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
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
Computer Science, Interdisciplinary Applications
Brendan Ruskey, Eric Rosenberg
Summary: In this article, we propose an approach to minimize expected unmet demand in a supply chain using a Bayesian network. We consider node upgrades to reduce the probability of node failure and formulate the problem as a linear binary integer program. Unlike previous formulations, our model allows for flexible conditional probability tables and multiple types of upgrades. We present computational results and discuss the application to a larger food supply chain. We also introduce a preprocessing method to reduce the number of constraints and evaluate the runtime savings achieved.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
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
Management
Filippo Pecci, Ivan Stoianov, Avi Ostfeld
Summary: A new mixed integer nonlinear programming formulation is proposed for optimizing the placement and operation of valves and chlorine booster stations in water distribution networks. The implemented algorithm outperforms off-the-shelf solvers and computes high-quality feasible solutions with bounds on optimality gaps.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(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
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
Economics
Mingyang Pei, Peiqun Lin, Jun Du, Xiaopeng Li, Zhiwei Chen
Summary: Modular vehicle technology allows flexible adjustments of vehicle capacity to meet passenger demand, and the proposed modular transit network system concept aims to address issues in traditional public transportation systems. By developing a mixed-integer nonlinear programming model, the optimal MTNS design is achieved, and its effectiveness is demonstrated through numerical examples.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(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
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
Automation & Control Systems
Xiumei Han, Haiqin Qin, Zhitao Wang, Ning Xu, Xudong Zhao, Jinfeng Zhao
Summary: This paper studies the optimal event-triggered control for constrained-input discrete-time switched nonlinear systems and proposes an algorithm based on triggered states. Neural networks are used to approximate the control input and costate vector functions of each subsystem. A trigger condition is designed to ensure the asymptotic stability of the switched system.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
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
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, Software Engineering
Navid Ansari, Hans-Peter Seidel, Vahid Babaei
Summary: In the field of computational design and fabrication, neural networks have become important alternatives to bulky forward simulations. The question of inverse design, i.e., how to compute a design that meets a desired target performance, has been long-standing. In this study, we demonstrate that the commonly occurring piecewise linear property in neural networks enables an inverse design formulation based on mixed-integer linear programming. Our method uncovers globally optimal or near optimal solutions in a principled manner and greatly facilitates combinatorial inverse design tasks. Additionally, we propose an efficient yet near-optimal hybrid approach for problems where finding the optimal solution is intractable.
ACM TRANSACTIONS ON GRAPHICS
(2022)
Article
Computer Science, Artificial Intelligence
Haohao Song, Jiquan Wang, Zhiwen Cheng, Tiezhu Chang
Summary: This paper proposes a hybrid improved sine and cosine algorithm to address the shortcomings of existing algorithms in solving mixed-integer programming problems, such as local convergence and poor solution accuracy. The improved position update formula is introduced to enhance the algorithm's global and local search abilities, and combined mutation is further given to avoid local convergence. Experimental results demonstrate that the hybrid mechanism of improved sine and cosine algorithm and combined mutation is demonstrably effective, providing a satisfactory solution for addressing high-dimensional complicated mixed-integer programming problems.
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
Agronomy
Boran Ekin Aydin, Gualbert H. P. Oude Essink, Joost R. Delsman, Nick van de Giesen, Edo Abraham
Summary: A significant increase in surface water salinization is expected in low-lying deltas worldwide, which leads to the increased demand for freshwater flushing. To address this issue, this paper proposes a novel network model-based approach to optimize the control of water level and salinity, aiming to reduce the demand for scarce freshwater.
AGRICULTURAL WATER MANAGEMENT
(2022)
Article
Engineering, Civil
David B. Steffelbauer, Jochen Deuerlein, Denis Gilbert, Edo Abraham, Olivier Piller
Summary: In this study, multiple leaks in a water distribution network are detected simultaneously by optimizing the hydraulic model. A hierarchical decision-making approach is employed to build demand models using smart meter data, calibrate roughness parameters, and transform leaks into virtual leak flow signals through a dual model. This innovative dual modeling approach achieved the highest true-positive rates for leak isolation in the competition.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Ties van der Heijden, Dorien Lugt, Ronald van Nooijen, Peter Palensky, Edo Abraham
Summary: This manuscript proposes the use of multiple electricity spot markets for price-based demand response in open canal systems in the Netherlands. By combining day ahead and intraday electricity markets and employing a hierarchical receding horizon economic Model Predictive Control, the proposed strategy leads to a decrease in costs and provides new insights into the trade-off between CO2 emissions and operating costs, differences between German and Dutch markets, and temporal changes in market conditions due to renewable energy integration.
JOURNAL OF HYDROINFORMATICS
(2022)
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
Environmental Sciences
Zarrar Khan, Edo Abraham, Srijan Aggarwal, Manal Ahmad Khan, Ricardo Arguello, Meghna Babbar-Sebens, Julia Lacal Bereslawski, Jeffrey M. Bielicki, Pietro Elia Campana, Maria Eugenia Silva Carrazzone, Homero Castanier, Fi-John Chang, Pamela Collins, Adela Conchado, Koteswara Rao Dagani, Bassel Daher, Stefan C. Dekker, Ricardo Delgado, Fabio A. Diuana, Jonathan Doelman, Amin A. Elshorbagy, Chihhao Fan, Rossana Gaudioso, Solomon H. Gebrechorkos, Hatim M. E. Geli, Emily Grubert, Daisy Huang, Tailin Huang, Ansir Ilyas, Aleksandr Ivakhnenko, Graham P. W. Jewitt, Maria Joao Ferreira dos Santos, J. Leah Jones, Elke Kellner, Elisabeth H. Krueger, Ipsita Kumar, Jonathan Lamontagne, Angelique Lansu, Sanghyun Lee, Ruopu Li, Pedro Linares, Diego Marazza, Maria Pia Mascari, Ryan A. McManamay, Measrainsey Meng, Simone Mereu, Fernando Miralles-Wilhelm, Rabi Mohtar, Abubakr Muhammad, Adenike Kafayat Opejin, Saket Pande, Simon Parkinson, Raphael Payet-Burin, Meenu Ramdas, Eunice Pereira Ramos, Sudatta Ray, Paula Roberts, Jon Sampedro, Kelly T. Sanders, Marzieh Hassanzadeh Saray, Jennifer Schmidt, Margaret Shanafield, Sauleh Siddiqui, Micaela Suriano, Makoto Taniguchi, Antonio Trabucco, Marta Tuninetti, Adriano Vinca, Bjorn Weeser, Dave D. White, Thomas B. Wild, Kamini Yadav, Nithiyanandam Yogeswaran, Tokuta Yokohata, Qin Yue
Summary: This article introduces the nexus between water, energy, and food, emphasizing the importance of understanding their interdependencies and trade-offs in solving global challenges. The article presents 10 key recommendations, highlighting the need for a nexus community of practice to facilitate communication, share standardized datasets, and develop applied case studies.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Energy & Fuels
Chelsea Kaandorp, Tes Miedema, Jeroen Verhagen, Nick van de Giesen, Edo Abraham
Summary: This study proposes a computational approach to find a mix of heat options per neighborhood that minimizes cumulative carbon emissions between 2030 and 2050. The results show that ambitious measures for building insulation and decarbonization in electricity generation can significantly reduce committed emissions, with low temperature heat systems being the optimal solution.
Article
Energy & Fuels
Yacob Mulugetta, Youba Sokona, Philipp A. Trotter, Samuel Fankhauser, Jessica Omukuti, Lucas Somavilla Croxatto, Bjarne Steffen, Meron Tesfamichael, Edo Abraham, Jean-Paul Adam, Lawrence Agbemabiese, Churchill Agutu, Mekalia Paulos Aklilu, Olakunle Alao, Bothwell Batidzirai, Getachew Bekele, Anteneh G. Dagnachew, Ogunlade Davidson, Fatima Denton, E. Ogheneruona Diemuodeke, Florian Egli, Eshetu Gebrekidan Gebresilassie, Mulualem Gebreslassie, Mamadou Goundiam, Haruna Kachalla Gujba, Yohannes Hailu, Adam D. Hawkes, Stephanie Hirmer, Helen Hoka, Mark Howells, Abdulrasheed Isah, Daniel Kammen, Francis Kemausuor, Ismail Khennas, Wikus Kruger, Ifeoma Malo, Linus Mofor, Minette Nago, Destenie Nock, Chukwumerije Okereke, S. Nadia Ouedraogo, Benedict Probst, Maria Schmidt, Tobias S. Schmidt, Carlos Shenga, Mohamed Sokona, Jan Christoph Steckel, Sebastian Sterl, Bernard Tembo, Julia Tomei, Peter Twesigye, Jim Watson, Harald Winkler, Abdulmutalib Yussuff
Summary: Aligning development and climate goals in Africa requires country-specific approaches to energy system development, taking into account the unique starting points and uncertainties of each country. Policy, finance, and research recommendations are provided to identify suitable energy pathways for development and enable their implementation.
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
Automation & Control Systems
Gert van Lagen, Edo Abraham, Peyman Mohajerin Esfahani
Summary: This article proposes an active fault isolation method for localizing leaks in water distribution networks (WDNs). The method uses classification of observed outputs and smooth kernel density estimation to approximate the output probability distribution functions (PDFs) corresponding to the considered faults. An active algorithm is introduced to minimize the overlap between output PDFs by designing optimal control inputs. Due to physical limitations and uncertainties, complete separation and fault isolation for a single observed output cannot be guaranteed, so an iterative framework is used with posterior probabilities from previous time steps serving as prior probabilities for the next time step. The method improves performance compared to the best passive method in the literature.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(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)