONTROL
(2023)
Proceedings Paper
Automation & Control Systems
Filippo Pecci, Ivan Stoianov, Avi Ostfeld
Summary: This manuscript investigates the design-for-control problem of optimizing chlorine boosters' locations and operational settings in water networks. The objective is to minimize deviations from target chlorine concentrations. The problem involves discretized linear PDEs to model chlorine concentrations' advective transport and binary variables to model chlorine boosters' placement. The resulting optimization problem is a difficult convex mixed integer program. A new swapping heuristic is proposed to optimally place and control chlorine boosters based on continuous relaxation. The heuristic is evaluated using two case studies, including a large operational water network in the UK.
2022 EUROPEAN CONTROL CONFERENCE (ECC)
(2022)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
