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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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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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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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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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Optimal Power Flow Integrated Electrical Gas System Using mixed-integer linear programming

Optimal Power Flow Integrated Electrical Gas System Using Mi...

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Wati, Trisna Risamasu, Yasmin Anggraeni Wibowo, Rony Seto Penangsang, Ontoseno Uman Putra, Dimas Fajar Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350395075

As time passes, the number generators gas-fired electricity will continue to increase. Power plants Gas fuel is very reliable for fulfilling load requirements and supporting peak loads. Besides its high efficiency, gas fired units are very profitable because of low capital costs, fast installation and minimal pollutant emissions, as well the start and shutdown speeds are the characteristics of the unit gas fuel is suitable for basic, medium, and heavy loads. Peaks provide backup for renewable generators. Intermittent an increases interdependence thus the electricity and gas energy sectors. Optimal and Gas Flow (OPGF) was the most basic problem in the mix these two systems. Gas consumption rates vary form over time, causing pressure fluctuations in the tissue gas transmission pipelines, which can have a negative impact on security and reliability of gas delivery. In addition, the gas system has slower dynamics than the system electricity and requires a longer stabilization time processing load changes and disturbances. Therefore, the optimal power and gas flow at integrated electrical and natural gas system for minimize operating cost. The OPGF problems were solved using mixed-integer linear programming (MILP) with Piecewise RMSE values the linear approximation is 5.57 © 2024 IEEE.

关键词： 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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Research on New Energy Consumption Prediction and Access Planning Based on Particle Swarm Optimization and mixed-integer linear programming 24

Research on New Energy Consumption Prediction and Access Pla...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Zhao, Lu Fan, Li Li, Yuanlin Power China Hubei Electric Engineering Co. Ltd China

ISBN: (纸本)9798400718137

With the rapid development of machine learning, it has been applied to solve various problems in social life because of its unique advantages. To address the current new energy consumption prediction and access planning methods of peak shifting capacity is insufficient. Particle swarm optimisation algorithm and mixed integer linear programming algorithm are proposed and applied to the models of new energy consumption prediction and photovoltaic access planning respectively. The new energy consumption prediction model based on particle swarm optimisation is applied for comparative analysis, and the results show that its average prediction accuracy and precision rate are 94.7% and 95.8%, respectively, which are significantly better than the comparison model. The mixed-integer linear programming-based PV access model is used for comparative analysis, and the results show that it is significantly better than the comparison model in terms of comprehensive cost and PV access volume dimensions, which is practical. The above results show that the two models proposed in the study have good performance and practicality, which are conducive to maximising the consumption of new energy and saving costs. © 2024 Copyright held by the owner/author(s).

关键词： mixed-integer linear programming

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A mixed-integer linear programming Model for Simultaneous Optimal Reconfiguration and Optimal Placement of Capacitor Banks in Distribution Networks

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IEEE ACCESS 2022年 10卷 52655-52673页

作者： Gallego, Luis A. Lopez-Lezama, Jesus M. Carmona, Oscar Gomez Londrina State Univ UEL Dept Elect Engn BR-86057970 Londrina Parana Brazil Univ Antioquia UdeA Fac Ingn Dept Ingn Elect Grp Invest GIMEL Medellin 050010 Colombia Univ Tecnolog Pereira UTP Grp Invest LIDER Programa Tecnologia Elect Pereira 660003 Colombia

Optimal capacitor placement and network reconfiguration are well-known methods to minimize losses, enhance reliability, and improve the voltage profile of electric distribution networks (EDNs). Distribution network reconfiguration (DNR) consists of altering the system topology by changing the states of ties and sectionalizing switches, while the optimal placement of capacitors (OPCAs) involves sizing and finding the optimal location of capacitor banks within the distribution network for reactive power control. DNR and OPCAs are challenging optimization problems involving both integer and continuous decision variables. Due to the nature of these problems (combinatorial optimization problems), most approaches that deal with DNR and OPCAs resort to metaheuristic techniques, and they are limited to applications in small-size distribution networks. Although these techniques have proven to be effective when dealing with non-convex optimization problems, their main drawbacks lie in the fact that they require the fine-tuning of several parameters and do not guarantee the finding of a globally optimal solution. This paper presents a mixed-integer linear programming (MILP) model to solve simultaneous DNR and OPCAs in radial distribution networks. The proposed model can be solved by commercially available software;it guarantees to obtain globally optimal solutions and requires low computational effort when compared with metaheuristic techniques employed for the same purpose. Several tests were carried out on seven benchmark EDNs ranging from 33 to 417 buses. In all cases, the proposed methodology was able to replicate the results reported in the specialized literature. Regarding the DNR alone, a novel solution was found for the 119-bus test system which is 1.86 % better than that previously reported in the specialized literature. Furthermore, new solutions are reported for the simultaneous optimal DNR and OPCAs for medium and large-size EDNs. The power loss reduction in the te

关键词： Capacitors Metaheuristics Distribution networks Voltage Search problems Genetic algorithms Particle swarm optimization Capacitor placement distribution systems mixed-integer linear programming network reconfiguration

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Optimizing Healthcare Delivery: A mixed-integer linear programming Model for Integrating Face-to- Face and Virtual Consultations

Optimizing Healthcare Delivery: A Mixed-Integer Linear Progr...

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2024 Global Digital Health Knowledge Exchange and Empowerment Conference, gDigiHealth.KEE 2024

作者： Oshan, Tareq Sharkawi, Mahmoud Hamada, Nuha College Of Engineering Al Ain University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331531331

Digital health (or telehealth) appears to be one of the most active areas of study in today's world and has been showing substantial evidence of effectiveness in different aspects of modern society. Most significantly, in the healthcare systems, where availability of quality services is the cornerstone, digital health solutions are transforming practices for patients. This paper proposes a mathematical model for healthcare systems in which a comprehensive approach is taken to patient need to be hospitalized. An objective function is established aiming to maximizing the number of patients needing hospitalization, either through face-to-face consultations or through online portals while taking into consideration the attitude of all patients towards online consultations. To achieve this, we use the preferences in the model to make sure that patient satisfaction is not compromised while resources are used efficiently. With a formal breakdown and analysis of the problem and considerable reliance on complex optimization algorithms, the model outlines a set of steps that will ensure the optimal distribution of patients in terms of face-to-face and virtual consultations. This in turn not only increases effectiveness of the healthcare delivery systems but it also improves the efficiency of the health care delivery systems as well. As a confirmation of our model, we tested it on an example data and used LINGO software to obtain the best solution of the problem. The findings from this example pinpoint the possibility of applying the model for developing effective solutions to patient distribution and, therefore, enhance the overall benefits of the healthcare system. This systematic approach is aimed at demonstrating the positive changes which are possible in the delivery of patient care through the introduction of technology supports in modern health care infrastructures. © 2024 IEEE.

关键词： mixed-integer linear programming

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