As global climate change continues to pose significant challenges, it is increasingly essential to explore sustainable agricultural development strategies. This study aims to develop a multi-objective collaborative op...
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As global climate change continues to pose significant challenges, it is increasingly essential to explore sustainable agricultural development strategies. This study aims to develop a multi-objective collaborative optimizationmodel, using the Fen River Irrigation District as a case study. It examines strategies based on the water- energy-food-carbon nexus and seeks to maximize bioenergy production. The research methodology integrates multi-objectiveoptimization theory with the ideal point method to obtain optimization solutions. This approach ensures the maximization of bioenergy output while minimizing carbon emissions and economic costs. The findings reveal that optimized bioenergy production in the study area can reach 1.17 x 1012 J, with contributions of 29.50 % from agriculture and 70.50 % from animal husbandry. Notably, animal husbandry emerges as the primary source of bioenergy production, generating 8.27 x 1011 J, predominantly from pigs, followed by sheep and cattle. The total optimized agricultural cultivation area is determined to be 6.76 x 104 ha, with corn taking the largest share at 73.86% of the total cultivated area, which improves the economic benefits of agriculture while increasing the production of bioenergy. Fruits and vegetables account for 8.69%, wheat for 3.45%, and legumes for 13.99%. In terms of the economic and environmental implications of bioenergy production, agriculture contributes more significantly to the agricultural economy compared to animal husbandry. Carbon dioxide (CO2) emissions are the major contributor to overall carbon emissions, followed by methane (CH4). The optimized allocation of water resources results in a more reasonable ratio between surface water and groundwater supply, with 0.41 x 108 m3 coming from groundwater and 1.93 x 108 m3 from surface water, effectively alleviating the problem of regional water resources tension and guaranteeing the long-term stability of agricultural production. The optimizationmodel f
With the increasing of demand diversification and short product lifecycle, industries encounter challenges of demand uncertainty. Japanese seru production system has received more attention with its efficiency and fle...
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ISBN:
(纸本)9781509063710
With the increasing of demand diversification and short product lifecycle, industries encounter challenges of demand uncertainty. Japanese seru production system has received more attention with its efficiency and flexibility. In this paper, a problem of seru production system formulation under uncertain condition is researched. A multi-objective optimization model of seru production system formulation problem is developed to minimize the cost and maximize the service level of the system. The purpose of this paper is to formulate an energy production system which can response to the stochastic demand efficiently. Sample average approximation is used to approximate the expected objective of the programming. Non-dominated sorting genetic algorithm II (NSGA-II) is improved to solve the multi-objective optimization model. Numerical experiments are conducted to test the tradeoff between cost and service level.
Gate assignment is one of the important tasks in the airport. The goal is to appoint an appropriate gate for the arrival or departure flight and ensures the flights are on schedule. So an appropriate and efficient gat...
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Gate assignment is one of the important tasks in the airport. The goal is to appoint an appropriate gate for the arrival or departure flight and ensures the flights are on schedule. So an appropriate and efficient gate assignment model can effectively decrease the flight delays and improve operation efficiency as well as service quality since it plays a major role in increasing revenues. In this paper, the multi-commodity network flow model is analyzed. And a robust multi-objective optimization model of gate assignment based on the objective of minimizing total time for passengers and balancing idle time for each gate is proposed to improve the service level and satisfaction of passengers and utilization rate of gates. The weighting method of linear is used to translate the multi-objective optimization model into the single objectiveoptimizationmodel. And ILOG CPLEX optimizer is used to solve the optimizationmodel of gate assignment in order to obtain the reasonable gate assignment result. The schedule data of 116 flights from Sheyang Taoxian International Airport of China are used to validate the effectiveness of the constructed optimizationmodel. And the multi-objective optimization model is compared with the existing mathematical model of gate assignment. The calculation and comparison results show that the constructed optimizationmodel can significantly improve the assignment efficiency and satisfaction, and balance the utilization rate of gates. So the constructed optimizationmodel of gate assignment can effectively provide a valuable reference for assigning gates.
New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer ...
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New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a multi-objective optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. (C) 2016 Elsevier Ltd. All rights reserved.
With the characteristics of high self-organized, dynamic, and interoperable, the wireless mesh network (WMN) is deemed as a potential technology to be applied widely for home, enterprise, and social public service. Ma...
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With the characteristics of high self-organized, dynamic, and interoperable, the wireless mesh network (WMN) is deemed as a potential technology to be applied widely for home, enterprise, and social public service. Many current optimization schemes usually focus on a single metric such as network deployment cost, throughput, QoS, and so on, but few schemes consider that the optimized metric may affect other metrics of WMN. In practice, the influence among the different metrics is often nonignorable. To optimize the performance from a global perspective, we propose a multi-objective optimization model based on immune algorithm (MOM-IA), which provides a paradigm to find the optimal solution satisfying some different restriction conditions. To simplify, MOM-IA mainly analyzes the restriction relationship of connectivity, redundancy, and throughput, which are the multiple objects. Considering the characteristic of dynamic and the discrete integer parameters in WMN, we design a longtime evolution immune algorithm to solve the MOM. Finally, the analysis of experiments presents that MOM-IA has good performance in solution set diversity and Pareto-front distribution, which means the probability to find the optimal solution in WMN. Copyright (c) 2014 John Wiley & Sons, Ltd.
The optimization of urban passenger transport structure can effectively save energy and reduce the carbon emission of transport. However, the carbon emission and energy consumption generated by the construction of tra...
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The optimization of urban passenger transport structure can effectively save energy and reduce the carbon emission of transport. However, the carbon emission and energy consumption generated by the construction of transport projects are worthy of attention. In this paper, a multi-objective optimization model of urban passenger transport structure oriented to low carbon is proposed considering passenger, operator, and construction. The optimal solution of the model is obtained based on the NSGA-II algorithm, and the validity of the model is verified with a case of Qingdao. The optimal ratio of Qingdao passenger traffic structure considering only the passenger perspective (PS), considering the passenger and operator (POS), and considering the three parties together (POCS) is obtained, respectively. The results show that the optimal structures obtained by the PS, POS, and POCS models increase the public transport passenger share by 12.74%, 20.74%, and 23.70%, compared with the actual values. From both the supply and demand sides, there has been an increase in the passenger share of public transport. The POS model is more suitable for solving structural optimization problems that do not involve construction carbon emissions in the short term. The POCS model is more suitable for long-term comprehensive structural optimization problems. The results of the study provide a reference basis for optimizing the urban passenger transport structure.
With the development of computer technology and theory, cloud computing technology suitable for modern computing and large-capacity storage has emerged. Cloud computing technology depends on the Internet. The advancem...
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With the development of computer technology and theory, cloud computing technology suitable for modern computing and large-capacity storage has emerged. Cloud computing technology depends on the Internet. The advancement and development of virtualization technologies such as cloud computing technology is the most cited of current computer technologies. With the development of cloud computing technology, this paper proposes a multi-group genetic algorithm based on interactive multi-functional processing methods, which can achieve the expectation of reducing the energy consumption of the cloud computing center to the greatest extent and at the same time ensure the stability and the running state of the server effectiveness. Through this survey, while reducing the company's operating costs, it will also contribute to energy conservation and environmental protection. multi-group genetic algorithms can effectively overcome the shortcomings of standard genetic algorithms, such as optimization range and efficiency, number of iterations, and premature end of iteration. The optimized result can reflect the subjective will of the decision-maker. Experimental examples show the efficiency of the algorithm, and the results show the applicability and feasibility of the algorithm. Based on the changes in the global atmospheric environment in recent years, this article discusses the reasons for the changes and focuses on the impact of human activities. In addition, the environmental issues that have received much attention today are the clues to discuss the climate issues and the harmful effects brought about by changes in the atmospheric environment, which has aroused people's attention to energy conservation and environmental protection.
The thinking model in mathematical problem solving (hereinafter referred to as TMMPS for short) is a kind of thinking model in the mathematical problem solving based on the multi-objective optimization model. In this ...
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ISBN:
(纸本)9781728121659
The thinking model in mathematical problem solving (hereinafter referred to as TMMPS for short) is a kind of thinking model in the mathematical problem solving based on the multi-objective optimization model. In this paper, a method for the feedback analysis of the thinking model in the mathematical problem solving combining the multi-objective optimization model is put forward. Based on the TMMPS, the multi-objective optimization model is added after the student stage to increase the learning efficiency of the students, maintain the characteristics of student diversity, and improve the independent learning abilities of the students for mathematics. The simulation results show that the TMPPS has advantages in both the optimization accuracy and the stability compared with other models.
In order to determine layout location of variable message signs(VMS) scientifically and reasonably and give full play to its transportation guidance effect, the optimal selection method of expressways VMS layout locat...
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ISBN:
(纸本)9783037857779
In order to determine layout location of variable message signs(VMS) scientifically and reasonably and give full play to its transportation guidance effect, the optimal selection method of expressways VMS layout location is studied based on general rules and requirements of expressways network VMS layout. Construct the multi-objective optimization model of VMS information utilization maximum, VMS effectiveness and VMS total cost minimum based on fuzzy constraint conditions, and the Hang-Jin-Qu expressway VMS layout is analyzed. The results show that the proposed multi-objective optimization model is more in line with the actual need of VMS layout and easy to operate, and has important practical value.
In order to find appropriate ventures for venture capitalists, this paper researches the match problem between ventures and venture capitalists through an intermediary. This paper gives six indexes for evaluating the ...
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ISBN:
(纸本)9781424421077
In order to find appropriate ventures for venture capitalists, this paper researches the match problem between ventures and venture capitalists through an intermediary. This paper gives six indexes for evaluating the satisfaction degree of venture capitalists and three indexes for evaluating the satisfaction degree of ventures. Then, according to the satisfaction degree of ventures and venture capitalists and the commission of an intermediary, it proposes a multi-objective optimization model and gives the corresponding solution to the model. Finally, a numerical example is given to show the feasibility and efficiency of the proposed model.
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