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MIRROR: Methodological Innovation to Remodel the Electric Loads to Reduce Economic OR Environmental Impact of User

ALGORITHMS 2023年第1期16卷

作者： Chimienti, Michela Danzi, Ivan Impedovo, Donato Pirlo, Giuseppe Semeraro, Gianfranco Veneto, Davide Key4 Srl Via Baione I-70043 Monopoli Italy Omnia24 Srl Piazza Repubbl 19 I-20124 Milan Italy Digital Innovat Srl Via Orabona 4 I-70125 Bari Italy Univ Bari Aldo Moro Dept Comp Sci Via Orabona 4 I-70125 Bari Italy Univ Sch Adv Studies IUSS Pavia Palazzo Broletto Piazza Vittoria 15 I-27100 Pavia Italy

Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standard energy production methodologies is based on the use of renewable sources;however, these methodologies do not produce energy consistently due to weather factors. This results in a significant commitment of the user who must appropriately distribute loads in the most productive time slots. In this paper, a comparison is made between two methods of predicting solar energy production, one statistical and the other meteorological. For this work, a system capable of presenting the scheduling of household appliances is tested. The system is able to predict the energy consumption of the users and the energy production of the solar system. The system is tested using data from three different users, and the mean percentage of consumption reduction is about 77.73%. This is achieved through optimized programming of appliance use that also considers user comfort.

关键词： optimization renewable energy modeling mathematical programming

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CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification

CounterFair: Group Counterfactuals for Bias Detection, Mitig...

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IEEE International Conference on Data Mining (ICDM)

作者： Alejandro Kuratomi Zed Lee Panayiotis Tsaparas Guilherme Dinis Junior Evaggelia Pitoura Tony Lindgren Panagiotis Papapetrou Department of Computer and Systems Sciences Stockholm University Sweden Department of Computer Science and Engineering University of Ioannina and Archimedes/Athena RC Greece

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Counterfactual explanations can be used as a means to explain a models decision process and to provide recommendations to users on how to improve their current status. The difficulty to apply these counterfactual recommendations from the users perspective, also known as burden, may be used to assess the models algorithmic fairness and to provide fair recommendations among different sensitive feature groups. We propose a novel model-agnostic, mathematical programming-based, group counterfactual algorithm that can: (1) detect biases via group counterfactual burden, (2) produce fair recommendations among sensitive groups and (3) identify relevant subgroups of instances through shared counterfactuals. We analyze these capabilities from the perspective of recourse fairness, and empirically compare our proposed method with the state-of-the-art algorithms for group counterfactual generation in order to assess the bias identification and the capabilities in group counterfactual effectiveness and burden minimization.

关键词： Scalability Prevention and mitigation Cost function Turning Minimization mathematical models Diamond Data models Data mining mathematical programming

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A New Node-Shift Encoding Representation for the Travelling Salesman Problem

A New Node-Shift Encoding Representation for the Travelling ...

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2024 1st International Conference on Logistics (ICL)

作者： Menouar Boulif Aghiles Gharbi Department of computer sciences LIMOSE University M'hamed Bougara of Boumerdes Boumerdes Algeria Department of computer sciences University M'hamed Bougara of Boumerdes Boumerdes Algeria

ISBN: (数字)9798331518844

ISBN: (纸本)9798331518851

The encoding representation is a critical component of every approximate solving method, and especially for the evolutionary algorithms. This paper presents a new encoding representation to solve the travelling salesman problem by the genetic algorithm. To assess the performance of the proposed chromosome structure, we compare it with state-of-the-art encoding representations. For that purpose, we use 14 benchmarks of different sizes taken from TSPLIB. Finally, after conducting the experimental study, we report the obtained results and draw our conclusion.

关键词： Vehicle routing Evolutionary computation Benchmark testing Encoding Biological cells Genetic algorithms mathematical programming Logistics

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Modal Synthesis of the Controller of the Underwater Vehicle Motion Control System

Modal Synthesis of the Controller of the Underwater Vehicle ...

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International Conference on Control Systems, mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Tatiana Ezangina Sergey Gayvoronskiy Maxim Mikhailovich National Research Tomsk Polytechnic University Tomsk Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The paper is dedicated to developing a method of maximizing a degree of robust stability of control systems with interval parameters based on a method of mathematical programming and root locus theory. The research resulted in a method of synthesizing a robust controller providing maximal operating speed of interval control systems in the worst operating modes. To prove effectiveness of the method proposed a controller for motion control system of an unmanned underwater vehicle with interval parameters was synthesized. Control quality of the system synthesized was examined with the help of multiparametric interval root locus.

关键词： Motion planning Autonomous underwater vehicles Regulators Robust stability Control systems Stability analysis Polynomials mathematical models Energy efficiency mathematical programming

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A Multi-stage Coordinated Scheduling Method for Heterogeneous Resources in Distribution Systems

A Multi-stage Coordinated Scheduling Method for Heterogeneou...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Jiexing Zhao Qiaozhu Zhai Yuzhou Zhou Lun Yang Xiaohong Guan Systems Engineering Institute Moeklinns Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Quantitative characterization of multidimensional uncertainties for heterogeneous renewable energy resources is a challenging problem. Existing methods typically rely on experiential knowledge and predefined policies. However, some temporal and spatial correlation relationships can not be precisely formulated based on experience, potentially leading to suboptimal solutions. Motivated by these challenges, this paper proposes a data-driven budget uncertainty set generation method to formulate the temporal and spatial correlation relationship for heterogeneous resources in distribution systems. The main idea is to iteratively find cutting planes (budget constraints) by solving straightforward optimization problems. The computational complexity is low. Besides, a multi-stage robust optimization framework is developed to derive an optimal coordinated scheduling policy for various renewable energy resources and storage devices. When uncertainties are observed gradually, decisions are adaptively optimized in a rolling horizon manner. No explicit decision assumption is required and therefore the performance is improved compared with existing methods. Numerical tests are implemented on a modified IEEE 33-bus system, verifying the effectiveness of the proposed method.

关键词： Performance evaluation Couplings Renewable energy sources Uncertainty Correlation Processor scheduling Energy Internet System integration mathematical programming Energy storage

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Optimal design of electrical machines: mathematical programming formulations

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第3期32卷 977-996页

作者： Cafieri, Sonia Liberti, Leo Messine, Frederic Nogarede, Bertrand Ecole Natl Aviat Civile Lab MAIAA Toulouse France Ecole Polytech LIX Palaiseau France ENSEEIHT IRIT Toulouse France ENSEEIHT LAPLACE Toulouse France

Purpose - The purpose of this paper is to investigate the impact of different mathematical formulations of the problem of optimal design of electrical machines on the results obtained using a local optimization solver. The aim is to investigate the efficiency and reliability of standard local solvers when handling different mathematical formulations. This could provide guidelines for designers in practical engineering applications. Design/methodology/approach - The paper proposes six equivalent mathematical formulations of the optimal design problem of a slotless permanent-magnet electric rotating machine. The authors investigate the impact of these different mathematical formulations on the results obtained using a local optimization solver which is well-known in the engineering community: MatLab's fmincon function. The paper first computationally compares the six proposed formulations with a fixed value for the number of pole pairs p, that gives continuous optimization problems, then discusses some results when p is free on three mixed-integer formulations. Findings - The paper shows that, even though the considered formulations are mathematically equivalent, their numerical performances are different when an optimization solver, such as the one proposed by MatLab in fmincon, is used. Thus, the designer must take care about the formulation of the design problem in order to make more efficient the use of these kind of algorithms. Originality/value - In the context of engineering applications, one usually resorts to well known and easy to use optimization solvers. The same optimization problem can be often formulated in different ways. Furthermore, the formal description of optimization problems has an impact on the applicability and efficiency of the corresponding solution methods. This is usually not taken into account when optimization solvers are exploited. The originality of this paper is in building on the theory of reformulations in mathematical optimization

关键词： Optimal design Analytical model Formulation Modelling Local optimization Numerical methods Inverse problem Electrical machine mathematical programming

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Application of Machine Algorithms for Classification and Formation of the Optimal Plan 5

Application of Machine Algorithms for Classification and For...

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5th International Conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Boyko, Nataliya Hlynka, Rostyslav Lviv Polytech Natl Univ Prof St 1 UA-79013 Lvov Ukraine

The paper presents three methods for data classification and finding the optimal plan: the study of the quadratic programming problem, the double problem and the Support Vector Machine method. It is known that linear programming is used to solve resource allocation problems. Also, its purpose is widely used to determine the highest profit or lowest cost, inventory management, the formation of an optimal transportation plan or to determine research, and so on. An important approach to the application of linear programming problems is the use of the duality principle, which is methodologically related to the theory of systems of dependent inequalities. This aspect better explains the concept of duality in linear programming problems with general mathematical rigor.

关键词： Data Mining mathematical programming Linear programming Nonlinear programming Quadratic programming Problem of the Quadratic programming Support Vector Machine

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Guest Editorial

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2021年第4期16卷 17-18页

作者： Feng, Liang Wang, Handing Ong, Yew-Soon Tan, Kay Chen Jin, Yaochu Chongqing Univ Chongqing Peoples R China Xidian Univ Xian Peoples R China Nanyang Technol Univ Singapore Singapore Hong Kong Polytech Univ Hong Kong Peoples R China Univ Surrey Surrey England

Evolutionary Algorithms (EAs) are nature-inspired population-based search methods which work on Darwinian principles of natural selection. Due to their strong search capability and simplicity of implementation, EAs have been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional mathematical programming approaches, such as linear programming, quadratic programming, and convex optimization. Despite the great success enjoyed by EAs, it is worth noting that existing EA solvers usually conduct the search process from scratch, regardless of how similar the new problem encountered is to those already solved in the past. Therefore, conventional EAs do not learn from previous problems and the search capabilities of the EA solvers do not automatically grow with problem-solving experiences. However, in reality, since problems seldom exist in isolation, solving one problem may thus yield useful information for solving other related problems. In the literature, there is a growing interest in conducting research on evolutionary transfer optimization (ETO) in recent years: a paradigm that integrates EA solvers with knowledge learning and transfer across related domains to achieve better optimization efficiency and performance.

关键词： Special issues and sections Evolutionary computation Search problems Quadratic programming Problem-solving mathematical programming Linear programming Convex functions

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Hiring People With Disabilities: Work Assignment Models for Decision Support

Hiring People With Disabilities: Work Assignment Models for ...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Michele Blasiak Julieta Oberti Jimena Strechia Héctor Cancela Patricia Quintana Carrera de Ingeniería de Producciçn Facultad de Ingeniería Universidad de la República Montevideo Uruguay Facultad de Ingeniería Instituto de Computaciçn Universidad de la República Montevideo Uruguay Facultad de Ingeniería Instituto de Ingeniería Mec´anica y Producciçn Industrial Universidad de la República Montevideo Uruguay

ISBN: (数字)9798331540975

ISBN: (纸本)9798331540982

Nowadays, the assimilation of workers with disabilities is recognized as both a challenge and an opportunity in terms of staff diversity and of corporate social responsibility of different organizations. In this context, achieving a fair and efficient task distribution is an essential aspect of guaranteeing the effective and full involvement of all the workers, independently from their cognitive and physical capacities. A task distribution model is a method for assigning and managing tasks within a work team, organization or group of persons, usually aiming to optimize productivity and efficiency. The main objective is to ensure that the correct tasks are assigned to the appropriate persons, taking into account their abilities, experience and current work charge. Task distribution models are particularly relevant when including people with disabilities, where they must be flexible and adaptable enough to guarantee the full integration of all the team members. In this work, we present a mathematical programming model for task distribution in the context of hiring people with disabilities. We discuss a practical case study at the Intendencia de Montevideo (IdeM - the municipality of the city of Montevideo), as part of a project for improving the integration of people with disabilities in its staff (currently the IdeM staff only includes about 1.5% of employees with disabilities; while applicable laws state that this percentage should raise to at least 4%). We present the results of applying the mathematical programming model, comparing the results against manual assignments. Different objective functions are also taken into account, as the various stakeholders also have specific goals that are important to take into account. The results show that mathematical programming models are an effective tool that can be used to support decision making and improve the integration of workers with disabilities in an organization.

关键词： Productivity Adaptation models Computational modeling Urban areas Organizations People with disabilities mathematical models Stakeholders mathematical programming Context modeling

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Exact reoptimisation under gradual look-ahead for operational control in production and logistics

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2023年第1期10卷

作者： Dunke, Fabian Nickel, Stefan Karlsruhe Inst Technol Inst Operat Res Discrete Optimizat & Logist Karlsruhe Germany Karlsruhe Inst Technol Inst Operat Res Discrete Optimizat & Logist Kaiserstr 12 D-76131 Karlsruhe Germany

In decision making, information about the future typically comes in different uncertainty degrees. For the near-future, information is often assumed as deterministic;online optimisation with look-ahead deals with such situations. The more distant future, contrarily, is usually afflicted with uncertainty. The farther in the future, the more pronounced the degree of uncertainty;online optimisation with gradual look-ahead considers such forecasting information. Operational tasks in production and logistics are often coined by mixtures of these information types. We propose a methodology based on mathematical programming (MP) which combines information horizons for the near and more distant future to solve online optimisation problems with gradual look-ahead by exact reoptimisation. To this end, we investigate how MP formulations for offline problems are transferred to the online case by adapting them to gradual look-ahead information. Further, we employ a sampling-based robustification to account for long-term uncertainty. In numerical experiments on online versions of combinatorial problems which lie at the heart of many operational problems from production and logistics (packing, routing, lot sizing, scheduling), we illustrate how the methodology can be applied in practice. Moreover, the analysis allows to establish a sample-based look-ahead and forecasting value indicating the benefit of improving forecasting capabilities.

关键词： Online optimisation gradual look-ahead mathematical programming production and logistics

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