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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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Optimal Design of a Biofuel Supply Chain Considering Government Subsidies: mixed-integer linear programming Model

Optimal Design of a Biofuel Supply Chain Considering Governm...

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IISE Annual Conference and Expo 2024

作者： Akbarian-Saravi, Niloofar Akbarian-Saravi, Ali Rabbani, Masoud School of Industrial Engineering College of Engineering University of Tehran Tehran Iran School of Industrial Engineering Amirkabir University of Technology Tehran Iran

ISBN: (纸本)9781713877851

Renewable energies have gained a significant attraction due to the increasing growing demand for energy and environmental concerns. As one of the alternatives to fossil fuels, bioethanol from switchgrass is considered. To realize the strategic and tactical biofuel supply chain decisions, in this study, we developed a different mixed-integer linear programming model aiming to minimize total annualized costs over a 5-year planning horizon by incorporating financial incentives and comparing the models with and without budget consideration. Using this model, the optimal allocation of material flows is determined along with optimal supply and pre-processing locations for producing a gasoline (95%)-ethanol (5%) blend. The monetary incentives include i) a constant budget for the whole Bioethanol Supply Chain (BSC) planning;and ii) treating the distribution of the budget as a decision variable. To illustrate the application of the proposed model, a case study of Iran is investigated. Results indicate the requirement of a monetary incentive to produce bioethanol and establish plants for bioethanol processing. The cost savings resulting from the optimal allocation of monetary incentives were higher than those from constant monetary incentives. The sensitivity analysis of the model indicates that the biomass-to-ethanol conversion rate has the highest impact on the total costs. © IISE Annual Conference and Expo *** rights reserved.

关键词： mixed-integer linear programming

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A mixed-integer linear programming model with multi-unit strategy for distributed biorefinery superstructures with economic and social benefits

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2022年第6期24卷 1903-1925页

作者： Maharana, Debasis Kommadath, Remya Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India Indian Inst Technol Guwahati Sch Business Gauhati 781039 India

Biofuels have emerged as a promising alternative for depleting fossil fuels. Efficient biofuel production requires the optimal supply chain design and the planning of complex production operations in biorefineries. The biomass processing is capacity constrained due to design restrictions. The limitation on the production capacity corresponding to a selected technology restricts the maximum production from the most profitable process. In such a scenario, the production plan involves the use of less profitable processes to satisfy the demand, resulting in a sub-optimal solution. This article proposes a multi-unit, multi-period supply chain model to overcome the restriction in plant capacity by establishing multiple units of a profitable process. The multi-objective optimization study of the proposed model has been demonstrated on a case study from the literature that requires the design of supply chain for bioenergy. The proposed formulation leads to solutions with better economic and social benefits than those reported in the literature. The multi-unit model provides a solution which has a 4.73% increase in profit and generates 78 additional jobs for a horizon of 12 months. The optimal network design has contributed to several key findings, such as the breakeven point of the raw materials with respect to the profit and the dual relationship between profit and the number of jobs generated. The study also analyzes the effect of multiple units and variations of demand on the proposed model. The multi-unit model is able to determine a solution with 3.96% improvement in profit even with a reduction in the demand by 50%. [GRAPHICS] .

关键词： Biofuel supply chain Facility location Capacity planning Social impact Economic optimization 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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A mixed-integer linear programming formulation for the modular layout of three-dimensional connected systems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2022年 201卷 739-754页

作者： O'Neill, Sam Wrigley, Paul Bagdasar, Ovidiu Univ Derby Dept Elect Comp & Math Kedleston Rd Derby DE22 1GB England

Given the considerable complexity of process plants, there has been a great deal of research focused on aiding the design of plant layout through mathematical optimisation, i.e. optimising the positioning of the equipment in the plant for space and cost efficiency. Recently, the use of modular approaches within the construction industry, whereby work is performed off-site before being assembled on-site, has become a popular and powerful way of reducing build schedules and costs. Modular approaches have many other real applications where items must be packed to minimise the connections between them (e.g. piping, wiring, modular office and factory layouts) and consider the modular layout of the system. In this paper, we provide a formulation of the problem that, in addition to the standard layout problem, considers a modular block layout to allow modular construction and transportation of the plant. The problem is represented as a directed network, with the aim to pack the items into predefined containers and minimise the rectilinear distance between the connected items. We propose mixed-integer linear programming (MILP) models for the 2-dimensional and 3-dimensional problems and solve them using the state-of-the-art mathematical programming solver, Gurobi. Because of the combinatorial nature of the problem, solutions involving a large number of items may not converge and a suboptimal solution must be considered. However, our results suggest that even in the case of optimising a large number of items, the suboptimal solutions found after a reasonable number of iterations where deemed, by a domain expert, to be a good enough starting point to continue the design process, especially in the early concept phase. (c) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Floor packing Bin packing Facility layout mixed-integer linear programming MILP Process plant design

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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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Formulating multi-class user equilibrium using mixed-integer linear programming

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EURO JOURNAL ON TRANSPORTATION AND LOGISTICS 2022年 11卷

作者： Lin, Xiao Leclercq, Ludovic Tavasszy, Lorant van Lint, Hans Delft Univ Technol Dept Transport & Planning Stevinweg 1 NL-2628 CN Delft Netherlands Univ Gustave Eiffel ENTPE LICIT Lyon France

We introduce an approach to formulate and solve the multi-class user equilibrium traffic assignment as a mixed-integer linear programming (MILP) problem. Compared to simulation approaches, the analytical MILP formulation makes the solution of network assignment problems more tractable. When applied in a multi -class context, it obviates the need to assume a symmetrical influence between classes and thereby allows richer traffic behavior to be taken into account. Also, it integrates naturally in optimization problems such as maintenance planning and traffic management. We develop the model and apply it for the Sioux Falls network, showing that it outperforms the traditional Beckmann-based and MSA approaches in smaller-scale problems. Further research opportunities lie in developing extensions of MILP-based assignment, with different variants of user equilibrium or dynamic assignment, and in improving the model and solution algorithms to allow large-scale application.

关键词： Traffic assignment User equilibrium Mathematical programming mixed-integer linear programming Multi-class

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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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Optimal override policy for chemotherapy scheduling template via mixed-integer linear programming

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OPTIMIZATION LETTERS 2022年第5期16卷 1549-1562页

作者： Huang, Yu-Li Sikder, Ishtiak Xu, Guanglin Mayo Clin Robert & Patricia E Kern Ctr Sci Hlth Care Delive Rochester MN USA Univ N Carolina Dept Syst Engn & Engn Management Charlotte NC 28223 USA

Owing to treatment complexity in chemotherapy administration, nurses are usually required at the beginning, end, and at certain times during the treatment period to ensure high-quality infusion. It is, thus, critical for an outpatient chemotherapy unit to design a scheduling template that can effectively match nursing resources with treatment requirements. The template contains appointment slots of different lengths and thus allows schedulers to place patients into these slots according to the provider's order. As the template is often used over a period of several months, there usually exists a mismatch between the daily patient mix and the fixed structure of the given template. Hence, override policies must be employed to adjust to demand. However, these policies are often manually performed by schedulers. We propose a mixed-integer linear programming model to systematically develop optimal override policies in place of the manual process to improve template utilization while maintaining template stability. Numerical experiments based on real-life data from a chemotherapy unit are conducted to demonstrate the effectiveness of the proposed approach.

关键词： Chemotherapy Scheduling template Override policy mixed-integer linear programming

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mixed-integer linear programming Model to Assess Lithium-Ion Battery Degradation Cost

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ENERGIES 2022年第9期15卷

作者： Oliveira, Debora B. S. Gloria, Luna L. Kraemer, Rodrigo A. S. Silva, Alisson C. Dias, Douglas P. Oliveira, Alice C. Martins, Marcos A. I. Ludwig, Mathias A. Gruner, Victor F. Schmitz, Lenon Coelho, Roberto F. CERTI Fdn Ctr Sustainable Energy BR-88040900 Florianopolis SC Brazil AES Brazil R&D & Innovat BR-04578000 Sao Paulo Brazil Univ Fed Santa Catarina Dept Elect Engn BR-88040900 Florianopolis SC Brazil

This work proposes a mixed-integer linear programming model for the operational cost function of lithium-ion batteries that should be applied in a microgrid centralized controller. Such a controller aims to supply loads while optimizing the leveled cost of energy, and for that, the cost function of the battery must compete with the cost functions of other energy resources, such as distribution network, dispatchable generators, and renewable sources. In this paper, in order to consider the battery lifetime degradation, the proposed operational cost model is based on the variation in its state of health (SOH). This variation is determined by experimental data that relate the number of charge and discharge cycles to some of the most important factors that degrade the lifespan of lithium-ion batteries, resulting in a simple empirical model that depends on the battery dispatch power and the current state of charge (SOC). As proof-of-concept, hardware-in-the-loop (HIL) simulations of a real microgrid are performed considering a centralized controller with the proposed battery degradation cost function model. The obtained results demonstrate that the proposed cost model properly maintains the charging/discharging rates and the SOC at adequate levels, avoiding accelerating the battery degradation with use. For the different scenarios analyzed, the battery is only dispatched to avoid excess demand charges and to absorb extra power produced by the non-dispatchable resources, while the daily average SOC ranges from 48.86% to 65.87% and the final SOC converges to a value close to 50%, regardless of the initial SOC considered.

关键词： battery energy storage system degradation cost model lithium-ion battery microgrid power dispatch mixed-integer linear programming

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