In this paper we introduce the circuit diameter of polyhedra, which is always bounded from above by the combinatorial diameter. We consider dual transportation polyhedra defined on general bipartite graphs. For comple...
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In this paper we introduce the circuit diameter of polyhedra, which is always bounded from above by the combinatorial diameter. We consider dual transportation polyhedra defined on general bipartite graphs. For complete MxN bipartite graphs the Hirsch bound (M-1)(N-1) on the combinatorial diameter is a known tight bound [Math. Oper. Res., 9 (1984), pp. 629-633]. For the circuit diameter we show the much stronger bound M+N-2 for all dual transportation polyhedra defined on arbitrary bipartite graphs with M+N nodes.
In this paper, we consider the joint replenishment and delivery scheduling of the one-warehouse, n-retailer system with stochastic demand. In this distribution system, items are first jointly replenished and therefore...
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In this paper, we consider the joint replenishment and delivery scheduling of the one-warehouse, n-retailer system with stochastic demand. In this distribution system, items are first jointly replenished and therefore can share a major ordering cost. Then a coordinated delivery scheduling is adopted to arrange the delivery plan. The problem is formulated as a mixed integer nonlinear program. Subsequently, we introduce two algorithms to handle this complex problem: a Lipschitz optimization method and a modified RAND heuristic. Numerical experiments show that the proposed methods are quite efficient. Sensitivity analysis also shows some interesting findings in terms of management.
Retiming is a technique for optimizing sequential circuits. In this paper, wediscuss this problem and propose an improved retiming algorithm based on variables *** the computation of the lower and upper bounds on vari...
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Retiming is a technique for optimizing sequential circuits. In this paper, wediscuss this problem and propose an improved retiming algorithm based on variables *** the computation of the lower and upper bounds on variables, the algorithm can signi-ficantly reduce the number of constraints and speed up the execution of retiming. Furthermore,the elements of matrixes D and W are computed in a demand-driven way, which can reducethe capacity of memory. It is shown through the experimental results on ISCAS89 benchmarksthat our algorithm is very effective for large-scale sequential circuits.
This note considers an average-cost Markov Decision Process (MDP) with finite state and action sets and satisfying the additional condition that there is a state to which the system jumps from any state and under any ...
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This note considers an average-cost Markov Decision Process (MDP) with finite state and action sets and satisfying the additional condition that there is a state to which the system jumps from any state and under any action with a positive probability. The main result is that the policy iteration algorithm is strongly polynomial for such MDPs, which are often used to model replacement and maintenance problems. (C) 2013 Elsevier B.V. All rights reserved.
This letter considers radio frequency (RF) energy harvesting and targets monitoring sensor nodes that are managed by a hybrid access point (HAP). We address a novel problem called maximizing data collected for complet...
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This letter considers radio frequency (RF) energy harvesting and targets monitoring sensor nodes that are managed by a hybrid access point (HAP). We address a novel problem called maximizing data collected for complete targets' coverage (MDC-CTC) that involves computing a schedule comprising the time used by the HAP for charging, and the time used by the sensor nodes for monitoring targets and data upload. We model the MDC-CTC problem as a linear program (LP) and use it to determine the optimal schedule that maximizes the amount of data collected by the HAP. In addition, we also propose a heuristic to generate a subset of set covers for use by the LP in large-scale networks. Our results show that the amount of data collected by the HAP is proportional to the number of sensor nodes or the HAP's transmission power. Finally, using our heuristic along with the formulated LP yields results that are within 75% of the optimal result.
This note provides a condition under which e 1 minimization (also known as basis pursuit) can recover short linear combinations of complex vectors chosen from fixed, overcomplete collection. This condition has already...
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This note provides a condition under which e 1 minimization (also known as basis pursuit) can recover short linear combinations of complex vectors chosen from fixed, overcomplete collection. This condition has already been established in the real setting by Fuchs, who used convex analysis. The proof given here is more direct.
The efficiency of interior-point algorithms for linear programming is related to the effort required to factorize the matrix used to solve for the search direction at each iteration. When the linear program is in symm...
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The efficiency of interior-point algorithms for linear programming is related to the effort required to factorize the matrix used to solve for the search direction at each iteration. When the linear program is in symmetric form (i.e., the constraints are Ax less-than-or-equal-to b, x greater-than-or-equal-to 0), then there are two mathematically equivalent forms of the search direction, involving different matrices. One form necessitates factoring a matrix whose sparsity pattern has the same form as that of (AA(T)). The other form necessitates factoring a matrix whose sparsity pattern has the same form as that of (A(T)A). Depending on the structure of the matrix A, one of these two forms may produce significantly less fill-in than the other. Furthermore, by analyzing the fill-in of both forms prior to starting the iterative phase of the algorithm, the form with the least fill-in can be computed and used throughout the algorithm. Finally, this methodology can be applied to linear programs that are not in symmetric form, that contain both equality and inequality constraints.
Recent developments and advances in energy storage technologies are making the application of energy storage technologies a viable solution to power applications. The energy storage system can store energy previously,...
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Recent developments and advances in energy storage technologies are making the application of energy storage technologies a viable solution to power applications. The energy storage system can store energy previously, and then release it in the proper time. Due to their flexibility, it is suitable to apply this technology to deregulated power markets. Therefore, this paper will build the economic analysis model for the energy storage system to apply to a distribution substation in a deregulated power market. The costs including installing energy storage system and operation and maintenance expense, and the revenues containing energy price arbitrage, reducing transmission access cost, and deferring facility investment are considered in this model. All these factors are evaluated by present worth value. Due to complexity of this problem, this paper proposes a method combining the genetic algorithm with linear program (GALP) to determine the optimal capacity and operations of the energy storage system. (C) 2011 Elsevier Ltd. All rights reserved.
A growing integration of electric vehicles (EV) and fast charging stations (FCS) has paved the way for the emergence of transportation and power distribution networks. However, previous work on the coordination of EV ...
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A growing integration of electric vehicles (EV) and fast charging stations (FCS) has paved the way for the emergence of transportation and power distribution networks. However, previous work on the coordination of EV in the transportation system mainly focuses on static traffic assignment (STA) models which are unable to characterize the temporal evolution of EV flows on traffic links. Meanwhile, limited prior effort in dynamic traffic assignment (DTA) employs a static FCS model which is unable to describe the temporal evolution of charging and discharging (C&D) behavior of EV flows, while respecting the state-of-charge (SoC) related operating constraints, hindering full exploitation of EV flexibility potentials. To bridge the knowledge gap, this paper proposes a novel DTA model to optimize the spatial-temporal distribution of EV flows on roads and at FCSs, taking into account their flexible C&D options and SoC-related constraints, through the solution of only a single linear program. Case studies validate the effectiveness of the proposed DTA model by benchmarking its performance against traditional STA models, and corroborate the core benefits from the proposed spatial-temporal coordination of EV C&D demand in the power distribution network in terms of peak demand reduction, improved RES absorption, reduced carbon emission as well as more efficient network congestion management.
Understanding what cannot be seen is difficult. Physical behavior can be explained on the basis of physical theories even if the behavior cannot be observed. Explanation of what is physically happening in the real wor...
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Understanding what cannot be seen is difficult. Physical behavior can be explained on the basis of physical theories even if the behavior cannot be observed. Explanation of what is physically happening in the real world would become easy, however, if annotations were superimposed on the real objects. Herein, the authors demonstrate how an understanding of a physical event can be facilitated by overlapping a real-world situation with a simulation that predicts a future state. This idea is demonstrated in a game application in which a player stacks blocks into a pile until it collapses. In general, it is easy to estimate whether a block on the edge of a table will fall or not. However, it is more difficult to predict whether a stack of many blocks will collapse, and in what manner the stack will collapse. Even though previous research has demonstrated that the problem of how two-dimensionally stacked blocks collapse can be reduced to solving a sequence of convex quadratic programs, algorithms for convex quadratic programs require massive computational resources. Hence, the authors developed a fast and new algorithm based on a linear program. The proposed algorithm realizes real-time simulation based on physics that superimposes predicted collapse. The block that is predicted to fall is superimposed on the real block with a lit background projection. The system was evaluated in an experiment, and superimposed augmented reality annotation was observed to be efficient. The system was also demonstrated in game contests and received positive feedback and comments.
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