This paper is concerned with the Short-Term Hydrothermal Scheduling (STHS) of hydro-dominated power systems. The problem's formulation includes the representation of operational constraints such as the hydraulic c...
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This paper is concerned with the Short-Term Hydrothermal Scheduling (STHS) of hydro-dominated power systems. The problem's formulation includes the representation of operational constraints such as the hydraulic coupling between hydro plants in cascade and the transmission limits in the electric network. In order to allow the problem's decomposition into hydraulic and electric subproblems, a linear-quadratic penalty approach is applied to enforce the coupling between hydro and electric variables. As a result, the problem's natural networkflow structure is fully exploited through special-purposed network flow algorithms. The technique has been implemented in FORTRAN in a SUN SPARCstation IPX and tested in a 440 KV subsystem of the main interconnected Brazilian power system.
The problem we treat is defined on a graph where each node is associated with a variable and there are loss functions defined on the arcs, depending on the difference between the corresponding node variables. The obje...
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The problem we treat is defined on a graph where each node is associated with a variable and there are loss functions defined on the arcs, depending on the difference between the corresponding node variables. The objective is to compute values for the node variables so as to minimize the sum of losses. We exploit the relation between this problem and networkflows optimization and use it in developing an approximation algorithm for the problem A main application of the problem is the synchronization of fixed cycle traffic signals.
In many applications of multiple objective network programming (MONP) problems, only integer solutions are acceptable as the final optimal solution. Representative efficient solutions are usually obtained by sampling ...
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In many applications of multiple objective network programming (MONP) problems, only integer solutions are acceptable as the final optimal solution. Representative efficient solutions are usually obtained by sampling the efficient set through the solution of augmented weighted Tchebycheff network programs. Because such efficient solutions are usually not integer solutions, a branch-and-bound (BB) algorithm is developed to find integer efficient solutions. The purpose of the BB algorithm is to support interactive procedures by generating representative integer efficient solutions. To be computationally efficient, the algorithm takes advantage of the network structure as much as possible. An algorithm, used in the BB algorithm and performed on the key tree, is developed to construct feasible solutions from infeasible solutions and basic solutions from nonbasic solutions when bounds on branching variables change. The BB algorithm finds basic and nonbasic or supported and unsupported integer efficient solutions as long as they are optimal. Details of the algorithm are presented, an example is provided and computational results are reported. Computational results show that the BB algorithm performs well. Although the BB algorithm is developed for the purpose of generating integer efficient solutions for MONP problems, it can also solve more general integer networkflow problems with linear side constraints. (c) 2013 Wiley Periodicals, Inc.
For many practical multiple objective network programming (MONP) problems, only integer solutions are meaningful and acceptable. Representative efficient solutions are usually generated by solving augmented weighted T...
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For many practical multiple objective network programming (MONP) problems, only integer solutions are meaningful and acceptable. Representative efficient solutions are usually generated by solving augmented weighted Tchebycheff network programs (AWTNPs), sub-problems derived from MONP problems. However, efficient solutions generated this way are usually not integer valued. In this study, two algorithms are developed to construct integer efficient solutions starting from fractional efficient solutions. One algorithm finds a single integer efficient solution in the neighborhood of the fractional efficient solution. The other enumerates all integer efficient solutions in the same neighborhood. Theory supporting the proposed algorithms is developed. Two detailed examples are presented to demonstrate the algorithms. Computational results are reported. The best integer efficient solution is very close, if not equal, to the integer optimal solution. The CPU time taken to find integer efficient solutions is negligible, when compared with that taken to solve AWTNPs. (C) 2010 Wiley Periodicals, Inc. networkS, Vol. 57(4), 362-375 2011
This paper presents and solves the maximum throughput dynamic networkflow problem, an infinite horizon integer programming problem which involves networkflows evolving over time. The model is a finite network in whi...
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This paper presents and solves the maximum throughput dynamic networkflow problem, an infinite horizon integer programming problem which involves networkflows evolving over time. The model is a finite network in which the flow on each arc not only has an associated upper and lower bound but also an associated transit time. flow is to be sent through the network in each period so as to satisfy the upper and lower bounds and conservation of flow at each node from some fixed period on. The objective is to maximize the throughput, the net flow circulating in the network in a given period, and this throughput is shown to be the same in each period. We demonstrate that among those flows with maximum throughput there is a flow which repeats every period. Moreover, a duality result shows the maximum throughput equals the minimum capacity of an appropriately defined cut.A special case of the maximum dynamic networkflow problem is the problem of minimizing the number of vehicles to meet a fixed periodic schedule. Moreover, the elegantsolution derived by Ford and Fulkerson for the finite horizon maximum dynamic flow problem may be viewed as a special case of the infinite horizon maximum dynamic flow problem and the optimality of solutions which repeat every period.
Software-defined networking (SDN) increases the network programmability, promoting an effective development of networked systems of cloud scale. As the scale of the networks and systems is growing larger and larger wi...
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ISBN:
(纸本)9781479982189
Software-defined networking (SDN) increases the network programmability, promoting an effective development of networked systems of cloud scale. As the scale of the networks and systems is growing larger and larger with time, programmability of the systems and networks is researched intensively. Many emulators are proposed and implemented to emulate large and complex networks inside a single computer, or a cluster of computers in the research lab. However, the emulators lack the ability to represent large systems such as data center networks or content delivery networks. Many of the networkalgorithms and design choices can also be tested for their functionality and efficiency in a simulator environment. While network emulators and simulators exist, a generic networkflow simulator that is easy to program a variety of highly distributed and gigantic systems is still lacking. This paper presents xSDN, an expressive simulator for dynamic networkflows. Adhering to the principles of software-defined networking paradigm from the design, xSDN focuses to be lean, light-weight, easy to learn and configure, and efficient, that can simulate networks of a scale of million nodes within a few seconds.
Electric vehicle (EV) adoption in long-distance logistics faces challenges such as range anxiety and uneven distribution of charging stations. Two pivotal questions emerge: How can EVs be efficiently routed in a charg...
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ISBN:
(纸本)9783031598340;9783031598357
Electric vehicle (EV) adoption in long-distance logistics faces challenges such as range anxiety and uneven distribution of charging stations. Two pivotal questions emerge: How can EVs be efficiently routed in a charging network considering range limits, charging speeds and prices? And, can the existing charging infrastructure sustain the increasing demand for EVs in long-distance logistics? This paper addresses these questions by introducing a novel theoretical and computational framework to study the EV networkflow problems. We present an EV networkflow model that incorporates range constraints and nonlinear charging rates, and identify conditions under which polynomial-time solutions can be obtained for optimal single EV routing, maximum flow, and minimum-cost flow problems. Our findings provide insights for optimizing EV routing in logistics, ensuring an efficient and sustainable future.
The innovation processes are connected with uncertainty and considerable financial risk. No matter whether a new product, technology or methodology is promoted, there are several stages that the innovation passes. The...
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ISBN:
(纸本)9781467363198
The innovation processes are connected with uncertainty and considerable financial risk. No matter whether a new product, technology or methodology is promoted, there are several stages that the innovation passes. The first three are connected with venture investments and the final - with eventual good profits. It is possible that the outcome is a failure, which is normal for any venture enterprise. Convenient tools for modeling the introduction of an innovation are the networkflow models which turn to be fairly adequate and to provide reliable data to the decision maker. It is to be noted that the process is unidirectional, i.e., it cannot go to back stages, the underlying graph is a directed acyclic one and this structure is of increasing interest in the research area. The work is illustrated by a numerical example which demonstrates the relevancy of the networkflow model.
This paper presents the use of a modified collective behavior strategy of ant colonies to find approximate sets in the multi-objective optimization problem. The currently used methods search for non-dominated solution...
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ISBN:
(纸本)9781450357401
This paper presents the use of a modified collective behavior strategy of ant colonies to find approximate sets in the multi-objective optimization problem. The currently used methods search for non-dominated solutions, which takes place directly on the basis of definitions in the previously generated finite set of admissible ratings, searching in the space of goals by analyzing active constraints, solving optimization tasks in terms of all subsequent individual optimization criteria and adopting optimization criteria in order to form a substitute criterion of optimization in the form of a combination of linear criteria with appropriately selected weighting factors. However, these methods are ineffective in many cases. Therefore, the authors of the article propose a new approach based on the use of rough sets flow graphs to control the strategy of communicating artificial ants in distributed cognitive environments. The use of this approach allows to maximize the number of generated solutions and finding non-dominated solutions for the multiple objectives.
We develop efficient algorithms to solve convex cost flow problems where the underlying graph is a circle, a line, or a tree. Each node i has an associated supply/demand b( i). The cost of sending flow on arc ( i, j) ...
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We develop efficient algorithms to solve convex cost flow problems where the underlying graph is a circle, a line, or a tree. Each node i has an associated supply/demand b( i). The cost of sending flow on arc ( i, j) is a piecewise linear convex function f(ij) defined over . Let n be the number of nodes and m = O(n) be the total number of pieces of all the convex functions. A flow x is feasible if the imbalances on all nodes are nonnegative. Excess ei(x) stored on node i has an associated linear cost cixei(x). We show that the problem on a circle can be transformed into an equivalent problem on a line in O( n) time. Thereafter, we develop an algorithm that solves the problem on a line in O(sort(n)+n(n)) time, where sort( n) is the time to sort n real numbers and (n) is the inverse Ackermann function. We also prove that when the nodes lie on a tree, the problem can be solved in O(nlog?n) time using the dynamic tree data structure. We describe applications in areas such as distributed computing, lot-sizing, computational biology, computational music, and transportation. (c) 2013 Wiley Periodicals, Inc. networkS, Vol. 62(4), 288-296 2013
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