This project examined the potential impact of process savings achieved through strategically sourced contracts on the Air Force Installation Contracting Agency's (AFICA) manpower. The project leveraged AFICA's...
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This project examined the potential impact of process savings achieved through strategically sourced contracts on the Air Force Installation Contracting Agency's (AFICA) manpower. The project leveraged AFICA's contract man-hour data to make logical inferences concerning the transaction costs of various strategically sourced contracts. The information derived from the transaction cost analysis was used to construct a theoretical linear program (LP) to optimize manpower with respect to man- hour savings achieved through strategically sourced contracts. The optimized manpower solution provides a theoretical framework to identify manpower savings that may be used to address AFICA mission objectives.
A physically concise polynomial-time iterative-cum-non-iterative algorithm is presented to solve the linear program (LP) Min c(t)chi subject to A chi = b, chi >= 0. The iterative part - a variation of Karmarkar pro...
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A physically concise polynomial-time iterative-cum-non-iterative algorithm is presented to solve the linear program (LP) Min c(t)chi subject to A chi = b, chi >= 0. The iterative part - a variation of Karmarkar projective transformation algorithm - is essentially due to Barnes only to the extent of detection of basic variables of the LP taking advantage of monotonic convergence. It involves much less number of iterations than those in the Karmarkar projective transformation algorithm. The shrunk linear system containing only the basic variables of the solution vector x resulting from A chi = b is then solved in the mathematically non-iterative part. The solution is then tested for optimality and is usually more accurate because of reduced computation and has less computational and storage complexity due to smaller order of the system. The computational complexity of the combination of these two parts of the algorithm is polynomial-time O(n(3)). The boundedness of the solution, multiple solutions, and no-solution (inconsistency) cases are discussed. The effect of degeneracy of the primal linear program and/or its dual on the uniqueness of the optimal solution is mentioned. The algorithm including optimality test is implemented in Matlab which is found to be useful for solving many real-world problems. (C) 2010 Elsevier Ltd. All rights reserved.
Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion b...
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Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster-based wireless sensor networks. We formulate the network design problem as mixed-integer linear programming. Our contribution is 3-fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy-aware routing model for optimal inter-cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre-deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.
Rural water networks in the developing world are typically branched networks with a single water source. The main design decision to be made for such networks is the choice of pipe diameters from a discrete set of com...
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Rural water networks in the developing world are typically branched networks with a single water source. The main design decision to be made for such networks is the choice of pipe diameters from a discrete set of commercially available pipe diameters. Larger the pipe diameters, better the service (pressure), but higher is the capital cost. In general, each link (connection between two nodes) in the network can consist of several pipe segments of differing diameters. For such networks, existing design tools solve the constrained-optimization problem heuristically [1] [2] . In [3] , an ILP formulation is proposed for the special case of one pipe diameter per link. This means that currently one can either get an optimal solution for the special case of one piped segment per link or get a non-optimal solution for the general case of multiple pipe segments per link. In this work, we come up with a model that solves the general formulation while still maintaining optimality. Our model has an LP formulation. It not only manages to optimally solve the general case, it also has a runtime performance that is better than both the heuristic approach to the general problem as well as the optimal approach to the specific problem (one pipe per link). To aid the designers of piped water networks, we have developed an optimization system called JalTantra that implements this model. It also has GIS functionality integrated for ease of providing network details. It is publicly available at http://***/jaltantra/.
Both the combinatorial and the circuit diameter of polyhedra are of interest to the theory of linear programming for their intimate connection to a best-case performance of linear programming algorithms. We study the ...
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Both the combinatorial and the circuit diameter of polyhedra are of interest to the theory of linear programming for their intimate connection to a best-case performance of linear programming algorithms. We study the diameters of dual network flow polyhedra associated to b-flows on directed graphs and prove quadratic upper bounds for both of them: the minimum of and for the combinatorial diameter, and for the circuit diameter. Previously, bounds on these diameters have only been known for bipartite graphs. The situation is much more involved for general graphs. In particular, we construct a family of dual network flow polyhedra with members that violate the circuit diameter bound for bipartite graphs by an arbitrary additive constant.
This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power con...
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This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power consumption when each user is served by an optimized subset of the base stations (BSs), using non-coherent joint transmission. We first derive a lower bound on the ergodic spectral efficiency (SE), which is applicable for any channel distribution and precoding scheme. Closed-form expressions are obtained for Rayleigh fading channels with either maximum ratio transmission (MRT) or zero forcing (ZF) precoding. From these bounds, we further formulate the DL power minimization problems with fixed SE constraints for the users. These problems are proved to be solvable as linear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulate a max-min fairness problem that maximizes the worst SE among the users, and we show that it can be solved as a quasi-linear program. Simulations manifest that the proposed methods provide good SE for the users using less transmit power than in small-scale systems and the optimal user association can effectively balance the load between BSs when needed. Even though our framework allows the joint transmission from multiple BSs, there is an overwhelming probability that only one BS is associated with each user at the optimal solution.
This letter considers a two-tiered multi-hop RF-harvesting network comprising of wireless routers and so called eh-nodes that harvest energy from RF emitted by the said routers. Our aim is to derive the shortest possi...
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This letter considers a two-tiered multi-hop RF-harvesting network comprising of wireless routers and so called eh-nodes that harvest energy from RF emitted by the said routers. Our aim is to derive the shortest possible superframe or time division multiple access schedule for use by routers, which are responsible for meeting flow and energy demands. We present a linear program to derive the optimal schedule whilst satisfying the said demands. We also outline a heuristic algorithm called Algo-TS to generate transmission sets. Our results show Algo-TS produces superframes that are at most 2% longer than the optimal solution in all tested scenarios.
In this paper, a novel algorithm is presented for the design of sparse linear-phase FIR filters. Compared to traditional l(1)-optimization-based methods, the proposed algorithm minimizes l(1) norm of a portion (instea...
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ISBN:
(纸本)9780992862657
In this paper, a novel algorithm is presented for the design of sparse linear-phase FIR filters. Compared to traditional l(1)-optimization-based methods, the proposed algorithm minimizes l(1) norm of a portion (instead of all) of nonzero coefficients. In this way, some nonzero coefficients at crucial positions are not affected by l(1) norm utilized in the objective function. The proposed algorithm employs an iterative procedure and the index set of these crucial coefficients is updated in each iteration. Simulation results demonstrate that the proposed algorithm can achieve better design results than both greedy methods and traditional l(1)-optimization-based methods.
One of the critical issues in a Wireless Sensor Network (WSN) is the design of a proper routing protocol. While the requirement of low latency and low-energy consumption is getting more and more importance in emerging...
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ISBN:
(纸本)9781509003044
One of the critical issues in a Wireless Sensor Network (WSN) is the design of a proper routing protocol. While the requirement of low latency and low-energy consumption is getting more and more importance in emerging applications, the WSN should be capable of fulfilling its mission in a timely manner and without loss of energy. In this paper, we focus on multi-hop flat routing. A mathematical model for Delay and Energy Aware Routing (DEAR) is proposed. Our model aims to build a trade-off between energy consumption and delay. The goal is to find out a route from all sensor nodes to the base station, which has a comparably lower overall distance, with fewer data forwards. We define a multi-objective function for simultaneously minimizing the distance and minimizing the delay in forwarding. To demonstrate the effectiveness of DEAR, we compare it with the LeeMoon (Lee and Moon Model), the MHRM (Minimum Hop Routing Model), and the MTEM (Minimum Transmission Energy Model). the numerical results show that our model outperforms those models in terms of latency and energy consumption.
In the parallel k-stage flow-shops problem, we are given m identical k-stage flow-shops and a set of jobs. Each job can be processed by any one of the flow-shops but switching between flow-shops is not allowed. The ob...
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ISBN:
(纸本)9783319398174;9783319398167
In the parallel k-stage flow-shops problem, we are given m identical k-stage flow-shops and a set of jobs. Each job can be processed by any one of the flow-shops but switching between flow-shops is not allowed. The objective is to minimize the makespan, which is the finishing time of the last job. This problem generalizes the classical parallel identical machine scheduling (where k = 1) and the classical flow-shop scheduling (where m = 1) problems, and thus it is NP-hard. We present a polynomial-time approximation scheme for the problem, when m and k are fixed constants. The key technique is to enumerate over schedules for big jobs, solve a linear programming for small jobs, and add the fractional small jobs at the end. Such a technique has been used in the design of similar approximation schemes.
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