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作者： Mas Martin, Irene Iowa State University

学位级别：M.S., Master of Science/Master of Surgery

Energy is essential to our society to ensure quality of life and renewable energy systems are necessary to achieve solutions to environmental problems. Renewable energies sources are needed from an environmental point of view, and they are effective from an economic perspective. This thesis examines bioenergy production through anaerobic digestion (AD) of different feedstock. This study aims to optimize energy resource collection, allocation, conversion, and dispatch by employing a dynamic mixed-integer linear programming model (MILP). The model optimizes the dispatch of renewable natural gas (RNG) and electricity based on urban residential, industrial and commercial energy demand. Moreover, the model system offers the most efficient location for AD facilities. One journal paper has been submitted to Renewable Energy: “Optimal production and dispatch of renewable natural gas, electricity, and fertilizer in municipal-scale anaerobic digestion supply chains” (Under review). The second paper will be published in the future. The analysis shows that AD systems could generate RNG with a levelized cost of $0.011/kWh, electricity at a levelized cost of $0.025 to $0.039/kWh, and fertilizer at a cost ranging from $0.035 to $0.055/kWh.

关键词： Anaerobic digestion mixed-integer linear programming Supply chain

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Soft regression trees: a model variant and a decomposition training algorithm

arXiv

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arXiv 2025年

作者： Consolo, Antonio Amaldi, Edoardo Manno, Andrea DEIB Politecnico di Milano Milano Italy Centro di Eccellenza DEWS DISIM Università degli Studi dell’Aquila L’Aquila Italy

Decision trees are widely used for classification and regression tasks in a variety of application fields due to their interpretability and good accuracy. During the past decade, growing attention has been devoted to globally optimized decision trees with deterministic or soft splitting rules at branch nodes, which are trained by optimizing the error function over all the tree parameters. In this work, we propose a new variant of soft multivariate regression trees (SRTs) where, for every input vector, the prediction is defined as the linear regression associated to a single leaf node, namely, the leaf node obtained by routing the input vector from the root along the branches with higher probability. SRTs exhibit the conditional computational property, i.e., each prediction depends on a small number of nodes (parameters), and our nonlinear optimization formulation for training them is amenable to decomposition. After showing a universal approximation result for SRTs, we present a decomposition training algorithm including a clustering-based initialization procedure and a heuristic for reassigning the input vectors along the tree. Under mild assumptions, we establish asymptotic convergence guarantees. Experiments on 15 well-known datasets indicate that our SRTs and decomposition algorithm yield higher accuracy and robustness compared with traditional soft regression trees trained using the nonlinear optimization formulation of Blanquero et al., and a significant reduction in training times as well as a slightly better average accuracy compared with the mixed-integer optimization approach of Bertsimas and Dunn. We also report a comparison with the Random Forest ensemble method. © 2025, CC BY-NC-ND.

关键词： mixed-integer linear programming

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Real-time Optimization for Wind-to-H2 Driven Critical Infrastructures Based on Active Constraints Identification and integer Variables Prediction

arXiv

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arXiv 2025年

作者： Goodarzi, Mostafa Li, Qifeng Electrical and Computer Engineering University of Central Florida Orlando United States

This paper proposes a concept of wind-to-hydrogen-driven critical infrastructure (W2H-CI) as an engineering solution for decarbonizing the power generation sector where it utilizes wind power to produce hydrogen through electrolysis and combines it with the carbon captured from fossil fuel power plants. First, a convex mathematical model of W2H-CI is developed. Then, an optimization model for optimal operation of W2H-CI, which is a large-scale mixed-integer convex program (MICP), is proposed. Moreover, we propose to solve this problem in real-time in order to hedge against the uncertainty of wind power. For this purpose, a novel solution method based on active constraints identification and integer variable prediction is introduced. This method can solve MICP problems very fast since it uses historical optimization data to predict the values of binary variables and a limited number of constraints which most likely contain all active constraints. We validate the effectiveness of the proposed fast solution method using two W2H-CI case studies. Copyright © 2025, The Authors. All rights reserved.

关键词： mixed-integer linear programming

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An integrated frequency assignment and fleet assignment model: A strategic application in the Brazilian regional aviation market

Transport Economics and Management

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Transport Economics and Management 2023年 1卷 151-159页

作者： Thiago Dias de Jesus Marcelo Seido Nagano Victor Rafael Rezende Celestino University of São Paulo São Carlos School of Engineering Av. Trabalhador Sãocarlense 400 São Carlos SP Brazil University of Brasília Faculty of Economics Administration Accounting and Public Policy Management Asa Norte Brasília DF Brazil

Airlines endeavour to implement substantial measures to ensure profitability and maintain a competitive edge in the market. This entails conducting extensive studies and engaging in strategic planning to make optimal decisions. A crucial element of this process involves addressing the Fleet Assignment Problem (FAP). Typically, aircraft operating within an established network are acquired before adopting this method, rather than being selected based on an optimal solution. Consequently, incumbent airlines frequently operate in Brazilian regional aviation with fleets that are not optimised for the specific routes they serve. This study aims to explore an integrated frequency assignment and fleet assignment model, which identifies the most suitable aircraft for a given network. Model parameters were estimated from historical data from multiple incumbent airlines and were applied to a fictitious airline operating from Goiania International Airport (Brazil) as the hub. The findings from multiple test runs ascertain optimal frequency and aircraft capacity selections, as well as recommended fleet sizes. Further analysis reveals, for instance, that the Embraer E190-E2 is the most suitable aircraft for a scenario constrained to a single type of fleet, demonstrating the potential for significant profitability on the routes in the simulated network.

关键词： Brazilian regional aviation Fleet assignment problem Integrated airline model mixed-integer linear programming

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Optimization of production, distribution, natural resources and capacity planning throughout process sector worldwide supply chains with various goals

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Resources Policy 2024年 97卷

作者： Yuan, Junli lv, Zhaohe Aliyeva, Tahmina Chen, Xu School of Economics Yunnan University Yunnan 650091 China Azerbaijan Harbin University of Commerce Harbin150028 China

This study delves into the intricate dynamics of green transformation within global mining and other process industries, focusing on production, distribution, and capacity planning under the framework of the Asian Mineral Vision. By addressing sustainability, resource efficiency, and cost reduction, the research highlights the multifaceted challenges faced by industry participants in achieving green objectives. In addition to financial efficiency, the study emphasizes the importance of customer service quality and responsiveness. To tackle these challenges, we propose a novel multiobjective mixed-integer linear programming (MILP) approach. The model incorporates key goals such as minimizing total cost, total flow time, and total missed sales, reflecting the interconnected nature of financial efficiency, operational agility, and customer satisfaction. To further enhance the model's flexibility, we integrate discrete strategies for plant capacity expansion, recognizing the crucial role of capacity management in responding to evolving demand dynamics. The multiobjective optimization problem is addressed using the lexicographic minimax technique and the Ε-constraint method. A comprehensive numerical example illustrates the practical relevance and effectiveness of our proposed model and solution methods, providing valuable insights into improving the resilience and performance of green supply chain networks in the process industry. © 2024

关键词： mixed-integer linear programming

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Multi-objective models for crop rotation planning problems

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AGRICULTURAL SYSTEMS 2024年 219卷

作者： Pontes, Roberto Da Silva Gervasio Brandao, Diego Nunes Usberti, Fabio Luiz De Assis, Laura Silva CEFET RJ Fed Ctr Technol Educ Rio De Janeiro Av Maracana 229 BR-20271110 Rio De Janeiro RJ Brazil Univ Estadual Campinas UNICAMP Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil

CONTEXT: Agriculture is a vital component of the global economy and modern societies. It has undergone significant consolidation and transformation in response to the food supply crisis, highlighting the important relationship between humans and the environment. However, concerns remain about food security, particularly with the projected population growth of over 9.5 billion by 2050. The computerization of agri-food supply chains has emerged as a significant response to these challenges. OBJECTIVE: (1) Develop a multi-objective model that explores both net return and crop diversity. (2) Solve the problem using techniques that guarantee optimality. (3) Evaluate the gain in crop diversity versus the net return of the optimized configuration. METHODS: The study presents four Multi-objective mixed-integer linear programming models with integer and binary decision variables for Crop Rotation Planning Problems. The objectives are to maximize net income and increase crop diversity and land utilization. RESULTS AND CONCLUSIONS: The study exclusively employs linear programming techniques to solve the models resulting in an optimal solution. A comparative analysis with existing models in the literature, which primarily focused on maximizing net income, yielded a noteworthy result. The proposed models demonstrate an average increase of 60% in crop diversity, with net return losses of less than 5%. SIGNIFICANCE: In conclusion, this research provides valuable information for crop rotation planning and highlights the importance of agricultural farm management and precision agriculture in addressing current challenges. The innovative nature of this research is exemplified by the use of mixed-integer linear programming techniques to solve a multi-objective problem with integer and binary variables. The obtained results demonstrate increased crop diversity and minimal economic losses, which have significant implications for several areas of agricultural science, policy, and practic

关键词： Multi-objective optimization Precision farming Crop rotation mixed-integer linear programming Agricultural farm management

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Light-EvoOPT: A Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale mixed integer linear Programs

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IEEE Transactions on Evolutionary Computation 2025年

作者： Ye, Huigen Xu, Hua Coello, Carlos A. Coello Tsinghua University State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Beijing100084 China Department of Computer Science D.F México07300 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L Monterrey Mexico

Machine Learning (ML)-based optimization frameworks emerge as a promising technique for solving large-scale mixed integer linear Programs (MILPs), as they can capture the mapping between problem structures and optimal solutions to expedite their solution process. However, existing solution frameworks often suffer from high model computation costs, incomplete problem reduction, and reliance on large-scale solvers, leading to performance bottlenecks in ultra-large-scale problems with complex constraints. To address these issues, this paper proposes Light-EvoOPT, a Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale mixed integer linear Programs, which can be divided into four stages: (1) Problem Formulation for problem division to reduce model computational costs, (2) Model-based Initial Solution Prediction for predicting and constructing the initial solution using a small-scale training dataset, (3) Problem Reduction for both variable and constraint reduction, and (4) Evolutionary Optimization for current solution improvement employing a lightweight optimizer. Experiments on four benchmark datasets with tens of millions of variables and constraints and a real-world problem show that the proposed framework based on the sole use of a lightweight optimizer, trained on only one-thousandth of the scale of ultra-large-scale problems, is able to outperform state-of-the-art ML-based frameworks and advanced solvers (e.g. Gurobi) within a specified computational time, validating the feasibility and effectiveness of our proposed ML-based evolutionary optimization framework for ultra-large-scale MILPs. © 2025 IEEE. All rights reserved.

关键词： mixed-integer linear programming

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Data-driven hospital personnel scheduling optimization through patients prediction

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CCF TRANSACTIONS ON PERVASIVE COMPUTING AND INTERACTION 2021年第1期3卷 40-56页

作者： Feng, Defan Mo, Yu Tang, Zhiyao Chen, Quanjun Zhang, Haoran Akerkar, Rajendra Song, Xuan Southern Univ Sci & Technol SUSTech UTokyo Joint Res Ctr Super Smart City Dept Comp Sci & Engn Shenzhen Peoples R China Western Norway Res Inst Sogndal Norway

With the rapid development of the modern city, technologies of smart cities are indispensable for solving urban problems. Medical services are one of the key areas related to the lives of urban residents. In particular, how to effectively manage the human resources of a hospital is a complex and challenging problem to improve treatment capabilities. Due to the grievous shortage of medical personnel, hospitals have to make quality schedules to improve the efficiency of the hospital and the utilization rate of human resources. Although there have been a large number of researches on hospital staff scheduling, few people also consider future patient population forecasts, doctor scheduling and hospital structure. These factors are very important in the hospital staff scheduling problem. Concerning this, this paper establishes an optimization system combining a two-layer mixed-integer linear programming and an extended prophet model for the hospital personnel scheduling. The model considers factors such as weather, disease types, number of patients, room resources, doctor resources, working hours, etc., and can quickly obtain a timetable with complex constraints. Finally, the convergence and the practicability of the model has been verified with real data from a hospital in China.

关键词： Hospital personnel scheduling Time series prediction mixed-integer linear programming

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A decision-support framework for residential heating decarbonisation policymaking

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ENERGY 2023年 268卷

作者： De Mel, Ishanki Bierkens, Floris Liu, Xinyao Leach, Matthew Chitnis, Mona Liu, Lirong Short, Michael Univ Surrey Dept Chem & Proc Engn Guildford GU2 7XH England Univ Surrey Ctr Environm & Sustainabil Guildford GU2 7XH England Univ Surrey Surrey Energy Econ Ctr SEEC Sch Econ Guildford GU2 7XH England

The decarbonisation of residential heating systems has become increasingly important to meet the global goals of minimising carbon emissions and combating climate change. However, with rising energy costs, this can be a significant challenge for low-income households. This study presents a novel optimisation framework to aid the decarbonisation of residential heating in the United Kingdom by combining technology-related decision-support with policy decisions. The framework can recommend the optimal retrofit of low-carbon heating technologies and fabric improvement measures such as insulation upgrades for improving energy efficiency. Concurrently, the optimal financial contributions towards investment costs from grants supporting low-income households and social housing is determined. It also includes piecewise linearisations to capture the detailed operation of air source heat pumps, which are set to replace natural gas-based heating systems, and assesses the eligibility of each dwelling for grant funding. A large case study consisting of social housing stock in Woking, UK, has been used to test the framework. Three scenarios are used to assess the efficacy of existing technology and policy combinations to meet local emissions reduction targets, which are benchmarked against emissions from existing gasbased heating systems and insulation measures. Results highlight the limitations of existing UK grants, as these can only achieve an emissions reduction of 33.5% without incurring significant additional investment costs to the local council. The lack of support towards installing hot water tanks, which are required for the operation of heat pumps, is another major limitation in existing grants. A proposed scenario, which introduces a fictional grant with unlimited funding, sheds light on the much larger grant contributions expected to achieve an emissions reduction of 66.8%, which surpasses local targets. These results also suggest the need for operational support to c

关键词： Optimisation mixed-integer linear programming Energy efficiency grants Retrofitting strategy Decarbonised heating

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Two-stage centralised management of Local Energy Market for prosumers integration in a community-based P2P

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APPLIED ENERGY 2023年第1期348卷

作者： Goitia-Zabaleta, Nerea Milo, Aitor Gaztanaga, Haizea Fernandez, Elvira IKERLAN Technol Res Ctr Energy Storage & Management Dept Basque Res & Technol Alliance BRTA P JM Arizmendiarrieta 2 Arrasate Mondragon 20500 Spain Univ Basque Country UPV EHU Elect Engn Dept Plaza Ingeniero Torres Quevedo 1 Bilbao 48013 Spain

Centrally managed or community-based P2P Energy Communities are at the forefront of the literature. A collectively shared energy configuration maximises the energy use of local generators. An interesting scenario is energy sharing when residential buildings conform to an Energy Community with tertiary buildings as prosumers. This article introduces a two-stage centralised management of the Local Energy Market for prosumers integration. In the first stage, energy planning is carried out. In this phase, the Local Energy Market Operator optimises the energy dispatch based on energy prosumers' predictions (previously performed by each participant using Boosting Decision Tree technique) and mixed integer linear programming. In the second stage, real time management is performed, where energy storage systems (individual Batteries and Central Energy Storage System) are employed to address the energy deviations. To address the viability of the proposed management, in the first place, the community-based P2P is compared to other Low Voltage configurations (Collective Self-Consumption and Full P2P) for one-stage management, where the community-based P2P approach obtains the best results in technical, economic, and environmental aspects. Subsequently, the two-stage management is examined with local Batteries and combining local Batteries with a Central Energy Storage System. The results show that the proposal, integrating local and central storage, significantly improves a local energy community's techno-economic and environmental indicators regarding other energy management market configurations.

关键词： Peer-to-peer energy trading Local Energy Market Two-stage energy management mixed-integer linear programming Local battery Community energy storage system

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