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International Symposium on Parallel and Distributed Processing with Applications, ISPA

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

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

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.

关键词： Connected vehicles Trajectory planning Computational modeling Numerical simulation Real-time systems Numerical models Trajectory Safety mixed integer linear programming Autonomous vehicles

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mixed-integer linear programming Solvers for Local Grid Capacity Planning Problems

Mixed-Integer Linear Programming Solvers for Local Grid Capa...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Sergii Saukh Taras Puchko Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The algorithms and software tools for solving large-scale NP-hard MILP (mixed-integer linear programming) problems are analyzed, specifically focusing on the modeling of electric power systems arising in the planning of the development of generating equipment. A generalized algorithm for solving such problems is defined, based on the possibility of their decomposition: investment costs for the construction of generating units and the number of such units are reflected in the master MILP subproblem, while the system operator's costs for the operation of generating units, as well as the technological indicators of their load for a set of electricity consumption graphs, are reflected in a set of independent secondary MILP subproblems. It is noted that for Local Grids with large shares of electricity production from renewable energy sources, the sets of independent secondary MILP subproblems are quite substantial. Therefore, the speed of solving the secondary MILP subproblems determines the speed of solving the original MILP problems of electric power system modeling. Based on the results of experimental studies using the SCIP, HiGHS, Cbc, and GLPK solvers to solve a test MILP problem of optimizing the generating capacity structure of a Local Grid, it was established that the SCIP and HiGHS solvers are appropriate for solving the secondary MILP subproblems.

关键词： Costs Electricity Software algorithms Production Systems modeling Power systems Planning mixed integer linear programming Software tools Investment

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

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

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

作者： Houria Lahmar Mohammed Dahane Kinza Nadia Mouss Mohammed Haoues Faculty of Technology University of Batna II Batna Algeria LGIPM Université de Lorraine Metz France

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

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： Costs Systematics Computational modeling Production planning Production Mathematical models mixed integer linear programming Information technology

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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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Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)

作者： Tareq Oshan Mahmoud Sharkawi Nuha Hamada College Of Engineering Al Ain University Abu Dhabi UAE

ISBN: (数字)9798331531331

ISBN: (纸本)9798331531348

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.

关键词： Adaptation models Systematics Telemedicine Mathematical models Data models Software Electronic healthcare mixed integer linear programming Resource management Portals

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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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Sustainable Power and Energy Conference (iSPEC)

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

ISBN: (数字)9798350395075

ISBN: (纸本)9798350395082

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

关键词： Costs Electricity Fluid flow Systems modeling Mathematical models Generators mixed integer linear programming Fuels Natural gas Load flow

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

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

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International Conference on Information Technology (INCIT)

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

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

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.

关键词： Schedules Costs Optimization models Solar energy Optimal scheduling Milling Predictive models Production facilities mixed integer linear programming Long short term memory

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Energetic and economic optimization of the yearly performance of three different solar assisted heat pump systems using a mixed integer linear programming algorithm

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ENERGY CONVERSION AND MANAGEMENT 2020年 206卷

作者： Simonetti, Riccardo Moretti, Luca Molinaroli, Luca Manzolini, Giampaolo Politecn Milan Dipartimento Energia Via Lambruschini 4 I-20156 Milan Italy

This paper presents a yearly energetic and economic assessment of three different solar assisted heat pump concepts integrated with electrical and thermal storages and applied to a single-family house. The three heat pump-based systems are (i) a conventional air-to-water heat pump plus photovoltaic solar modules, (ii) a water-to-water heat pump coupled to hybrid photovoltaic-thermal solar modules and (iii) a dual source heat pump integrated with hybrid photovoltaic-thermal solar modules. The energetic and economic analysis is performed using physically-based models of each component for different numbers of solar modules and sizes of the electric storage. The maximum economic saving of these highly integrated systems is determined through an optimization algorithm based on the mixed integer linear programming technique. The results show that the dual source heat pump system with the largest battery size is the system that achieves the highest energetic performance, i.e. 77% primary energy saving with respect to traditional boiler-based system, whereas the lowest operating cost and the highest economic saving are obtained using the conventional air-to-water heat pump without any electric storage since it achieves 31% economic saving compared with the baseline system.

关键词： Economic analysis Energetic analysis mixed integer linear programming Hybrid photovoltaic/thermal collectors Solar assisted heat pump

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Energy Management of a Workplace with EVs by mixed-integer linear programming

Energy Management of a Workplace with EVs by Mixed-Integer L...

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International Technical Conference o Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Yuki Sakamoto Takumi Namba Kiyotsugu Takaba Ritsumeikan University Kusatsu Japan

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

The use of EV batteries as secondary energy sources has recently been attracting great attention due to their large capacities. This paper is concerned with energy management of a workplace making use of the employees' EVs. We formulate such an energy management as a mixed-integer linear programming (MILP) problem by taking account of the arrival/departure status of the EVs and the benefits of the EV owners. Through a numerical simulation, we compare the effectiveness of the centralized algorithm and the distributed algorithm (Camisa et al. 2022) for finding a near-optimal solution to the MILP. The following points turn out from the simulation. If the number of EVs is small, the centralized algorithm can obtain a better solution to the MILP problem faster than the distributed algorithm without admitting the EV owners' decision-making. In contrast, in the case of a large number of EVs, the distributed algorithm can solve the problem faster than the centralized one taking account of the EV owners' decision-making at the price of the quality of its solution.

关键词： Computers Employment Decision making Numerical simulation linear programming mixed integer linear programming Batteries

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On the Equivalence Between mixed-integer linear and linear programming Problems in MPC for Photovoltaic Plants with Energy Storage

On the Equivalence Between Mixed-Integer Linear and Linear P...

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IEEE ANDESCON

作者： Rodrigo Graves Angel L. Cedeño Cùsar Silva Juan C. Agüero Departamento de Electronica Universidad Tecnica Federico Santa Maria Valparaiso Chile

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

This paper investigates the equivalence between a class of mixed-integer linear and linear programming prob-lems. By utilizing the addition of slack variables theorem, we demonstrate that certain optimization problems arising in Model Predictive Control strategies, which involve logical constraints, can be solved as linear programming problems. Consequently, the equivalent linear programming problem can be solved using the standard and numerically efficient method named the Sim-plex method, as opposed to mixed-integer linear programming problems for which the best solution method is not clear. As a case study, we address the control problem of photovoltaic plants with energy storage systems. We design a model-based predictive control scheme based on our proposed equivalent linear programming problem to maximize economic benefits derived from energy delivered by both photovoltaic panels and energy storage systems to the grid in a deregulated electricity market, achieved by managing the charge and discharge of the energy storage. Additionally, the proposed scheme allows for the consideration of rate-based variable efficiency in the energy stor-age system model, offering a more comprehensive implementation compared to the previous approach in the literature, where only an ideal battery was considered. Furthermore, simulations reveal that the proposed control methodology reduces the execution time of model-predictive control iterations compared to another approach based on mixed-integer linear programming schemes with similar considerations, thereby enhancing the scalability of the solution.

关键词： Photovoltaic systems Economics Transforms linear programming Electricity supply industry Batteries mixed integer linear programming Optimization Standards Predictive control

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A mixed integer linear programming framework for optimising the extraction strategy of open pit - underground mining options and transitions

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2020年第10期34卷 700-724页

作者： Afum, Bright Oppong Ben-Awuah, Eugene Askari-Nasab, Hooman Laurentian Univ Bharti Sch Engn MOL Sudbury ON Canada Univ Alberta Sch Min & Petr Engn Dept Civil & Environm Engn MOL Edmonton AB Canada

The decision to exploit a mineral deposit that can be extracted by open pit and/or underground mining involves consideration of the following options: (a) independent open pit extraction;(b) independent underground extraction;(c) simultaneous open pit and underground (OPUG) extraction;(d) sequential OPUG extraction;and (e) combinations of (c) and (d). This paper investigates the extraction strategy for deposits using mixed integer linear programming (MILP) optimisation framework to maximise the net present value and determine the schedules for mining, processing, underground capital and operational developments, and 3D crown pillar position. The MILP framework is implemented for a gold deposit. The results showed a combined sequential and simultaneous open pit and underground (OPUG) mining with crown pillar as the optimal extraction option generating NPV that is 11% and 13% better than independent OP or UG mining, respectively.

关键词： mixed integer linear programming open pit and underground mine planning mining options mine optimisation transition depth crown pillar

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