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 ...
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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 throu...
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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 ...
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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.
Heavy-haul railways (HHRs) pose significant challenges due to their substantial traction weight, extended train length, and complex operational environments. Heavy-haul trains (HHTs), equipped with traditional pneumat...
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Heavy-haul railways (HHRs) pose significant challenges due to their substantial traction weight, extended train length, and complex operational environments. Heavy-haul trains (HHTs), equipped with traditional pneumatic control braking systems, must adopt cycle braking strategies on long downhill slopes. The varying traction masses of HHTs on these railways lead to diverse maneuvering characteristics, presenting challenges for drivers and dispatchers in unforeseen circumstances. To enhance transportation efficiency and mitigate operational complexities, a trajectory optimization method is formulated for determining the optimal trajectory of HHTs with different traction masses under complex conditions, including long downhill slopes, temporary speed limit sections, and regular sections. It considers the dynamics of train traction, braking, and coasting at each phase, optimizing objectives such as train operation efficiency, energy consumption, and pneumatic braking times. A linear weight search algorithm ensures punctuality, and the model is linearized into a mixed-integer linear programming (MILP) form using segmented and stepwise functions to align with operational realities. Simulation experiments utilizing real data and various HHT configurations validate the efficacy of the proposed approach against alternative methods. This method offers precise trajectory optimization under complex conditions, providing valuable guidance for dispatchers and drivers in the heavy-haul railway sector.
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 Min...
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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 particula...
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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.
作者:
Ye, HuigenXu, HuaCoello, Carlos A. CoelloTsinghua 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 mixedintegerlinear Programs (MILPs), as they can capture the mapping between problem structures and optimal...
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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...
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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
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 rel...
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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 linearprogramming 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
Manufacturing enterprises employ a series of production or assembly operations to manufacture products. These operations are performed in a particular sequence. It is crucial to establish the job sequence that will un...
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