Future traffic that will be accompanied by higher alternative drive concepts will pose as a challenge when it comes to corresponding energy systems. Whereas today's conventional energy supply infrastructure (in fo...
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Future traffic that will be accompanied by higher alternative drive concepts will pose as a challenge when it comes to corresponding energy systems. Whereas todays conventional energy supply infrastructure (in form of...
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Future traffic that will be accompanied by higher alternative drive concepts will pose as a challenge when it comes to corresponding energy systems. Whereas todays conventional energy supply infrastructure (in form of gas stations) has not been optimized for the integration of charging processes into vehicle operation, the inevitable development of alternative energy supply systems and infrastructures will pose as a chance to reevaluate and optimize them accordingly This paper will show that with currently available charging powers charging processes can be integrated into urban traffic operations without the need for explicit charging halts or detours (e. g. at traffic lights) and present a method for the analysis of microscopic traffic scenarios and the optimal placement of corresponding charging stations. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
We consider a network of agents whose objective is for the aggregate of their states to converge to a solution of a linear program in standard form. Each agent has limited information about the problem data and can co...
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We consider a network of agents whose objective is for the aggregate of their states to converge to a solution of a linear program in standard form. Each agent has limited information about the problem data and can communicate with other agents at discrete time instants of their choosing. Our main contribution is the synthesis of a distributed dynamics and a set of state-based rules, termed triggers, that individual agents use to determine when to opportunistically broadcast their state to neighboring agents to ensure asymptotic convergence to a solution of the linear program. Our technical approach to the algorithm design and analysis overcomes a number of challenges, including establishing convergence in the absence of a common smooth Lyapunov function, ensuring that the triggers are detectable by agents using only local information, accounting for asynchronism in the state broadcasts, and ruling out various causes of arbitrarily fast state broadcasting. Various simulations illustrate our results.
The paper provides a comparison between different control allocation techniques in over-actuated Autonomous Underwater Vehicles. The pseudoinverse, linear programming (LP), Quadratic programming (QP), Mixed Integer Li...
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The paper provides a comparison between different control allocation techniques in over-actuated Autonomous Underwater Vehicles. The pseudoinverse, linear programming (LP), Quadratic programming (QP), Mixed Integer linear programming (MILP) and Mixed Integer Quadratic programming (MIQP) are evaluated in simulation on the V-Fides vehicle model. The MILP and MIQP techniques allow to include in their implementations a more detailed characterization of the non-linear static behaviour of the actuators. This customizability can be also exploited to improve the practical stability of the system. The metrics used for comparison include the maximum attainable forces and torques, the integral of the error allocation and the required thrusters effort. Our simulation results show that, in particular with respect to thrusters effort, MILP and MIQP are the preferred allocation methods. The computational complexity associated to both methods is not such to compromise their implementation in operating vehicles;in particular, the MILP version is currently implemented in the V-Fides vehicle. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
The relevance of planning non-hierarchical supply chains has increased due to growing collaboration among industrial and logistic organizations once this planning approach aims to optimize the supply chain while prese...
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The relevance of planning non-hierarchical supply chains has increased due to growing collaboration among industrial and logistic organizations once this planning approach aims to optimize the supply chain while preserving each actor's individuality. linear programming is the predominant modelling approach to deal with non-hierarchical supply chains according to the state-of-the-art literature. Metaheuristics and exact methods are the classical solving methods for linear programming problems, with different characteristics in terms of solution quality and capability of handling complex problems in feasible computation time. In this context, this paper evaluates methods to solve linear programming problems considering their capability of dealing with most common decision model types associated with spare parts supply chains applying collaborative planning concepts. The gathered references substantiate the conclusion that, for normal sized problems, the simplex method continues to be the most attractive method. For bigger problems, interior point methods can be a better alternative. And for problems that surpass interior point method capacity, metaheuristics are recommended (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
It is well known that linear programming is P-complete, with a logspace reduction. In this work we ask whether linear programming remains P-complete, even if the polyhedron (i.e., the set of linear inequality constrai...
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ISBN:
(纸本)9781450340571
It is well known that linear programming is P-complete, with a logspace reduction. In this work we ask whether linear programming remains P-complete, even if the polyhedron (i.e., the set of linear inequality constraints) is a fixed polyhedron, for each input size, and only the objective function is given as *** formally, we consider the following problem: maximize cx, subject to Ax <= b;x is an element of R-d, where A,b are fixed in advance and only c is given as an input. We start by showing that the problem remains P-complete with a logspace reduction, thus showing that n(o(1))-space algorithms are unlikely. This result is proved by a direct classical reduction. We then turn to study approximation algorithms and ask what is the best approximation factor that could be obtained by a small space algorithm. Since approximation factors are mostly meaningful when the objective function is non-negative, we restrict ourselves to the case where x >= 0 and c >= *** show that (even in this possibly easier case) approximating the value of max c.x (within any polynomial factor) is P-complete with a polylog space reduction, thus showing that 2((log n)o(1)) -space approximation algorithms are unlikely. The last result is proved using a recent work of Kalai, Raz, and Rothblum, showing that every language in P has a no-signaling multi-prover interactive proof with poly-logarithmic communication complexity. To the best of our knowledge, our result gives the first space hardness of approximation result proved by a PCP-based argument
The energy consumption in heterogeneous computing systems (HCS) has attracted a great deal of attention in both scientific and commercial fields due to operating and environmental concerns. Based on the technique of d...
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ISBN:
(纸本)9781509052523
The energy consumption in heterogeneous computing systems (HCS) has attracted a great deal of attention in both scientific and commercial fields due to operating and environmental concerns. Based on the technique of dynamic voltage and frequency scaling (DVFS), many studies have investigated and developed efficient task scheduling algorithms for energy reduction. However, most of them provide only one refined frequency for each task to perform slack reclamation. Moreover, the total energy-saving is accumulated by individual local minimum of energy consumption with less or no global consideration. In this paper, we use a linear combination of processor frequencies to execute each task and allocate time slices for these frequencies by a linear programming approach. The goal of energy reduction is represented by a global function associated with the set of time slices while the constraint declarations are given by runtime precedence-constraints and processor-constraints, respectively. In this case, the problem of energy-efficient task scheduling becomes a linear program which can be solved by a mature set of linear programming solvers. The experimental results show the effectiveness of our proposed method and demonstrate the superior performance over existing approaches without sacrificing the schedule length.
This paper proposes a metaheuristic approach called as Multi Objective Water Cycle Algorithm (MOWCA) for Fuzzy Multi Objective Non-linear programming Problem (FMONLPP). The suggested approach initially defines a fuzzy...
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ISBN:
(纸本)9781538628430;9781538628423
This paper proposes a metaheuristic approach called as Multi Objective Water Cycle Algorithm (MOWCA) for Fuzzy Multi Objective Non-linear programming Problem (FMONLPP). The suggested approach initially defines a fuzzy goal for each objective function with hyperbolic membership function. Further, the converted Fuzzy Multi-Objective linear Fractional programming Problem (FMOLFPP) is changed into Fuzzy Multi Objective Non-linear programming Problem (MONLPP) and to find pareto solutions by the proposed metaheuristic approach. Generally, the efficiency of the metaheuristic algorithm is evaluated through various performance measures. In the present study, the following performance measures Generational Distance (GD), Reversed Generational Distance (RGD), Maximum spread (MS), Metric of spacing (S) and Delta Metric (Δ) are used to show the efficiency of the proposed algorithm. Finally, the comparative study is carried out with other existing metaheuristic approaches.
Motivated by the fact that the l(1)-penalty is piecewise linear, we proposed a ramp loss linear programming nonparallel support vector machine (ramp-LPNPSVM.), in which the l(1)-penalty is applied for the RNPSVM, for ...
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Motivated by the fact that the l(1)-penalty is piecewise linear, we proposed a ramp loss linear programming nonparallel support vector machine (ramp-LPNPSVM.), in which the l(1)-penalty is applied for the RNPSVM, for binary classification. Since the ramp loss has the piecewise linearity as well, ramp-LPNPSVM. is a piecewise linear minimization problem and a local minimum can be effectively found by the Concave Convex Procedure and experimental results on benchmark datasets confirm the effectiveness of the proposed algorithm. Moreover, the l(1)-penalty can enhance the sparsity.
Sherali-Adams [25] and Lovasz-Schrijver [21] developed systematic procedures to strengthen a relaxation known as lift-and-project methods. They have been proven to be a strong tool for developing approximation algorit...
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ISBN:
(数字)9783319334615
ISBN:
(纸本)9783319334615;9783319334608
Sherali-Adams [25] and Lovasz-Schrijver [21] developed systematic procedures to strengthen a relaxation known as lift-and-project methods. They have been proven to be a strong tool for developing approximation algorithms, matching the best relaxations known for problems like Max-Cut and Sparsest-Cut. In this work we provide lower bounds for these hierarchies when applied over the configuration LP for the problem of scheduling identical machines to minimize the makespan. First we show that the configuration LP has an integrality gap of at least 1024/1023 by providing a family of instances with 15 different job sizes. Then we show that for any integer n there is an instance with n jobs in this family such that after Omega(n) rounds of the Sherali-Adams (SA) or the Lovasz-Schrijver (LS+) hierarchy the integrality gap remains at least 1024/1023.
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