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18th International Symposium on Information Theory and Its Applications, ISITA 2024

作者： Shibayama, Naoki Igarashi, Yasutaka Tokyo University of Science 2641 Yamazaki Noda Chiba278-8510 Japan

ISBN: (纸本)9784885523519

The block cipher LBC-3, which was proposed by Nyssanbayeva et al. in 2022, has a 64-bit block size, an 80-bit secret key size, and the number of rounds is 20. The designer investigated the avalanche effect of LBC-3, and Yasushi et al. evaluated the security against differential and linear cryptanalysis. On the other hand, it has not been evaluated the security against integral cryptanalysis, which is one of the most powerful cryptanalysis techniques on block cipher. In this paper, we apply integral cryptanalysis to LBC-3. By establishing the mixed integer linear programming (MILP) model based on bit-based division property, we discovered the 18-round integral characteristic, the longest integral characteristic of LBC-3. Then, based on the 16-round characteristic newly found, we show that the integral attack on full-round LBC-3 is more efficiently possible with 2{50.8 data and 2{51.4 times of encryption. © 2024 The Institute of Electronics, Information and Communication Engineers.

关键词： mixed-integer linear programming

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A Distributed Algorithm for Large-Scale Multi-Agent MINLPs 63

A Distributed Algorithm for Large-Scale Multi-Agent MINLPs

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Dong, Sheng Xia, Weiguo Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9798350316339

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting mixed-integer Nonlinear programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in mixed-integer linear programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations. © 2024 IEEE.

关键词： mixed-integer linear programming

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Learning to Branch in Combinatorial Optimization with Hybrid Convolutional and Attentional Graph Neural Networks

Learning to Branch in Combinatorial Optimization with Hybrid...

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2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024

作者： Zhang, Xilin Gu, Shenshen School of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798331504755

Solving combinatorial optimization problems holds significant practical importance, and many such problems can be transformed into MILP problems. Currently, Branch-and-Bound (B&B) is a typical method for solving mixed-integer linear programming (MILP) problems. In this paper, we propose a hybrid convolutional and attentional graph neural network to learn the variable selection policy in B&B. The model combines graph convolutional and graph attention mechanisms, achieving more efficient node embeddings and reducing computational time. The mathematical model of MILP is transformed into a bipartite graph structure, which effectively extracts features. The B&B solving process is mapped to a Markov decision process, and imitation learning is used to train the model. By solving a series of combinatorial optimization problems that can be transformed into MILP problems, we validate that our model can improve B&B. Experimental results show that our model outperforms state-of-the-art branching machine learning methods and generalizes to problems of different scales. © 2024 IEEE.

关键词： mixed-integer linear programming

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Contrastive Predict-and-Search for mixed integer linear Programs 41

Contrastive Predict-and-Search for Mixed Integer Linear Prog...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Taoan Ferber, Aaron Zharmagambetov, Arman Tian, Yuandong Dilkina, Bistra Department of Computer Science University of Southern California Los AngelesCA United States Department of Computer Science Cornell University IthacaNY United States Menlo ParkCA United States

mixed integer linear programs (MILP) are flexible and powerful tools for modeling and solving many difficult real-world combinatorial optimization problems. In this paper, we propose a novel machine learning (ML)-based framework ConPaS that learns to predict solutions to MILPs with contrastive learning. For training, we collect high-quality solutions as positive samples. We also collect low-quality or infeasible solutions as negative samples using novel optimization-based or sampling approaches. We then learn to make discriminative predictions by contrasting the positive and negative samples. During testing, we predict and fix the assignments for a subset of integer variables and then solve the resulting reduced MILP to find high-quality solutions. Empirically, ConPaS achieves state-of-the-art results compared to other ML-based approaches in terms of the quality of and the speed at which solutions are found. Copyright 2024 by the author(s)

关键词： mixed-integer linear programming

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Power Grid Dispatch Modeling for the Prospective Current Trends Scenario for the ERCOT Service Area of Texas

Power Grid Dispatch Modeling for the Prospective Current Tre...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Das, Jani Tutay, Gizem Toplu Gulen, Gurcan Young, Michael Bureau of Economic Geology University of Texas AustinTX United States University of Texas at Austin Department of Mechanical Eng TX United States

ISBN: (纸本)9798331541125

The management of power systems depends on economic dispatch (EC) and constrained unit commitment (UC). A way to schedule generation to meet electricity demand (reliability), with a time-varying cycle (hourly, daily, weekly) at a minimum cost is by short term optimization using mixed integer linear program (MILP). This determines the optimum resource generation pattern for a given time based on resource characteristics, network topology and demand levels. This study presents a generation characteristics and cost prediction model for 2039, using the AURORA grid modeling software, and environmental impacts stemming from generation. The model simulates hourly system operation by assessing and analyzing resource adequacy and the impact of resource changes on system operation over time, while considering transmission congestion. The resource stack is based on the 'current trends' scenario published by ERCOT and the stack is varied to reflect different generation mixes. The results show congestion increases with penetration of wind/solar higher than 'current trends'. Increases in battery storage and transmission capacities mitigate congestion. The environmental impacts show the generation wise contribution for the modeled year, pointing to where technology and material substitutions could mitigate impacts, wherever possible. © 2025 IEEE.

关键词： mixed-integer linear programming

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Economic Optimial Scheduling Strategy for Thermal Power Units Under Deep Peak Regulation Considering Wind Power Integration 9th

Economic Optimial Scheduling Strategy for Thermal Power Unit...

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9th Purple Mountain Form on Smart Grid Protection and Control, PMF 2024

作者： Yang, Yuge Bao, Yuqing NARI School of Electrical and Automation Engineering Nanjing Normal University Nanjing210046 China

ISBN: (纸本)9789819616701

To expedite the energy transformation of the power system, the involvement of thermal power units (TPUs) in deep peak regulation (DPR) has become an effective strategy for enhancing the utilization of renewable energy. However, the optimal scheduling strategy of TPUs participating in DPR is characterized by multivariate, complex, and nonlinear mixed-integer programming problem. To address the problems of existing solution methods frequently falling into the local optimal solution and ignore the nonlinear characteristics and lead to inaccurate approximation results, an optimal scheduling model based on the multi-condition non-uniform piecewise linearization method was proposed, and the multi-condition objective function was converted into the form of non-piecewise linear equations by introducing decision variables, and the model was converted into a mixed integer linear programming (MILP) problem by using the non-uniform piecewise linearization method based on the optimal power segmentation interval of the multi-condition DPR model. Ultimately, the proposed method’s reliability and precision are confirmed through the analysis of various examples. © State Grid Electric Power 2025.

关键词： mixed-integer linear programming

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Small-scale LNG supply chain optimization for LNG bunkering in Turkey

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COMPUTERS & CHEMICAL ENGINEERING 2022年 162卷

作者： Doymus, Mehmet Sakar, Gul Denktas Yildiz, Seyda Topaloglu Acik, Abdullah Dokuz Eylul Univ Maritime Fac Dept Marine Transportat Engn Tinaztepe Campus TR-35390 Izmir Turkey Dokuz Eylul Univ Maritime Fac Dept Logist Management Tinaztepe Campus TR-35390 Izmir Turkey Dokuz Eylul Univ Engn Fac Dept Ind Engn Tinaztepe Campus TR-35390 Izmir Turkey Dokuz Eylul Univ Maritime Fac Dept Maritime Business & Adm Tinaztepe Campus TR-35390 Izmir Turkey

LNG (Liquefied Natural Gas) provides a viable option to comply with emission control measures as an alternative marine fuel. Supply chain optimization is critical for LNG bunkering development in the mar-itime context as it requires high capital-expenditure. This study proposes a model for optimizing the ship-to-ship LNG bunkering supply chain. The related problem is defined as a Multiple Period Maritime Fleet Size and Routing Problem. The proposed mathematical model has been solved for various demand scenarios obtained by multiple regression and forecasting as a case study of ship-to-ship LNG bunker deliveries in Turkey. The model presents an optimal solution as a tactical and strategic decision-making tool, finds the number and size of the LNG bunker barges and the optimum allocation of the barges and the distribution network within a ship-to-ship bunkering framework. Moreover, it provides a commercial framework for shipowners and suppliers by determining the breakeven point for investment decisions.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Small-scale LNG LNG bunkering LNG as a marine fuel Forecasting Optimization mixed-integer linear programming

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Integrated production and distribution planning for industrial gases supply chains

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COMPUTERS & CHEMICAL ENGINEERING 2022年 161卷

作者： Neiro, Sergio M. S. Madan, Tarun Pinto, Jose M. Maravelias, Christos T. Uberlandia Fed Univ UFU Sch Chem Engn Av Joao Naves de Avila 2121Bloco 5K BR-38408100 Uberlandia MG Brazil Linde PLC Linde Digital Amer Danbury CT 06810 USA Princeton Univ Dept Chem & Biol Engn Princeton NJ USA Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ USA

A challenge in planning the operations of multisite production networks is the need to simultaneously balance production across sites while satisfying customers demand and keeping customer service levels high and overall costs low. Accordingly, we present a planning approach for the simultaneous optimization of production and distribution operations. Specifically, we consider a regional Argon supply chain, comprised of multiple production sites and customers, in which liquid Argon is distributed through tanker trucks under cryogenic conditions. The proposed approach first generates detailed routes and then uses these routes in an integrated model to fully optimize production and distribution. Using a series of case studies, based on industrially inspired data, we show that the proposed approach leads to high quality results for regional networks of real-world sizes.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Coordinated production-distribution plan Inventory routing mixed-integer linear programming Decomposition Industrial gases

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Scheduling of Energy Hub Resources Using Robust Chance-Constrained Optimization

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IEEE ACCESS 2022年 10卷 129738-129753页

作者： Esmaeel Nezhad, Ali Nardelli, Pedro H. J. Sahoo, Subham Ghanavati, Farideh LUT Univ Sch Energy Syst Dept Elect Engn Lappeenranta 53850 Finland Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Univ Aveiro Dept Ind Engn & Management P-3810193 Aveiro Portugal

This paper develops a robust chance-constrained model for handling the uncertainties of generation and consumption in multi-carrier energy hubs. The proposed model incorporates corresponding loading factors for each type of electrical, heating, and cooling loads. This is done to assess the maximum loadability of the whole system. In this respect, the chance-constrained approach is implemented for the feasibility assessment of the operation problem with uncertainties. The uncertainties which are assumed here include the forecast errors of electrical, heating, and cooling load demands, and the volatile solar power generation. The overall problem formulation is developed in the mixed-integer linear programming (MILP) framework. The standard chance-constrained approach is converted to a deterministic optimization model by utilizing the Big M method. The main objective of the proposed model is to maximize the loadability index with uncertainties while addressing the permissible risk index of the decision-maker. The studied energy hub comprises electrical, heating, and cooling loads, and the energy flow technique is adopted in this paper to model the load balance equations. The simulation results are presented for different scenarios while addressing features of the proposed model for the summer and winter seasons. Furthermore, the developed model is evaluated for different scenarios and a comparison is made with the information-gap decision theory (IGDT) method.

关键词： Chance-constrained programming energy hub robust optimization loadability index mixed-integer linear programming

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A mixed rebalancing strategy in bike sharing systems

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ENGINEERING OPTIMIZATION 2022年第7期54卷 1160-1177页

作者： Xu, Guoxun Xiang, Ting Li, Yanfeng Li, Jun Guo, Qiang Hainan Univ Sch Tourism Haikou Hainan Peoples R China Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China

In a bike sharing system (BSS), for various reasons, such as truck fuel load, truck capacity and high customer demand, the number of trucks deployed by the BSS operator is usually insufficient and they cannot balance all of the stations in time. To incentivize users to relocate some of the excess bikes from stations with surplus bikes to bike deficient stations, a mixed rebalancing strategy is proposed. The problem is formulated as a mixed-integer linear programming model. A hybrid genetic algorithm is developed to solve the proposed problem. Computational experiments are performed to illustrate the properties of the problem. The numerical results show that the proposed solution method can yield high-quality solutions in short computing times.

关键词： Bike rebalancing problem mixed rebalancing strategy mixed-integer linear programming hybrid genetic algorithm

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