Unbalanced feeder systems cause deteratorating power quality and increase investment and operating costs. Feeder reconfiguration and phase swapping are two popular methods to balance the systems. For an unbalanced fee...
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Unbalanced feeder systems cause deteratorating power quality and increase investment and operating costs. Feeder reconfiguration and phase swapping are two popular methods to balance the systems. For an unbalanced feeder system, feeder reconfiguration is difficult to meet the phase balancing criterion due to the limited number of sectionalizing switches available. Phase swapping is another alternative and direct approach for phase balancing. Phase swapping has not received its deserved attention due to the complexity of feeder systems, the dimension of problems, and totally overlooking the impacts of phase imbalance. Phase swapping can economically and effectively balance the feeder systems to improve power quality and reduce power system total cost. Deregulation arises competition on power quality, service reliability, and electricity price. Therefore phase swapping can enhance utilities competitive capability. This paper proposes a mixed-integerprogramming formulation for phase swapping optimization. Single-phase loads are treated differently with three-phase loads. Nodal phase swapping and lateral phase swapping are also introduced. An example is used to illustrate the proposed method.
Classification is a procedure to separate data or alternatives into two or more classes. In practice, the need to classify alternatives involving multiple criteria into distinct classes is considerable. Therefore, det...
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Classification is a procedure to separate data or alternatives into two or more classes. In practice, the need to classify alternatives involving multiple criteria into distinct classes is considerable. Therefore, determining how to assist decision makers in classifying alternatives into multiple classes is an important issue in the field of multiple-criteria decision aids. This study proposes a two-phase case-based distance approach used to assist decision makers to classify alternatives into multiple groups. By incorporating the advantages of the case-based distance method, the proposed two-phase approach can classify alternatives by evaluating a set of cases selected by decision makers, reduce the number of misclassifications, improve multiple solution problems, and lessen the impact of outliers. An interactive classification procedure is also proposed to provide flexibility in such a way that decision makers can check and adjust classification results iteratively. (C) 2012 Elsevier Ltd. All rights reserved.
Electrification of the truck fleet has the potential to reduce the "harder-to-abate"emissions of logistics significantly, but is generally considered to be very challenging. In this study, we focus on the en...
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Electrification of the truck fleet has the potential to reduce the "harder-to-abate"emissions of logistics significantly, but is generally considered to be very challenging. In this study, we focus on the energy- efficient routing of a mixed fleet of conventional and electric heavy-duty trucks with pickup and delivery under energy consumption uncertainty. We propose an energy consumption model that accounts for realistic driving dynamics, road conditions, weight, and distances. Integrating this model into the routing problem, we address energy consumption uncertainty using second-order cone mixed-integerprogramming. A quantitative case study is then performed on the operating costs and CO2 2 emissions benefits of electrifying heavy-duty trucks, which demonstrates improved fleet performance with optimal operating results. Scenarios with different parameter settings are tested to compare different performance metrics and provide practical insights. We evaluate routing decisions to demonstrate that stochastic optimization is necessary for reliable truck routing and produces robust results that significantly reduce capacity violations in route execution.
Long-term storage(LTS)can provide various services to address seasonal fluctuations in variable renewable energy by reducing energy ***,long-term unit commitment(UC)with LTS involves mixed-integerprogramming with lar...
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Long-term storage(LTS)can provide various services to address seasonal fluctuations in variable renewable energy by reducing energy ***,long-term unit commitment(UC)with LTS involves mixed-integerprogramming with large-scale coupling constraints between consecutive intervals(state-of-charge(SOC)constraint of LTS,ramping rate,and minimum up/down time constraints of thermal units),resulting in a significant computational ***,an iterative-based fast solution method is proposed to solve the long-term UC with ***,the UC with coupling constraints is split into several sub problems that can be solved in ***,the solutions of the sub problems are adjusted to obtain a feasible solution that satisfies the coupling ***,a decoupling method for long-term time-series coupling constraints is proposed to determine the global optimization of the SOC of the *** price-arbitrage model of the LTS determines the SOC boundary of the LTS for each sub ***,the sub problem with the SOC boundary of the LTS is iteratively solved *** proposed method was verified using a modified IEEE 24-bus *** results showed that the computation time of the unit combination problem can be reduced by 97.8%,with a relative error of 3.62%.
This paper studies limited-stop operations for rail transit systems and presents an optimization model, which aims to minimize passengers' travel times and on-board crowdedness. The model combines the design of se...
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This paper studies limited-stop operations for rail transit systems and presents an optimization model, which aims to minimize passengers' travel times and on-board crowdedness. The model combines the design of service itinerary for each vehicle and the allocation of vehicles of various types among multiple lines. Three methods are proposed including a mixed-integer program and two column generation algorithms. Numerical results on generic scenarios show that the column generation algorithm can provide sub-optimal solutions and is computationally efficient. In a case study on Melbourne's railway network, its solution significantly improves in the passenger travel time compared to the current practice.
We address a capacitated vehicle routing problem (CVRP) in which the demand of a node can be split on several vehicles celled split services by assuming heterogeneous fixed fleet. The objective is to minimize the flee...
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We address a capacitated vehicle routing problem (CVRP) in which the demand of a node can be split on several vehicles celled split services by assuming heterogeneous fixed fleet. The objective is to minimize the fleet cost and total distance traveled. The fleet cost is dependent on the number of vehicles used and the total unused capacity. In most practical cases, especially in urban transportation, several vehicles transiting on a demand point occurs. Thus, the split services can aid to minimize the number of used vehicles by maximizing the capacity utilization. This paper presents a mix-integer linear model of a CVRP with split services and heterogeneous fleet. This model is then solved by using a simulated annealing (SA) method. Our analysis suggests that the proposed model enables users to establish routes to serve all given customers using the minimum number of vehicles and maximum capacity. Our proposed method can also find very good solutions in a reasonable amount of time. To illustrate these solutions further, a number of test problems in small and large sizes are solved and computational results are reported in the paper. (c) 2006 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Portfolio optimization models are widely adopted in asset management, quantitative trading, and other applications. Relative robust portfolio optimization further considers the situation that the optimization result o...
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ISBN:
(纸本)9781728168739
Portfolio optimization models are widely adopted in asset management, quantitative trading, and other applications. Relative robust portfolio optimization further considers the situation that the optimization result of the absolute robust optimization model only depends on the worst case. To apply the relative robust portfolio model to inseparable assets, this paper proposes an integer relative robust optimization model based on mixed -integerprogramming. The experimental results show that the integer relative robust portfolio model can achieve a higher rate of return, lower relative risk, and superior balance between robustness and profitability. Furthermore, to deal with massive computing loads of the model when applied to large-scale assets and largescale historical data, a parallel version of the integer relative robust optimization model is implemented with NIPI, that can achieve excellent speedup ratio and scalability.
This paper proposes an iterative mathematical optimization framework to solve the layout and hydraulic design problems of sewer networks. The layout selection model determines the flow rate and direction per pipe usin...
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This paper proposes an iterative mathematical optimization framework to solve the layout and hydraulic design problems of sewer networks. The layout selection model determines the flow rate and direction per pipe using mixed-integerprogramming, which results in a tree-like structured network. This network layout parametrizes a second model that determines hydraulic features including the diameter and the upstream and downstream invert elevations of pipes using a shortest path algorithm. These models are embedded in an iterative scheme that refines a cost function approximation for the first model upon learning the actual design cost from the second model. The framework was successfully tested on two sewer network benchmarks from the literature and a real sewer network located in Bogota, Colombia, that is proposed as a new instance. For both benchmarks, the proposed methodology found a better solution with up to 42% cost reduction compared to the best methodologies reported in the literature. These are near-optimal solutions with respect to construction cost that satisfy all hydraulic and pipe connectivity constraints of a sewer system.
作者:
Saldarriaga, JuanHerran, JuanaUniv Andes
Water Distribut & Sewerage Syst Res Ctr Dept Civil & Environm Engn Bogota Colombia Univ Andes
Water Distribut & Sewerage Syst Res Ctr Dept Civil & Environm Engn Bogota Colombia
When designing a sewer system, the goal is to find the design with the lowest cost while complying with the hydraulic constraints. However, with the emergence of new concerns like climate change and high-density urban...
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When designing a sewer system, the goal is to find the design with the lowest cost while complying with the hydraulic constraints. However, with the emergence of new concerns like climate change and high-density urbanization, those designs must also be reliable and resilient. This is the objective of the present research, to provide an approach for finding low-cost, sewer networks design while considering the reliability and the resilience as a criterion in the selection of the best design. For that purpose, a method is proposed for evaluating resilience and reliability in a variety of designs produced by modifying the objective function of the network's layout selection model. The methodology was tested in four scenarios using two cost equations and two sewer network benchmarks from the literature. In most of the scenarios, it was possible to find designs with lower cost and higher resilience and reliability than those previously published.
Portfolio optimization models are widely adopted in asset management, quantitative trading, and other applications. Relative robust portfolio optimization further considers the situation that the optimization result o...
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ISBN:
(数字)9781728168739
ISBN:
(纸本)9781728168746
Portfolio optimization models are widely adopted in asset management, quantitative trading, and other applications. Relative robust portfolio optimization further considers the situation that the optimization result of the absolute robust optimization model only depends on the worst case. To apply the relative robust portfolio model to inseparable assets, this paper proposes an integer relative robust optimization model based on mixed-integerprogramming. The experimental results show that the integer relative robust portfolio model can achieve a higher rate of return, lower relative risk, and superior balance between robustness and profitability. Furthermore, to deal with massive computing loads of the model when applied to large-scale assets and largescale historical data, a parallel version of the integer relative robust optimization model is implemented with MPI, that can achieve excellent speedup ratio and scalability.
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