This paper proposes an innovative procedure of finding efficient facility location–allocation (FLA) schemes, integrating data envelopment analysis (DEA) and a multi-objective programming (MOP) model methodology. FLA ...
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In this study we evaluate the research performance of the Computer Science departments in United Kingdom. We consider the research activity as a series process with two components, where the first component portrays t...
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ISBN:
(纸本)9781728149592
In this study we evaluate the research performance of the Computer Science departments in United Kingdom. We consider the research activity as a series process with two components, where the first component portrays the research productivity and the second component portrays the impact of the research outcomes of each department. The analysis is based on the data drawn from the Research Excellence Framework 2014 (REF 2014), which is the system for assessing the quality of research in the higher education institutions of United Kingdom. We carry out the assessment by employing the composition approach of Network Data Envelopment Analysis (Network DEA). Also, we encompass a qualitative aspect into the exercise based on the categorization of the publications provided by REF 2014.
Voltage/VAR control (VVC) methods implemented by on-load tap-changers, capacitor banks and photovoltaic (PV) associated inverters can efficiently maintain power quality (reduce voltage deviation) and reduce energy los...
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ISBN:
(纸本)9781728119816
Voltage/VAR control (VVC) methods implemented by on-load tap-changers, capacitor banks and photovoltaic (PV) associated inverters can efficiently maintain power quality (reduce voltage deviation) and reduce energy loss for active distribution networks (ADNs). However, minimization of voltage deviation is in competition with minimization of energy loss. Besides, uncertainties such as PV power generation and load demand impair control results. To address these issues, this paper proposes a multi-objective VVC model based on a multi-stage coordination framework for ADNs, aiming to minimize voltage deviation and energy loss simultaneously. More importantly, this paper proposes a multi-objective robust optimization approach to robustly optimize the multi-objective VVC model against uncertainties. Correspondingly, a solution algorithm based on adaptive weighted-sum and column-and-constraint generation algorithms is developed to solve the multi-objective robust optimization problem. Simulation results show well distributed solutions and high solution robustness of the proposed multi-objective robust VVC against uncertainties.
作者：
Zuo, RanYang, YueBeijing Jiaotong Univ
Sch Traff & Transportat MOE Key Lab Urban Transportat Complex Syst Theory Beijing Peoples R China Beijing Jiaotong Univ
Sch Traff & Transportat State Key Lab Rail Traff Control & Safety Beijing Peoples R China
In the event of natural disasters and other modes of transport failures, resulting in a sudden surging passenger flow in transportation hub, aimed at the minimum of passenger evacuation time and the lowest cost of the...
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ISBN:
(纸本)9781728100425
In the event of natural disasters and other modes of transport failures, resulting in a sudden surging passenger flow in transportation hub, aimed at the minimum of passenger evacuation time and the lowest cost of the operating company, the combination of minimization model is established, the operating frequency, the length of train classification, the configuration number and the full load rate taken as constraints. In the model, the weights of the two optimization targets are coordinated by the decision-maker's intention to get more benefiting results. The Nanjing Metro Line 1 is taken as an example to get train operating frequency and the urban rail train operation schedule under emergency conditions is worked out. The results show that the model designed in this paper can satisfy the need of passenger evacuation under emergency conditions.
this paper proposes an alternative analytical algorithm for a general nonlinear multi-objective optimization problem. Rather than employing the vertical coordinate system as in the existing analytical algorithms, our ...
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ISBN:
(纸本)9781728138480
this paper proposes an alternative analytical algorithm for a general nonlinear multi-objective optimization problem. Rather than employing the vertical coordinate system as in the existing analytical algorithms, our algorithm adopts the parallel coordinate system. This new algorithm can guarantee to obtain exact Pareto optimum, which is independent of the relative scales of the objective functions and successful in producing representative points in the Pareto set. The composition of these properties is missing from the existing popular methods like weighted sum or epsilon - constraints. Furthermore, this algorithm can interact with the preferences of decision makers rapidly at any precision to obtain the expected Pareto optimum and with smaller computation expense. Finally, numerical examples are conducted to verify the effectiveness of the proposed algorithm.
In order to meet the humanized requirements of course scheduling in colleges and universities, a multi-objective optimization course scheduling model with soft constraints is established. According to the characterist...
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ISBN:
(纸本)9781538677322
In order to meet the humanized requirements of course scheduling in colleges and universities, a multi-objective optimization course scheduling model with soft constraints is established. According to the characteristics of many constraints and large search space of course scheduling model, the unique coding method and optimized initial population generation operator are designed, and genetic operators such as crossover and mutation are also designed for the problem. The experimental results demonstrate that the proposed algorithm can obtain the high quality course schedule according to the proposed soft constraints, and it can avoid typical genetic algorithm to fall into local optimum problem.
Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be...
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Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be considered based on environmental protection regulations of countries. In this thesis, environmental concerns with a mathematical model are investigated to design and configure a multi-period, multi-product, multi-echelon green meat supply chain network. A multi-objective mixed-integer linear programming formulation is developed to optimize three objectives simultaneously: minimization of the total cost, minimization of the total CO2 emissions released from transportation, and maximization of the total capacity utilization. To demonstrate the efficiency of the proposed optimization model, a green meat supply chain network for Southern Ontario, Canada is designed. A solution approach based on augmented εε-constraint method is developed for solving the proposed model. As a result, a set of Pareto-optimal solutions is obtained. Finally, the impacts of uncertainty on the proposed model are investigated using several decision trees. Optimization of a food supply chain, particularly a meat supply chain, based on multiple objectives under uncertainty using decision trees is a new approach in the literature.
The decarbonization of the power sector has led many academic, governmental and non-governmental institutions to design scenarios with 100% renewable energy and several options to compensate the mismatch between deman...
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The decarbonization of the power sector has led many academic, governmental and non-governmental institutions to design scenarios with 100% renewable energy and several options to compensate the mismatch between demand and generation have been presented. This paper addresses the benefits to renewable energy integration ensured by the complementarity, and flexibility options, such as demand-side management and batteries. A non-linear multi-objective problem is proposed that maximizes the complementarity and minimizes the total expansion cost. The model also optimizes the battery integration, complementarity between sources and regions, costs, demand-side management, hydropower storage requirements and spatial technology distribution with hourly resolution. The methodology is implemented for the Brazilian case and a scenario able to guarantee three consecutive years of extreme drought in 2050 is presented, without the need of new large reservoirs. Such scenario is ensured with 10% of solar energy, 43% of wind energy, 4.3% of biomass, 41% of hydropower and 1.7% of demand-side management.
Aiming at the requirement of working efficiency and security of automated warehouse and taking the operation time of outbound-inbound, the equivalent center of gravity of overall shelf and the degree of relative accum...
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Aiming at the requirement of working efficiency and security of automated warehouse and taking the operation time of outbound-inbound, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products as the multi-objective functions, the mathematical model is constructed for multi-objective storage location allocation optimization. According to the simple weighted genetic algorithm, it is easily prone to the problem of immature convergence when solving multi-objective programming problems. So, the multi-population genetic algorithm is proposed to solve the mathematical model of storage location allocation optimization. Combining with the experiment data of toy car assembly and automated warehouse, the results of the automated warehouse storage location allocation are obtained. FlexSim dynamic simulation model is established based on the storage location allocation solution, the physical parameters of automated warehouse and the experimental requirements plan of vehicle model assembly. The operation effect of the model and the utilization rate of the equipment are analyzed. The result of multi-population genetic algorithm is more reasonable and effective. It is proved that the result of multi-population genetic algorithm is superior to the result of simple weighted genetic algorithm, which provides an effective method for storage location allocation optimization and outbound-inbound dynamic simulation.
In this paper, we discuss a multi-period portfolio selection with discounted transaction costs in a fuzzy uncertain investment environment, which has not been given much attention before. We assume that an investor...
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In this paper, we discuss a multi-period portfolio selection with discounted transaction costs in a fuzzy uncertain investment environment, which has not been given much attention before. We assume that an investor's motivation is to find the portfolio with maximizing terminal wealth and the cumulative skewness on portfolios, and minimizing the cumulative risk on portfolios. We consider the major criteria including wealth, risk, skewness, transaction costs, proportion entropy, transaction lots, the maximum holding number of assets in the portfolio and budge constraint. We propose a possbilistic mean-semivariance-skewness model with discounted transaction costs for multi-period fuzzy portfolio selection. To solve the multi-objective portfolio selection model, we first introduce a weighted max-min fuzzy goal programming approach to take investor's different investment preferences into account and transform it into a single-objectiveprogramming problem and then design a dynamic differential evolution algorithm for solution. Finally, we provide an empirical study with the sample data from Chinese stock market to analyze the application of the model and the performance of the solution algorithm.
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