Article
Mathematical & Computational Biology
Shanshan Pan, Jinbao Jian, Linfeng Yang
Summary: This paper proposes a mixed integer linear programming approach to solve the dynamic economic dispatch problem and demonstrates its effectiveness and feasibility through experiments.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Energy & Fuels
Ahmad M. Alshamrani, Khalid A. Alnowibet, Adel F. Alrasheedi
Summary: This article proposed a bi-level transmission expansion planning model that incorporates prohibited operating zones (POZ) and multi-fuel units (MFU). The study showed that considering these issues in the lower level's economic dispatch problem introduces more complexity but provides more realistic optimal plans.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Computer Science, Software Engineering
M. V. Dolgopolik
Summary: This paper proposes and studies a version of the DCA (Difference-of-Convex functions Algorithm) using the 21 penalty function to solve nonsmooth DC optimization problems with nonsmooth DC equality and inequality constraints. The method uses an adaptive penalty updating strategy to improve performance, which is based on the steering exact penalty methodology and involves solving auxiliary convex subproblems to determine the appropriate value of the penalty parameter. The paper provides a detailed convergence analysis of the method and demonstrates its practical performance through application to two nonsmooth discrete optimal control problems.
OPTIMIZATION METHODS & SOFTWARE
(2023)
Article
Engineering, Electrical & Electronic
Ahmad M. Alshamrani, Adel F. Alrasheedi, Khalid A. Alnowibet
Summary: This article proposes a methodology for generation expansion planning in power networks that incorporates prohibited operating zones (POZ) and multi-fuel options (MFO). The existing models do not consider these factors, which can significantly impact the optimal planning scheme.
ELECTRICAL ENGINEERING
(2023)
Article
Operations Research & Management Science
Nachuan Xiao, Xin Liu, Kim-Chuan Toh
Summary: In this paper, the authors propose constraint-dissolving approaches for optimization problems over closed embedded submanifolds of Rn. They transfer the Riemannian optimization problem into the unconstrained minimization of a constraint-dissolving function, which has easy-to-compute gradient and Hessian. Theoretical properties of the approach are studied, and it is shown that the proposed method inherits convergence properties from unconstrained optimization.
MATHEMATICS OF OPERATIONS RESEARCH
(2023)
Article
Automation & Control Systems
Deliang Li, Chunyu Yang, Dexuan Zou
Summary: This paper presents a nondominated sorting genetic algorithm III with three crossover strategies (NSGA-III-TCS) for the combined heat and power dynamic economic emission dispatch (CHPDEED) problems with or without prohibited operating zones. NSGA-III-TCS achieves the largest average hypervolumes and shows a desirable convergence for six CHPDEED problems. It also has relatively high coverage rates in most cases, indicating the dominance of its Pareto sets over those of the other algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Jinling Meng, Fei Zhu, Yangyang Ge, Peiyao Zhao
Summary: Improving resistance to interference is crucial for the widespread application of reinforcement learning. To address the issue of unstable results and performance deterioration in standard adversarial training, this study proposes a robust reinforcement learning method that integrates safety constraints into adversarial training by modeling them as environment termination conditions. Furthermore, a modified constrained Markov Decision Process (CMDP) considering perturbation is introduced for better modeling of the robust reinforcement learning problem.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
D. Martins, M. Mendes
Summary: The paper introduces a hybrid algorithm, HEA-GA-SA, to solve the Economic Dispatch Problem with Prohibited Operating Zones and Uncertainties, considering the worst case of parametric uncertainties. Despite having higher computational and dispatch costs, the robust solutions found by HEA-GA-SA remain feasible in the presence of uncertainties, making them suitable for real-world implementation.
IEEE LATIN AMERICA TRANSACTIONS
(2021)
Article
Chemistry, Multidisciplinary
Bing Wang, Fumian Wang, Yuquan Chen, Chen Peng
Summary: In this paper, a novel fixed-time distributed optimization algorithm is proposed to solve the multi-agent collaborative optimization (MSCO) problem with local inequality constraints, global equation constraints and unknown disturbances. A penalty function method is used to eliminate the local inequality constraints and transform the original problem into a problem without local constraints. Then, a three-stage control scheme is designed to achieve a robust fixed-time convergence, including a fixed-time reaching law, a suitable interaction strategy, and a fixed-time gradient optimization algorithm for the multi-agent system.
APPLIED SCIENCES-BASEL
(2023)
Article
Operations Research & Management Science
Xinqiang Qian, Kai-Rong Wang, Xiao-Bing Li
Summary: The paper establishes necessary optimality conditions for a nondifferentiable multiobjective interval-valued optimization problem and derives the equivalence of weakly LU efficient solutions between the primal problem and its penalized counterpart. This equivalence is established under certain conditions and examples are provided to illustrate the advantages of the results in some cases.
Proceedings Paper
Green & Sustainable Science & Technology
Lucas Do Carmo Yamaguti, Juan M. Home-Ortiz, Mahdi Pourakbari-Kasmaei, Jose Roberto Sanches Mantovani
Summary: This paper proposes a model and solution technique for solving the decentralized optimal power flow problem considering prohibited operation zones. A matheuristic algorithm is used to handle the integer variables, while a non-linear optimization solver is used for the continuous variables. The effectiveness of the model is validated through experiments.
2022 IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2022 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE)
(2022)
Article
Chemistry, Multidisciplinary
Dengshan Huang, Yulin Tang, Qisheng Wang
Summary: This paper proposes a general calculation method based on penalty function and weighted observation value to solve the EIV model with equality and inequality constraints, which is easy to understand and implement. The results from validation examples show that this method is effective and feasible.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Tadeusz Antczak
Summary: This paper considers the convex nonsmooth optimization problem with fuzzy objective function and both inequality and equality constraints. The Karush-Kuhn-Tucker necessary optimality conditions are proved and the exact l(1) penalty function method is used to solve the problem.
Article
Automation & Control Systems
C. U. Solis, J. B. Clempner, A. S. Poznyak
Summary: This paper proposes an online constrained extremum-seeking approach for an unknown convex function with unknown constraints, formulating the problem using the penalty method and developing an extremum seeking algorithm. By employing standard deterministic Integral Sliding Mode Control to reject undesirable uncertainties and perturbations, and then applying gradient decedent technique to compensate for unknown dynamics.
INTERNATIONAL JOURNAL OF CONTROL
(2021)
Article
Mathematics
Jia Liu, Xianjia Wang
Summary: This paper presents an iterative algorithm to find the optimal incentive mechanism for the principal-agent problem under moral hazard with infinite agent action profiles and observable results. The algorithm transforms the problem into a semi-infinite programming problem with infinite incentive-compatibility constraints and uses an exterior penalty function method to find the optimal solution. The convergence of the algorithm is illustrated, providing the optimal incentive mechanism for the principal-agent problem in cases with infinite incentive-compatibility constraints under moral hazard.
ACTA MATHEMATICA SCIENTIA
(2021)
Article
Agriculture, Multidisciplinary
Sonia Cristina Poltroniere, Angelo Aliano Filho, Amanda Suellen Caversan, Antonio Roberto Balbo, Helenice de Oliveira Florentino
Summary: The main products obtained from sugarcane for the global market are sugar, ethanol, and energy. With limited space for expansion in sugarcane cultivation in Brazil, a new type of cane called energy-cane with higher fiber content has been developed. Meeting the demand for sugar, ethanol, and energy products requires planting both energy-cane and sugarcane, posing challenges in planning and decision-making in the energy-sugar sector.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Engineering, Aerospace
Leonardo Murilo Nepomuceno, Roberto Gil Annes da Silva, Alejandro Sobron, Petter Krus, David Lundstrom
Summary: This study investigates the estimation of basic aerodynamic characteristics of a complex configuration using low-cost equipment and prototyping methods. The results highlight the limitations of commonly used aerodynamic methods and demonstrate the importance of physical models in reducing uncertainty.
AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Jessica A. Delgado, Edmea C. Baptista, Antonio R. Balbo, Edilaine M. Soler, Diego N. Silva, Andre C. P. Martins, Leonardo Nepomuceno
Summary: The Optimal Reactive Power Flow (ORPF) problem is an important computational tool in power system planning and operation. The mixed-discrete version of the problem (DORPF) aims to minimize transmission losses while meeting power demand and operational limits. Existing methods for solving the DORPF problem have their limitations, such as poor matrix-conditioning and lack of infeasibility detection. This paper proposes an integration of optimization approaches that address all these issues, including the use of a primal-dual penalty-interior-point method and a sinusoidal penalty function method. Numerical tests confirm the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Economics
Rodolfo Rodrigues Barrionuevo Silva, Andre Christovao Pio Martins, Edilaine Martins Soler, Edmea Cassia Baptista, Antonio Roberto Balbo, Leonardo Nepomuceno
Summary: This paper discusses the energy procurement problem in hydro-dominated markets and proposes a Price Scenario Generation model to estimate future energy prices. The estimated prices are then used in the Energy Procurement Model for Hydrothermal Systems to calculate optimal procurement decisions. The study reveals correlations between hydro and economic variables.
Article
Engineering, Electrical & Electronic
Rafael R. Souza, Antonio R. Balbo, Andre C. P. Martins, Edilaine M. Soler, Edilaine M. Baptista, Diego N. Sousa, Leonardo Nepomuceno
Summary: This paper proposes a solution approach for Stochastic Optimal Power Flow (SOPF) models under uncertainty in wind power generation. By obtaining exact analytical expressions for the derivatives of wind power costs and handling the non-differentiability of thermal costs, the equivalent SOPF model is reformulated as a differentiable nonlinear programming problem. A modified log-barrier primal-dual interior/exterior-point method is proposed to solve the equivalent SOPF model, which outperforms meta-heuristic approaches in terms of computation times and optimality. The proposed method is applied to various power systems and shows significant improvements.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Tiago Gomes Cabana, Edmea Cessia Baptista, Edilaine Martins Soler, Andre Christovao Pio Martins, Antonio Roberto Balbo, Leonardo Nepomuceno
Summary: The Residual Demand Curve (RDC) has traditionally been obtained by the difference between the inverse functions of aggregated demand and supply, but it cannot directly represent constraints related to complex-bid markets. This paper interprets the traditional RDC as an optimization model and proposes Optimization-Based Residual Demand (OBRD) models that can represent complex-bid markets. We show that under certain conditions, the RDC obtained by the OBRD model is equivalent to that provided by traditional methods. Results demonstrate that the RDCs obtained by the OBRD model are significantly more accurate for complex-bid markets.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Matheus Pereira Vieira, Andre Christovao Pio Martins, Edilaine Martins Soler, Antonio Roberto Balbo, Leonardo Nepomuceno
Summary: This paper proposes a novel robust market clearing procedures (MCP) model to account for the uncertainties associated with clean and renewable energy sources. By optimizing energy and reserves, the model improves real-time security and reliability, with only slight impacts on social welfare function, even in high levels of wind power penetration.
Proceedings Paper
Engineering, Industrial
Ricardo B. N. M. Pinheiro, Tiago G. Cabana, Antonio R. Balbo, Leonardo Nepomuceno
Summary: The PPI approach proposed in this paper offers a new way to handle FOZ constraints in Economic Dispatch with Forbidden Operation Zones (ED-FOZ) problems, allowing for the application of efficient gradient-based optimization methods. Numerical results show that this method efficiently solves ED-FOZ problems with acceptable computation times involving a large number of generating units and FOZ.
2021 14TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRY APPLICATIONS (INDUSCON)
(2021)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)