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arXiv 2024年

作者： Yang, Songqiying Adil, Ania Feron, Eric KAUST Saudi Arabia

With advancements in technology, commercial aircraft formation flying is becoming increasingly feasible as an efficient and environmentally friendly flight method. However, gaps remain in practical implementation, particularly in collision avoidance for aircraft formations. Existing avoidance algorithms mainly focus on single aircraft or UAV swarms, lacking comprehensive studies on the complex interactions within commercial aircraft formations. To address this, this paper proposes an optimization model designed to generate safe and effective collision avoidance solutions for commercial aircraft formations. This model demonstrates avoidance paths for formations facing intruders and offers insights for developing formation flight strategies. This study explores response strategies for commercial aircraft formations encountering intruders, considering the difficulty of pilot maneuvers. The findings provide theoretical support for the practical implementation of commercial formation flying and may advance the adoption of this technology. © 2024, CC BY.

关键词： mixed-integer linear programming

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A mixed-integer linear programming Approach to Optimize Return Strategies in Remanufacturing System 10

A Mixed-Integer Linear Programming Approach to Optimize Retu...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Lahmar, Houria Dahane, Mohammed Mousse, Kinza Nadia Haoues, Mohammed Univ Batna II Fac Technol Batna 05001 Algeria Univ Lorraine LGIPM F-57000 Metz France

ISBN: (纸本)9798350373981;9798350373974

This study addresses a production planning problem in a remanufacturing system, where the objective is to determine the amount of used products of different qualities that need to be recovered in order to produce remanufactured products that meet customer demands, while minimizing total costs, which include used product return costs, remanufacturing costs, start-up costs and storage costs. To achieve this objective, a new mixed-integer linear programming (MILP) model is developed for this problem. This model is evaluated through computational experiments conducted under different scenarios, considering limited or unlimited quantities of used products and different period lengths. The results of this study illustrate the effectiveness and efficiency of the proposed model in minimizing costs and meeting customer demands of remanufactured products.

关键词： mixed-integer linear programming

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Life-of-mine optimization for integrated open stope development and production scheduling using a mixed-integer linear programming framework

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MINING TECHNOLOGY-TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY 2023年第2期132卷 106-120页

作者： Appianing, Emmanuel John Andrew Ben-Awuah, Eugene Pourrahimian, Yashar Laurentian Univ Bharti Sch Engn & Comp Sci Min Optimizat Lab Sudbury ON Canada Univ Alberta Min & Rock Sci Dev & Innovat Lab Dept Civil & Environm Engn Sch Min & Petr EngnMin Optimizat Lab Edmonton AB Canada

Extraction and development sequences must be scheduled strategically in order to maximize the life of mine. The complexities and computational difficulties associated with underground mine planning makes it challenging and open to further research. This paper presents a mathematical programming framework based on mixed-integer linear programming (MILP) formulation for integrated open stope development and production scheduling. Additionally, the MILP model incorporates backfilling, operational levels control, and stope extraction duration control during production scheduling. The MILP model generated a net present value of $244.7 M and determined the capital, ventilation, operational, and ore pass development schedules as well as mining and processing schedules for a gold project with 25 years mine life. A total of 2.48 Mt of material was extracted and processed out of 2.88 Mt mineralized material.

关键词： mixed-integer linear programming open stope mining ore pass development backfilling capital (decline) development production schedule

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mixed-integer linear programming approaches for nested p-center problems with absolute and relative regret objectives

arXiv

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arXiv 2024年

作者： Brandstetter, Christof Sinnl, Markus Institute of Production and Logistics Management Johannes Kepler University Linz Linz Austria Institute of Business Analytics and Technology Transformation Johannes Kepler University Linz Linz Austria JKU Business School Johannes Kepler University Linz Linz Austria

We introduce the nested p-center problem, which is a multi-period variant of the well-known pcenter problem. The use of the nesting concept allows to obtain solutions, which are consistent over the considered time horizon, i.e., facilities which are opened in a given time period stay open for subsequent time periods. This is important in real-life applications, as closing (and potential later re-opening) of facilities between time periods can be undesirable. We consider two different versions of our problem, with the difference being the objective function. The first version considers the sum of the absolute regrets (of nesting) over all time periods, and the second version considers minimizing the maximum relative regret over the time periods. We present three mixed-integer programming formulations for the version with absolute regret objective and two formulations for the version with relative regret objective. For all the formulations, we present valid inequalities. Based on the formulations and the valid inequalities, we develop branch-andbound/ branch-and-cut solution algorithms. These algorithms include a preprocessing procedure that exploits the nesting property and also begins heuristics and primal heuristics. We conducted a computational study on instances from the literature for the p-center problem, which we adapted to our problems. We also analyse the effect of nesting on the solution cost and the number of open facilities. © 2024, CC BY.

关键词： mixed-integer linear programming

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Learning Presolver Selection for mixed-integer linear programming 24

Learning Presolver Selection for Mixed-Integer Linear Progra...

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16th International Conference on Machine Learning and Computing (ICMLC)

作者： Song, Wentao Gu, Naijie Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798400709234

Presolving has become an important component of modern mixed-integer linear programming (MILP) solvers. Empirically, it has been observed that the performance of the solver is significantly influenced by the presolving algorithm (presolver), and selecting an appropriate combination of presolvers can improve solving efficiency. In industry, it is common to manually control the switch of presolvers to find a more efficient combination. However, with the emergence of new presolvers, it has become increasingly challenging to manually implement an optimal selection strategy in the vast combination space. Therefore, this paper proposes presolver selection, which needs to consider two key issues: (P1) How many presolvers should be selected? (P2) Which presolvers should be preferred among them? To address this challenge, this paper uses a hierarchical sequence model (HEM) to learn the presolver selection strategy through reinforcement learning. Specifically, the high-level model learns how many presolvers should be selected, and the low-level model learns to select a subset of presolvers within the determined size. The experimental results show that the method used can solve (P1) and (P2) better scompared to the designed baseline. It effectively improves the performance of the solver on real-world and synthetic MILPs.

关键词： mixed-integer linear programming Presolver Selection Hierarchical Sequence Model Reinforcement Learning

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Analysis of the Economic Feasibility of Power Substitution Potential at the Industrial Park Level Based on mixed-integer linear programming and Its Application in a Pharmaceutical Park

Analysis of the Economic Feasibility of Power Substitution P...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Li, Yunfeng Xu, Yutao Xiao, Yong Feng, Qihui Zhao, Yun Zhang, Yuelang CSG Electric Power Research Institute Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang China

ISBN: (纸本)9798331506797

To address the lack of rapid and economical methods for assessing the power substitution potential of enterprises and industrial parks under the dual-carbon goals, this study employs a mixed-integer linear programming (MILP) approach to construct a year-round, 8760-hour simulation and optimization model of the energy system at the industrial park level. A quantitative analysis framework for power substitution potential is proposed. Through a practical analysis of power substitution in a pharmaceutical park in Guizhou, it was found that compared with the traditional gas boiler solution, the optimized power substitution scheme using high-temperature heat pumps could save the target park approximately 95,000 RMB in annual energy costs, reduce unit energy cost by about 3%, and decrease unit energy-related carbon emissions by about 4%. This study provides a scientific method and simulation framework for quantitatively analyzing power substitution potential in industrial parks. © 2025 IEEE.

关键词： energy simulation mixed-integer linear programming Power substitution

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Multistage reliability-based expansion planning of ac distribution networksusing a mixed-integer linear programming model

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 138卷 107916-107916页

作者： Tabares, Alejandra Munoz-Delgado, Gregorio Franco, John F. Arroyo, Jose M. Contreras, Javier Los Andes Univ Dept Ind Engn Bogota Colombia Univ Castilla La Mancha Escuela Tecn Super Ingn Ind Ciudad Real Spain Sao Paulo State Univ Sch Energy Engn Sao Paulo Brazil

A new mathematical model for the multistage distribution network expansion planning problem consideringreliability is proposed in this paper. Decisions related to substation and branch expansion are driven bythe minimization of the total cost, which comprises investment and operating costs including the impactof reliability. The proposed model features two main novelties. First, a set of novel algebraic expressions isdevised for a standard reliability index, namely the expected energy not supplied. As a result, the dependence ofreliability on network topology is explicitly and effectively cast in the mathematical formulation of the planningproblem at hand. In addition, the effect of the network is characterized by a computationally efficient piecewiselinear representation of the ac power flow model that takes into account both real and reactive power. Theresulting optimization problem is formulated as an instance of mixed-integer linear programming, whichprovides a suitable framework for the attainment of high-quality solutions with acceptable computational effortusing efficient off-the-shelf software with well-known convergence properties. The effectiveness of the proposedplanning methodology is empirically demonstrated by providing cheaper expansion plans that enhance systemreliability and by achieving better computational results as compared with state-of-the-art models.

关键词： ac network model Distribution network expansion planning mixed-integer linear programming Multistage Reliability

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A mixed-integer linear programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

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ENERGIES 2022年第9期15卷 3063-3063页

作者： Gallego Pareja, Luis A. Lopez-Lezama, Jesus M. Gomez Carmona, Oscar State Univ Londrina UEL Dept Elect Engn BR-86057970 Londrina Parana Brazil Univ Antioquia Dept Ingn Elect Res Grp Efficient Energy Management GIMEL Calle 67 53-108 Medellin 050010 Colombia Univ Tecnol Pereira Fac Technol Cr 27 10-02 Pereira 660003 Colombia

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time;furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.

关键词： distribution systems reconfiguration distributed generation mixed-integer linear programming

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Optimization of Factory Energy Costs through Solar Energy Forecasting and Machine Scheduling Using mixed-integer linear programming 8

Optimization of Factory Energy Costs through Solar Energy Fo...

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8th International Conference on Information Technology, InCIT 2024

作者： Peerasantikul, Korawee Auwatanamongkol, Surapong National Institute of Development Administration Thailand

ISBN: (纸本)9798350366303

This study utilizes machine learning and optimization techniques to lower energy costs in factory operations. By incorporating weather forecast data and machine learning techniques, we aim to accurately predict solar energy production. Various machine learning models, including XGBoost, Random Forest, Support Vector Regression (SVR), Ridge Regression, and Long Short-Term Memory (LSTM) networks, were employed. Among these, XGBoost emerged as the most effective model for solar energy forecasting. Utilizing the solar energy production predicted by the models, a mixed-integer linear programming (MILP) optimization algorithm is used to determine the optimal scheduling for Sand Milling Machines. The model strategically schedules machine operations during intervals when solar energy is underutilized and during off-peak periods to minimize energy costs. This approach also ensures that the sand milling process meets critical constraints by maintaining sand levels within desired minimum and maximum thresholds. The results underscore the potential for significant cost savings and enhanced energy efficiency in factory operations through the integration of machine learning predictions and optimization techniques. © 2024 IEEE.

关键词： mixed-integer linear programming

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Conflict-Free Navigation Optimization at Unsignalized Intersections Using mixed-integer linear programming for Connected and Autonomous Vehicles 22

Conflict-Free Navigation Optimization at Unsignalized Inters...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Chu, Shuanglong Guo, Jinfeng Mu, Chen Chang'an University School of Information Engineering Xi'an China

ISBN: (纸本)9798331509712

The advent of Connected and Autonomous Vehicles (CAVs) offers significant potential to mitigate traffic accidents and reduce congestion through advanced unsignalized intersection technologies. However, this potential also introduces substantial modeling and computational challenges. This paper establishes a mixed integer linear programming (MILP) model and a Simulated Annealing Algorithm (SAA) to solve the unsignalized intersection problem. It begins with an indepth analysis of the conflict relationships among vehicles at intersections, laying the foundation for the model. Building on this, a novel vehicle trajectory planning model is introduced, leveraging the MILP model to ensure faster and more practical trajectory calculations, meeting real-time computation requirements. Finally, this paper designs a SAA to determine the optimal vehicle passing order, enhancing both safety and efficiency at intersections. Through numerical simulations, the research verifies the efficiency of the proposed MILP model and the effectiveness of the SAA in optimizing vehicle trajectories. © 2024 IEEE.

关键词： mixed-integer linear programming

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