The Experts below are selected from a list of 504126 Experts worldwide ranked by ideXlab platform
Linfeng Yang - One of the best experts on this subject based on the ideXlab platform.
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a feasibility pump based Solution Algorithm for two stage robust optimization with integer recourses of energy storage systems
IEEE Transactions on Sustainable Energy, 2021Co-Authors: Cuo Zhang, Zhao Yang Dong, Linfeng YangAbstract:To address uncertainties, two-stage robust optimization (TSRO) methods have been widely used for power system operation and planning problems. However, solving TSRO problems with integer recourses of energy storage systems (ESSs) is challenging. This letter proposes a feasibility pump based column and constraint generation (FP-CCG) Solution Algorithm to solve these TSRO problems. In this letter, firstly, a TSRO problem of ESS operation with state-of-charge management is formulated. Then, the FP-CCG Algorithm is proposed and applied to solve this TSRO problem. Simulation results indicate full Solution robustness and high computation efficiency of the FP-CCG Solution Algorithm.
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a feasibility pump based Solution Algorithm for two stage robust optimization with integer recourses of energy storage systems
IEEE Transactions on Sustainable Energy, 2021Co-Authors: Cuo Zhang, Zhao Yang Dong, Linfeng YangAbstract:To address uncertainties, two-stage robust optimization (TSRO) methods have been widely used for power system operation and planning problems. However, solving TSRO problems with integer recourses of energy storage systems (ESSs) is challenging. This article proposes a feasibility pump based column and constraint generation (FP-CCG) Solution Algorithm to solve these TSRO problems. In this article, firstly, a TSRO problem of ESS operation with state-of-charge management is formulated. Then, the FP-CCG Algorithm is proposed and applied to solve this TSRO problem. Simulation results indicate full Solution robustness and high computation efficiency of the proposed FP-CCG Solution Algorithm.
Fengqi You - One of the best experts on this subject based on the ideXlab platform.
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optimal supply chain design and operations under multi scale uncertainties nested stochastic robust optimization modeling framework and Solution Algorithm
Aiche Journal, 2016Co-Authors: Dajun Yue, Fengqi YouAbstract:Although strategic and operational uncertainties differ in their significance of impact, a “one-size-fits-all” approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model that handles multi-scale uncertainties in a holistic framework, aiming to optimize the expected economic performance while ensuring the robustness of operations. Stochastic programming and robust optimization approaches are integrated in a nested manner to reflect the decision maker's different levels of conservativeness toward strategic and operational uncertainties. The resulting multi-level mixed-integer linear programming model is solved by a decomposition-based column-and-constraint generation Algorithm. To illustrate the application, a county-level case study on optimal design and operations of a spatially-explicit biofuel supply chain in Illinois is presented, which demonstrates the advantages and flexibility of the proposed modeling framework and efficiency of the Solution Algorithm. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3041–3055, 2016
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deciphering and handling uncertainty in shale gas supply chain design and optimization novel modeling framework and computationally efficient Solution Algorithm
Aiche Journal, 2015Co-Authors: Jiyao Gao, Fengqi YouAbstract:The optimal design and operations of shale gas supply chains under uncertainty of estimated ultimate recovery (EUR) is addressed. A two-stage stochastic mixed-integer linear fractional programming (SMILFP) model is developed to optimize the levelized cost of energy generated from shale gas. In this model, both design and planning decisions are considered with respect to shale well drilling, shale gas production, processing, multiple end-uses, and transportation. To reduce the model size and number of scenarios, we apply a sample average approximation method to generate scenarios based on the real-world EUR data. In addition, a novel Solution Algorithm integrating the parametric approach and the L-shaped method is proposed for solving the resulting SMILFP problem within a reasonable computational time. The proposed model and Algorithm are illustrated through a case study based on the Marcellus shale play, and a deterministic model is considered for comparison. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3739–3755, 2015
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integrated scheduling and dynamic optimization by stackelberg game bilevel model formulation and efficient Solution Algorithm
Industrial & Engineering Chemistry Research, 2014Co-Authors: Yunfei Chu, Fengqi YouAbstract:We propose a novel method to solve the integrated scheduling and dynamic optimization problem for sequential batch processes. The scheduling problem and the dynamic optimization problems are collaborated by a Stackelberg game (leader–followers game). Mathematically, the integrated problem is formulated into a bilevel program. The scheduling problem in the upper level acts as the leader, while the dynamic optimization problems in the lower level are the followers. The follower problems have their own objectives, but the leader problem can coordinate the follower problems to pursue its objective. To efficiently solve the bilevel program, we develop a decomposition Algorithm. It first solves the lower-level problems to determine the response functions. The response functions are then represented by piecewise linear functions to solve the upper-level problem. The integrated method is consistent with the ISA 95 standard and can be easily implemented in an IT infrastructure following the standard. The performan...
Xuesong Zhou - One of the best experts on this subject based on the ideXlab platform.
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solving the time dependent multi trip vehicle routing problem with time windows and an improved travel speed model by a hybrid Solution Algorithm
Cluster Computing, 2019Co-Authors: Yan Sun, Danzhu Wang, Maoxiang Lang, Xuesong ZhouAbstract:In this study, we explore a time-dependent multi-trip vehicle routing problem (TDMTVRP) with an improved travel speed model. This problem is set in a scenario that (a) a set of customers with fixed demands and service time windows have to be served in a sequence of service trips which originate and terminate at a distribution centre, (b) the service trips will be assigned to a fleet of vehicles with fixed capacities and maximum allowable working durations each day, (c) each vehicle can perform more than one service trip, and (d) the link travel times varies with vehicle travel speeds which results from congestion effects during different time of day in urban areas. The aim of the TDMTVRP model is to find an optimal strategy to minimize the vehicle utilized times and their total scheduling time. A continuous piecewise linear function is first introduced to represent the variation and transition of vehicle travel speeds with the time of the day instead of the traditional staircase travel speed function. Then a hybrid Solution Algorithm is developed by using the nearest-neighbour heuristic to obtain an initial Solution and Tabu search heuristic to search the optimal Solution. Finally, an experimental case study is used to verify the feasibility of the proposed model and Algorithm. The experimental results indicate that compared with the CVRPTW (capacitated vehicle routing problem with time windows) model, the TDMTVRP model proposed in this study can both decrease the vehicle utilized times dramatically and shorten the vehicle travel distances slightly in dealing with the vehicle routing problem.
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constraint reformulation and a lagrangian relaxation based Solution Algorithm for a least expected time path problem
Transportation Research Part B-methodological, 2014Co-Authors: Lixing Yang, Xuesong ZhouAbstract:Abstract Using a sample-based representation scheme to capture spatial and temporal travel time correlations, this article constructs an integer programming model for finding the a priori least expected time paths. We explicitly consider the non-anticipativity constraint associated with the a priori path in a time-dependent and stochastic network, and propose a number of reformulations to establish linear inequalities that can be easily dualized by a Lagrangian relaxation Solution approach. The relaxed model is further decomposed into two sub-problems, which can be solved directly by using a modified label-correcting Algorithm and a simple single-value linear programming method. Several Solution Algorithms, including a sub-gradient method, a branch and bound method, and heuristics with additional constraints on Lagrangian multipliers, are proposed to improve Solution quality and find approximate optimal Solutions. The numerical experiments investigate the quality and computational efficiency of the proposed Solution approach.
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stochastic optimization model and Solution Algorithm for robust double track train timetabling problem
IEEE Transactions on Intelligent Transportation Systems, 2010Co-Authors: Muhammad Babar Khan, Xuesong ZhouAbstract:By considering various stochastic disturbances unfolding in a real-time dispatching environment, this paper develops a stochastic optimization formulation for incorporating segment travel-time uncertainty and dispatching policies into a medium-term train-timetabling process that aims to minimize the total trip time in a published timetable and reduce the expected schedule delay. Based on a heuristic sequential Solution framework, this study decomposes the robust timetabling problem into a series of subproblems that optimize the slack-time allocation for individual trains. A number of illustrative examples are provided to demonstrate the proposed model and Solution Algorithms using data collected from a Beijing-Shanghai high-speed rail corridor in China.
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equivalent gap function based reformulation and Solution Algorithm for the dynamic user equilibrium problem
Transportation Research Part B-methodological, 2009Co-Authors: Hani S Mahmassani, Xuesong ZhouAbstract:A variety of analytical and simulation-based models and Algorithms have been developed for the dynamic user equilibrium (DUE) traffic assignment problem. This paper aims to develop a theoretically sound simulation-based DUE model and its Solution Algorithm, with particular emphasis on obtaining Solutions that satisfy the DUE conditions. The DUE problem is reformulated, via a gap function, as a nonlinear minimization problem (NMP). The NMP is then solved by a column generation-based optimization procedure which embeds (i) a simulation-based dynamic network loading model to capture traffic dynamics and determine experienced path travel costs for a given path flow pattern and (ii) a path-swapping descent direction method to solve the restricted NMP defined by a subset of feasible paths. The descent direction method circumvents the need to compute the gradient of the objective function in finding search directions, or to determine suitable step sizes, which is especially valuable for large-scale simulation-based DUE applications. Computational results for both small and large real road networks confirm that the proposed formulation and Solution Algorithm are effective in obtaining near-optimal Solutions to the DUE problem.
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variable toll pricing and heterogeneous users model and Solution Algorithm for bicriterion dynamic traffic assignment problem
Transportation Research Record, 2006Co-Authors: Chungcheng Lu, Xuesong Zhou, Hani S MahmassaniAbstract:A dynamic traffic assignment model and its Solution Algorithm for the bicriterion dynamic user equilibrium (BDUE) problem that allows for heterogeneous users with different value-of-time (VOT) preferences are presented. Assuming the VOT as a continuously distributed random variable across the population of trips, the BDUE problem is formulated as a system of infinite-dimensional variational inequalities (VIs). Rather than solving the VI formulation directly, this study employs a generalized Frank-Wolfe Algorithm to find the BDUE flow pattern. A bicriterion time-dependent least-cost path Algorithm is applied to generate the extreme efficient path set, and the corresponding breakpoints naturally define the multiple user classes and thereby generate the descent direction for a multiclass dynamic network loading. A traffic simulator is used to describe the traffic flow propagation and the spatial and temporal interactions. To circumvent the difficulty of storing the memory-intensive path set and routing polic...
Hsiaodong Chiang - One of the best experts on this subject based on the ideXlab platform.
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electric distribution system load capability problem formulation Solution Algorithm and numerical results
IEEE Transactions on Power Delivery, 2000Co-Authors: Hsiaodong ChiangAbstract:This paper undertakes the problem of determining the load capability of distribution networks, or, equivalently, the amount of load a feeder, specific area or circuit of a large-scale unbalanced distribution network can withstand before violating an operational constraint. A new formulation is given. A Solution Algorithm suitable for large-scale unbalanced distribution networks with capacitor control actions is developed and test results on a NYSEG 394-bus distribution network are included.
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service restoration for unbalanced radial distribution systems with varying loads Solution Algorithm
Power Engineering Society Summer Meeting, 1999Co-Authors: Karen Nan Miu, Hsiaodong ChiangAbstract:This paper presents a service restoration Algorithm for distribution systems which considers the timing of the switch operation sequence and the projected load changes which normally occur during the restoration process. It is imperative to determine a feasible sequence of switch status changes that considers the normal change in serviced load and increases in the outage loads. The Algorithm employs unbalanced power flow studies for rigorous constraint checking and produces a feasible sequence of switch operations with respect to anticipated load changes.
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fast decoupled power flow for unbalanced radial distribution systems
IEEE Transactions on Power Systems, 1995Co-Authors: Ray D Zimmerman, Hsiaodong ChiangAbstract:This paper presents a novel power flow formulation and an effective Solution method for general unbalanced radial distribution systems. Comprehensive models are considered including lines, switches, transformers, shunt capacitors, cogenerators, and several types of loads. A new problem formulation of three-phase distribution power flow equations taking into account the radial structure of the distribution network is presented. A distinguishing feature of the new problem formulation is that it significantly reduces the number of power flow equations, as compared with the conventional formulation. The numerical properties as well as the structural properties of distribution systems are exploited resulting in a fast decoupled Solution Algorithm. The proposed Solution Algorithm is evaluated on three-phase unbalanced 292-bus and 394-bus test systems with very promising results.
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an efficient Algorithm for real time network reconfiguration in large scale unbalanced distribution systems
IEEE Transactions on Power Systems, 1995Co-Authors: Jincheng Wang, Hsiaodong Chiang, G DarlingAbstract:Real-time applications demand fast computation, and this paper proposes an efficient Algorithm for real-time network reconfiguration on large unbalanced distribution networks. A novel formulation of the network reconfiguration to achieve loss minimization and load balancing is given. To reduce computational requirements for the Solution Algorithm, well justified power flow and loss reduction formulae in terms of the on/off status of network switches are proposed for efficient system updating. The Algorithm relies only on a few full power flow studies based on system states attained by explicit expressions using backward-forward sweeps for efficient computation of power system states at the critical system operating points. The Solution Algorithm runs in an amount of time linearly proportional to the number of tie switches and the number of sectionalizing switches in the system. The Solution Algorithm has been implemented into a software package and tested on unbalanced distribution systems including a system with 292 buses, 76 laterals, 7 transformers, 45 switches and 255 line sections under diverse system conditions.
Jinsu Mun - One of the best experts on this subject based on the ideXlab platform.
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A Solution Algorithm for a dynamic deterministic user equilibrium assignment model with departure time choice
Transportation Planning and Technology, 2011Co-Authors: Jinsu MunAbstract:A route-based combined model of dynamic deterministic route and departure time choice and a Solution method for many origin and destination pairs is proposed. The divided linear travel time model is used to calculate the link travel time and to describe the propagation of flow over time. For the calculation of route travel times, the predictive ideal route travel time concept is adopted. Solving the combined model of dynamic deterministic route and departure time choice is shown to be equivalent to solving simultaneously a system of non-linear equations. A Newton-type iterative scheme is proposed to solve this problem. The performance of the proposed Solution method is demonstrated using a version of the Sioux Falls network. This shows that the proposed Solution method produces good equilibrium Solutions with reasonable computational cost.
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a model and Solution Algorithm for dynamic deterministic user equilibrium assignment
Transportation Planning and Technology, 2009Co-Authors: Jinsu MunAbstract:In this paper a route-based dynamic deterministic user equilibrium assignment model is presented. Some features of the linear travel time model are first investigated and then a divided linear travel time model is proposed for the estimation of link travel time: it addresses the limitations of the linear travel time model. For the application of the proposed model to general transportation networks, this paper provides thorough investigations on the computational issues in dynamic traffic assignment with many-to-many OD pairs and presents an efficient Solution procedure. The numerical calculations demonstrate that the proposed model and Solution Algorithm produce satisfactory Solutions for a network of substantial size with many-to-many OD pairs. Comparisons of assignment results are also made to show the impacts of incorporation of different link travel time models on the assignment results.
