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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lai, Shengzhi Zhao, Peize Li, Ning Yu, Weichuan The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong The Hong Kong University of Science and Technology Interdisciplinary Programs Office Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China The Hong Kong University of Science and Technology Division of Life Science Hong Kong

ISBN: (纸本)9798350386226

Characterizing post-translational modifications (PTMs) is crucial due to their vital role in regulating cellular activities. Current database search methods for peptide identification and PTM characterization face the challenge of exponentially increasing number of PTM combinations. Consequently, these methods resort to enumerating a small fraction of the PTM combinations. However, based on our analysis, more than 99% of the PTM combinations (up to three PTMs) are infeasible as their total mass does not match the mass shift between the precursor mass and theoretical mass. Enumerating those infeasible combinations is unnecessary and would lead to unsatisfactory performance. To address this issue, we propose a two-step method for characterizing multiple PTMs with a reduced search space of feasible PTM combinations only. The method first uses a mixed integer linear program (MILP) to collect feasible peptide sequences and PTM mass combinations. Then, these feasible peptides are exhaustively examined to find the optimal one. We conducted comprehensive experiments to evaluate the method's performance. When applied to a soybean data set, our method has better performance than a representative tag-based search engine. The effectiveness of search space reduction and the sensitivity and precision of our method are demonstrated. © 2024 IEEE.

关键词： mixed-integer linear programming

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A Bi-Level Optimization Model for Citizen Energy Communities Towards Sustainable Development: A Stackelberg Game Theory Approach 9

A Bi-Level Optimization Model for Citizen Energy Communities...

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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Mobahseri, Sahar Rashidinejad, Masoud Abdollahi, Amir Mollahassanipour, Mojgan Dorahaki, Sobhan Shahid Bahonar University of Kerman Department of Electrical Engineering Kerman Iran University of Sistan and Baluchestan Faculty of Electrical and Computer Engineering Zahedan Iran

ISBN: (纸本)9798350329797

This paper presents a Stackelberg game bi-level decision-making framework for optimizing the operation of multi-Citizen Energy Communities (CECs) in an energy market. The framework considers the interaction between the distribution grid and downstream CECs, with the objective of maximizing the profit of the distribution company (DISCO) and reducing pollution at the upper level, and minimizing the cost of CECs and reducing carbon emissions at the lower level. The proposed nonlinear bi-level model is transformed into a mixed-integer linear programming (MILP) model using Karush-Kuhn-Tucker (KKT) conditions and duality theory. The effectiveness of the model is evaluated using a distribution test system. This paper highlights the importance of CECs in achieving a green and sustainable society, and provides a practical and efficient solution for optimizing their operation in an energy market. © 2024 IEEE.

关键词： mixed-integer linear programming

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Proactive Sequential Phase Swapping Scheduling for Distribution Systems with a Finite Horizon 16

Proactive Sequential Phase Swapping Scheduling for Distribut...

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16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024

作者： Sun, Hongbo Kosanic, Miroslav Kawano, Shunsuke Raghunathan, Arvind Kitamura, Shoichi Takaguchi, Yusuke Mitsubishi Electric Research Labs CambridgeMA02139 United States Massachusetts Institute of Technology Dept. of Electrical Engineering CambridgeMA02139 United States Mitsubishi Electric Corporation Advanced R&d Technology Center Amagasaki661-8661 Japan

ISBN: (纸本)9798350386127

As renewable energy sources and plug-in electric vehicles increasingly penetrate power distribution systems, phase imbalance becomes more prevalent, posing significant challenges for electric utilities. This paper proactively tackles this issue by implementing phase swapping sequentially at strategic time steps and locations to improve long-term operational cost savings within a given scheduling horizon. An offline data-driven phase-swapping scheduler is developed, mimicking multi-step phase-swapping optimization through an imitation learning framework via supervised learning using random forest regression. This scheduler determines the time steps and associated loads and generations requiring phase swapping. The phase swapping over the finite scheduling horizon is modeled as a multi-step mixed-integer nonlinear programming problem, utilizing a generic branch-based radial load flow model that considers both wye and delta connections for loads and generations. For an upcoming scheduling horizon, the scheduler, based on forecasted generation, load, and price profiles, first identifies the time steps and loads/generations requiring phase swapping. Subsequently, the phase swapping actions for each determined time step are solved sequentially using a mixed-integer nonlinear optimization model with phase swapping at the determined locations and with determined number of swaps. Numerical experiments are conducted on the modified IEEE 13-node test feeder to validate the effectiveness of the proposed approach. © 2024 IEEE.

关键词： mixed-integer linear programming

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Aggregated Energy Management of Multiple Factories that Purchased Green Energy Collectively 24

Aggregated Energy Management of Multiple Factories that Purc...

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24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Yao, Leehter Ku, Cheng-Yu Teo, J.C. National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350355185

This paper proposes an aggregated energy management of multiple factories that purchased green energy collectively. These factories purchase green energy collectively to get a better price. The green energy is procured for each time step throughout the consumption day in a day ahead manner. On the consumption day, the green energy provider feeds green energy into the power grid at each time step, following the quantities that were purchased for those specific time intervals. The aggregated energy management system optimally dispatches the electrical power of the factories in an aggregated manner based on the purchased green energy profiles to maximize the consumption of the purchased green energy. A mixed integer linear programming (MILP) formulation is developed to carry out the aggregated energy management. To overcome the high computation time associated with conventional MILP, a linear programming relaxation technique is employed. This technique relaxes the binary variables, allowing them to take any value between 0 and 1, which enables the optimization problem to be solved in a very short time. After solving the optimization problem, the values of the variables are corrected back to binary values using a delicate method to ensure the feasibility of the solution. © 2024 IEEE.

关键词： mixed-integer linear programming

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A Hybrid Discrete Jaya Algorithm Based on Gene Expression programming for Heterogeneous Multi-Satellites Cooperative Observation 2

A Hybrid Discrete Jaya Algorithm Based on Gene Expression Pr...

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2nd International Conference on Artificial Intelligence and Automation Control, AIAC 2024

作者： Yang, Junxiang He, Lei College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798331529147

In disaster observation, disasters with different characteristics have different degrees of demand for the selection of observation satellites, which directly affects the imaging quality, specifically involving the matching problem between mission types and satellite types. In this paper, for this Heterogeneous multi-Agile Earth Observation Satellites Scheduling Problem (HAEOSSP), a mixed integer linear programming model is established, and the time-dependent transition time and task matching are taken into account. To solve HAEOSSP, a Hybrid Discrete Jaya algorithm based on Gene Expression programming (HD-Jaya-GEP) method is designed to evolve task assignment rule. Coded with GEP, the Jaya algorithm is discretized by a neighborhood-based strategy, while the single-satellite task scheduling is solved by a mature meta-heuristic algorithm. Finally, simulation experiments are performed to evaluate the generalization of HD-Jaya-GEP, which validates the effectiveness and capability. © 2024 IEEE.

关键词： mixed-integer linear programming

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Algorithm for the robust shortest path problem with multimodal transportation 8

Algorithm for the robust shortest path problem with multimod...

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8th International Conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Guo, Jinzuo Liu, Tianyu Song, Guopeng Guo, Bo College of Systems Engineering National University of Defense Technology Changsha410073 China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

This paper explores the challenges of finding robust shortest paths in multimodal transportation networks. This study develops a robust mixed-integer programming model, taking into account practical constraints related to parameter uncertainty in transportation times. We propose a modified Dijkstra algorithm that integrates parameter uncertainty with multimodal transportation. The findings indicate that the proposed approach is capable of producing optimal solutions for the robust shortest path problem involving multimodal transportation in the context of time uncertainty, highlighting its practical relevance. © 2024 SPIE.

关键词： mixed-integer linear programming

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Improved Bounds of integer Solution Counts via Volume and Extending to mixed-integer linear Constraints 30

Improved Bounds of Integer Solution Counts via Volume and Ex...

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30th International Conference on Principles and Practice of Constraint programming, CP 2024

作者： Ge, Cunjing Biere, Armin National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China University of Freiburg Germany

ISBN: (纸本)9783959773362

Solution counting and solution space integration over linear constraints are important problems with many applications. Previous works addressed either only counting integer points in polytopes (integer counting) with integer variables or alternatively only computing the volume of polytopes (solution space integration) on variables over the reals, including approximating the integer count via a polytope's volume. We are not aware of a non-trivial algorithm which addresses the mixed case, where linear constraints are over mixed integer and real variables. In this paper, we propose a new randomized algorithm to approximate guarantees (bounds) of integer solution counts. Then we present upper and lower bounds for solution space integration over mixed-integer linear constraints. Thus, proposed algorithms can be extended to mixed-integer cases as well. The experiments show that approximations are often very close to exact results in practice, and bounds approximated by our algorithm are often tight and useful. © Cunjing Ge and Armin Biere.

关键词： mixed-integer linear programming

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Optimal Decision Tree Design for Near Real-Time Control of Battery Energy Storage Systems 26

Optimal Decision Tree Design for Near Real-Time Control of B...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Handel, Katrin Anitha, Angellina Ebenezer Rigo-Mariani, Remy Schulte, Katrin Haubrock, Jens University of Applied Sciences and Arts Bielefeld Institute for Technical Energy Systems Bielefeld Germany University Grenoble Alpes CNRS Grenoble INP G2Elab Grenoble France

ISBN: (纸本)9798331532833

The increasing numbers of distributed energy resources (DER) incur new challenges for the energy supply due to the volatilities and uncertainties of renewable energies. To utilize the benefits of DER, a combination with storage systems and intelligent controls is necessary. Commonly used control methods are based on Model Predictive Control (MPC) with forecast and optimization, which require computational capabilities at the DER on a level, on which the distribution system operator (DSO) has no access. This paper proposes a control algorithm based on data, which combines a model-based and an artificial intelligence (AI) based approach to utilize benefits from both methods while compensating for their drawbacks. mixed-integer Quadratic programming (MIQP) is used to generate training and testing data for a decision tree (DT). The investigation of the optimal composition of the training data and the optimal architecture of the DT are the main focus of this paper. © 2024 IEEE.

关键词： mixed-integer linear programming

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Optimised architecture design for an MEA power distribution system considering load profile and fault-tolerance

Optimised architecture design for an MEA power distribution ...

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IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles / International Transportation Electrification Conference (ESARS-ITEC)

作者： Wang, Xin Atkin, Jason Yeoh, Seang Bozhko, Serhiy Univ Nottingham Dept Elect & Elect Engn Fac Engn Nottingham England Univ Nottingham Computat Optimisat & Learning Lab Sch Comp Sci Nottingham England

ISBN: (纸本)9798350346893

With the development of More Electric Aircraft (MEA), novel power distribution systems (PDSs) are required to meet significantly increased electrical load demands onboard. Nevertheless, newer designs must comply with the strict flight operational and safety requirements, while achieving optimisation aims, such as reducing aircraft weight and energy consumption. This paper proposes a two-stage optimisation design method for a novel PDS investigated for MEA. Two optimal power scheduling-based models for minimising power losses and PDS weight are formulated in mixed-integer linear programming (MILP). The first model considers varying load demands and is solved for all flight stages simultaneously in the first design stage for weight minimisation. The obtained architecture is further optimised for all faulty scenarios and selected typical fight stages simultaneously in the second design stage to improve the PDS reliability. Consequently, a low power loss, light weighted, and fault-tolerant PDS architecture is obtained. A case study is provided to exemplify the improvement in the PDS by adopting the proposed method.

关键词： More electric aircraft power distribution system architecture design two-stage optimisation mixed-integer linear programming

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Sustainable Short-Term Production Planning Optimization

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SN Computer Science 2023年第6期4卷 824页

作者： Zanella, Fernando Vaz, Clara Bento Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança (IPB) Campus de Santa Apolónia Bragança 5300-253 Portugal Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança (IPB) Campus de Santa Apolónia Bragança 5300-253 Portugal Centre for Management and Industrial Engineering (CEGI/INESC TEC) Porto Portugal

This study proposes a framework for short-term production planning of a Portuguese company operating as a tier 2 supplier in the automotive sector. The framework is intended to support the decision-making process regarding a single progressive hydraulic press, which is used to manufacture cold-stamped parts for exhaust systems. The framework consists of two sequential levels: (1) a mixed-integer linear programming (MILP) model to determine the optimal production quantities per week while minimizing the total cost;(2) a dynamic production sequencing rule for scheduling operations on the hydraulic press. The two levels are combined and implemented in Excel, where the MILP model is solved using the Solver add-in, and the second level uses the optimal production quantities as inputs to determine the production sequence using a dynamic priority rule. To validate the framework, a proposed optimal plan was compared to a real plan executed by the company, and it was found that the framework could save up to 22.1% of the total cost observed in reality while still satisfying demand. To address uncertainties, the framework requires a rolling weekly planning horizon. © 2023, The Author(s).

关键词： mixed-integer linear programming Responsible production Short-term production planning

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