This paper proposes a distributed control framework to optimize the offset for a path in a traffic network with arbitrary topology. Each intersection along the target path applies the model predictive control to optim...
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This paper proposes a distributed control framework to optimize the offset for a path in a traffic network with arbitrary topology. Each intersection along the target path applies the model predictive control to optimize their own phase sequence and green splits with the objective of minimizing the sum of queue lengths. The first intersection on this path is regarded as the main intersection and responsible for optimizing the start green time and duration of the first phase on this path with a weighted objective according to the real-time traffic information, while the other intersections take the constraints of offset imposed by intersections ahead into consideration. The signal cycles of these intersections are fixed but allowed to be different. For computation efficiency, the nonlinear optimization problem is approximately reformulated as a mixed-integer linear programming problem. Numerical experiments on a calibrated network of Caohejing District in Shanghai indicate that our proposed method can effectively decrease delay time and waiting time especially at medium and high traffic loads. Copyright (C) 2020 The Authors.
Recently we proposed a new mixed-integer linear programming formulation for the Multi-Target Tracking (MTT) problem and used a standard optimization solver to demonstrate its viability [1]. Subsequently, we provided G...
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ISBN:
(纸本)9780578647098
Recently we proposed a new mixed-integer linear programming formulation for the Multi-Target Tracking (MTT) problem and used a standard optimization solver to demonstrate its viability [1]. Subsequently, we provided Graphics Processing Unit (GPU) accelerated algorithms for the underlying Multi-dimensional Assignment Problem (MAP) with decomposable costs or triplet costs using a Lagrangian Relaxation (LR) framework. Here, we present a Dual-Ascent algorithm that provides monotonically increasing lower bounds and converges in a fraction of iterations required for a subgradient scheme. This approach can handle a large number of targets for many time steps with massive parallelism and computational efficiency. The dual-ascent framework decomposes the MAP into a set of linear Assignment Problems (LAPs) for adjacent time-steps, which can be solved in parallel using the GPU-accelerated method of [2], [3]. The overall dual-ascent algorithm is able to efficiently solve problems with 100 targets and 100 time-frames with high accuracy. We demonstrate the applicability of our new algorithm to MTT by including realistic issues of missed detections and false alarms. Computational results demonstrate the robustness of the algorithm with good MMEP and ITCP scores and solution times for the larger problems in less than 6 seconds.
The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of int...
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The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of intermittent PV power, optimum scheduling of over-production is necessary to minimize energy curtailment. Failure to include an accurate assessment of curtailed energy costs in the scheduling process increases wasted energy. Moreover, applying an objective function without considering the cost coefficients results in an inefficient concentration of curtailed power in a specific time interval. In this study, we provide an optimization method for scheduling the microgrid assets to evenly distribute curtailment over the entire daily period of PV generation. Each of the curtailment intervals established in our optimization model features the application of different cost coefficients. In the final step, curtailment costs are added to the objective function. The proposed cost minimization algorithm preferentially selects intervals with low curtailment costs to prevent the curtailment from being concentrated at a specific time. By inducing even distribution of curtailment, this novel optimization methodology has the potential to improve the cost-effectiveness of the PV-powered microgrid
This paper addresses the development of conflict graph-based algorithms and data structures into the COIN-OR Branch-and-Cut (CBC) solver, including: (i) an efficient infrastructure for the construction and manipulatio...
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This paper addresses the development of conflict graph-based algorithms and data structures into the COIN-OR Branch-and-Cut (CBC) solver, including: (i) an efficient infrastructure for the construction and manipulation of conflict graphs;(ii) a preprocessing routine based on a clique strengthening scheme that can both reduce the number of constraints and produce stronger formulations;(iii) a clique cut separator capable of obtaining dual bounds at the root node LP relaxation that are 19.65% stronger than those provided by the equivalent cut generator of a state-of-the-art commercial solver, 3.62 times better than those attained by the clique cut separator of the GLPK solver and 4.22 times stronger than the dual bounds obtained by the clique separation routine of the COIN-OR Cut Generation Library;and (iv) an odd-cycle cut separator with a new lifting module to produce valid odd-wheel inequalities. The average gap closed by this new version of CBC was up to four times better than its previous version. Moreover, the number of mixed-integer programs solved by CBC in a time limit of three hours was increased by 23.53%. (C) 2020 Elsevier Ltd. All rights reserved.
The distribution system operation and planning are facing a great challenge from the increasing penetration of electric vehicles, especially in case of large amount of aggregated simultaneously charging load at public...
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ISBN:
(纸本)9781728155081
The distribution system operation and planning are facing a great challenge from the increasing penetration of electric vehicles, especially in case of large amount of aggregated simultaneously charging load at public charging stations. This paper proposes a comprehensive planning method for allocating charging stations with a minimum impact on distribution system hosting capacity while satisfying public charging demand with reasonable travel distance and investment cost. A new concept of extra load hosting capacity (ELHC) is proposed to evaluate the maximum extra load that the system can absorb without operational violations. The EV charging demand is estimated using a transportation simulation tool with real-world data. The distribution system is modeled with a linear three-phase branch flow model that captures the multi-phase and unbalance of a distribution system. The planning problem is formulated as a mixed-integer linear programming (MILP) problem and is validated on the IEEE 123 node test feeder together with real-world Illinois transportation network data.
The finance-based scheduling problem(FBSP)is about scheduling project activities without exceeding a credit line financing *** FBSP is extended to consider different execution modes that result in the multi-mode FBSP(...
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The finance-based scheduling problem(FBSP)is about scheduling project activities without exceeding a credit line financing *** FBSP is extended to consider different execution modes that result in the multi-mode FBSP(MMFBSP).Unfortunately,researchers have abandoned the development of exact models to solve the FBSP and its ***,researchers have heavily relied on the use of heuristics and meta-heuristics,which do not guarantee solution *** exact models are available for contractors who look for optimal solutions to the multi-objective ***,which is an exact solver,has witnessed a significant decrease in its computation ***,its current version,CPLEX 12.9,solves multi-objective optimization *** study presents a mixed-integer linear programming model for the multi-objective *** CPLEX 12.9,we discuss several techniques that researchers can use to optimize a multi-objective *** test our model by solving several problems from the *** also show how to solve multi-objective optimization problems by using CPLEX 12.9 and how computation time increases as problem size *** small increase in computation time compared with possible cost savings make exact models a must for ***,the linearprogramming-relaxation of the model,which takes seconds,can provide an excellent lower bound.
This study presents a reinforcement learning (RL) approach for the mixed model sequencing (MMS) problem with a minimization of work overload situations. The proposed approach generates the sequence in a constructive w...
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This study presents a reinforcement learning (RL) approach for the mixed model sequencing (MMS) problem with a minimization of work overload situations. The proposed approach generates the sequence in a constructive way, so that an action denotes the model to be sequenced next. The trained policy quickly creates an initial sequence, which allows us to use the cutoff time to further improve the solution quality with a metaheuristic. Our numerical evaluation based on an existing benchmark dataset shows that our approach is superior to established methods if the demand plan follows its expected distribution from the learning process.
The present research aims to formulate competition in a retail energy market in the presence of an Integrated Demand Response (IDR) program to reduce prosumer costs and increase retailer profits. This gives prosumers ...
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The present research aims to formulate competition in a retail energy market in the presence of an Integrated Demand Response (IDR) program to reduce prosumer costs and increase retailer profits. This gives prosumers more degrees of freedom to reduce their energy costs. The retail energy market includes retailers and prosumers equipped with an energy hub containing a boiler for producing heat and combined heat and power (CHP). Retailers aim to maximize profit, whereas prosumers seek to minimize their costs. Hence, a multi-leader-follower game with a bi-level program emerges in which the upper level deals with the profit maximization of each retailer while the lower level considers the cost minimization of each prosumer. The strategic behaviour of each retailer is modelled as a Mathematical Program with Equilibrium Constraints (MPEC) problem. Simultaneously solving all MPECs, which leads to an Equilibrium Problem with Equilibrium Constraints (EPEC), determines the market equilibrium point. The equilibrium point is achieved using mathematical, analytical methods and linearization of nonlinear constraints by accurate techniques. Two different case studies are developed to investigate how the number of retailers influences the market equilibrium point. The first case includes two retailers, while the second case considers an increase in the number of retailers. The results demonstrate that with an increase in retailers' number, their competition increases, causing the prosumers costs to reduce. Furthermore, our results suggest the IDR impact on reduced prosumers cost and increased retailers profit. (c) 2021 Elsevier Ltd. All rights reserved.
We consider an uncapacitated location problem where p facilities have to be located in order to serve a given set of customers, and we assume that a customer requesting for a service has to reach a facility at his/her...
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We consider an uncapacitated location problem where p facilities have to be located in order to serve a given set of customers, and we assume that a customer requesting for a service has to reach a facility at his/her own cost. In this setting, a central issue is that of fairness among customers for the accessibility to the services provided. Every choice regarding the location of facilities corresponds to a distance distribution of customers to reach an open facility. Minimizing the average of this distribution would lead to a p-median problem, where system efficiency is optimized but the fair treatment of users is neglected. Minimizing the maximum (worst-case) of the distribution would lead to a p-center problem, where the unfair treatment of users is mitigated but system efficiency is neglected. To compromise between these two extremes, we minimize the conditional beta-mean, i.e., the average distance traveled by the 100 x beta% of customers farther from a facility. We call Fair Facility Location Problem (FFLP(beta)) the resulting optimization problem, which is formulated as a mixed-integerlinear Program (MIP) with a proven integer-friendly property. We propose a heuristic framework to produce a set of representative solutions to the FFLP(beta). The framework is based on Kernel Search, a heuristic scheme that has been shown to obtain high-quality solutions for a number of MIPs. Computational experiments are reported to validate the quality of the solutions found by the proposed solution algorithm, and to provide some general guidelines regarding the trade-off between average and worst-case optimization. Finally, we report on a case study stemming from the screening activities related to the pandemic triggered by the SARS-CoV-2 virus. The case study regards the optimal location of a number of drive-thru temporary testing sites for collecting swab specimens.
The emergence of novel demand-side management strategies is provoking that residential consumers cannot be longer categorized as inflexible loads. In that sense, new paradigms such as flexible demand and vehicle-to-ho...
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The emergence of novel demand-side management strategies is provoking that residential consumers cannot be longer categorized as inflexible loads. In that sense, new paradigms such as flexible demand and vehicle-to-home capabilities have appeared on pursuing a more efficient management of the different domestic assets such as smart appliances, small renewable generators and storage facilities. Consideration of such kind of management strategies plays a vital role on designing stages of electrifications systems for isolated homes. In this regard, a proper evaluation of possible demand-side capabilities enables a more accurate evaluation of the electrification project. This paper tackles this issue by developing a mixedintegerlinearprogramming formulation for optimal planning of electrification systems for off-grid dwellings. The developed framework allows to incorporate and analyse advanced demand-side strategies such as deferrable loads and vehicle-to-home processes by evaluating different project costs over various time horizons. The proposed approach is applied to a benchmark off-grid residential case study and various results are provided and analysed. As the most relevant result, it is worth remarking that total project cost can be reduced by -21.2% and -25.73% by considering flexible demand and vehicle-to-home capabilities, respectively. Nevertheless, the highest monetary savings were achieved when both strategies are jointly adopted, reducing the total project cost up to -41.5%. The reported results demonstrate the importance of considering the different demand-side strategies on planning stages of electrification systems for off-grid dwellings.
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