Nowadays, the management of natural gas networks primarily relies on the expertise gained by operators over the years. Nevertheless, the need to reduce energy consumption and the progressive installation of electric c...
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Nowadays, the management of natural gas networks primarily relies on the expertise gained by operators over the years. Nevertheless, the need to reduce energy consumption and the progressive installation of electric compressors call for the adoption of systematic optimization tools. This study proposes a Mixed Integer Linear Programming (MILP) model for optimizing the operation of complex real-world gas networks to minimize the environmental impact of the compression work in presence of both gas-turbine driven and electric compressors. The operational problem includes the gas transport dynamic equations, detailed modeling of compressor stations and control valves, while handling complex branch and looped networks with possible reverse flow. To address large-scale problems, a graph reduction procedure and a novel bilevel decomposition algorithm are developed. This methodology, validated with real data, enables the optimization of the nationwide Italian network, comprising 51 compressors and 9727 km of pipes.
In this paper, we study a distributionally robust optimization approach to chance-constrained stochastic programs to hedge against uncertainty in the distributions of the random parameters. We consider a general polyh...
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In this paper, we study a distributionally robust optimization approach to chance-constrained stochastic programs to hedge against uncertainty in the distributions of the random parameters. We consider a general polyhedral ambiguity set under finite support. We develop a decomposition-based solution approach to solve the model and use mixing inequalities to develop custom feasibility cuts. In addition, probability cuts are also developed to handle the distributionally robust chance constraint. Finally, we present a numerical study to illustrate the effectiveness of the proposed decomposition-based algorithm and showcase the results for Wasserstein ambiguity set, total variation distance ambiguity set, and moment-based ambiguity set as special cases of the polyhedral ambiguity set.
We study the static load balancing problem in a distributed computer system with the tree hierarchy configuration. It is formulated as a nonlinear optimization problem. After studying the conditions that the solution ...
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We study the static load balancing problem in a distributed computer system with the tree hierarchy configuration. It is formulated as a nonlinear optimization problem. After studying the conditions that the solution to the optimization problem of the tree hierarchy network satisfies, we demonstrate that the special structure of the optimization problem leads to an interesting decomposition technique. A new effective decomposition algorithm to solve the optimization problem is presented. The proposed algorithm is compared with two other well known algorithms: the Flow Deviation (FD) algorithm and the Dafermos-Sparrow (D-S) algorithm. It is shown that the amounts of the storage required for the proposed algorithm and the FD algorithm are O(n) for load balancing of an n-node system. However, the amount of the storage required for the D-S algorithm is O(n log(n)). By using numerical experiments, we show that both the proposed algorithm and the D-S algorithm have much faster convergence in terms of central processing unit (CPU) time than the FD algorithm.
At this stage, brain health can be directly expressed in the human hand posture estimation. Therefore, model estimation of healthy human hand posture can also be used as a criterion for brain health. The recognition a...
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At this stage, brain health can be directly expressed in the human hand posture estimation. Therefore, model estimation of healthy human hand posture can also be used as a criterion for brain health. The recognition algorithm of healthy human hand gesture based on global feature extraction of depth map sequence is not enough to analyze the motion correlation of healthy human hand posture, which leads to the need to improve the accuracy of human body hand gesture description and the change of movement speed of robustness. After analyzing the characteristics of healthy human hand movement in detail, this paper proposes a hand posture decomposition algorithm based on depth map sequence. The goal is to find information that plays a key role in hand gesture recognition in the depth map sequence. The algorithm can remove redundant information and improve the robustness of the recognition algorithm.
This paper proposes a realistic queueing model of automated guided vehicle (acv) systems in just-in-time production systems. The model takes into consideration return paths, Erlang distributed service times, and pull-...
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This paper proposes a realistic queueing model of automated guided vehicle (acv) systems in just-in-time production systems. The model takes into consideration return paths, Erlang distributed service times, and pull-type dispatching rule, assuming finite buffer capacities. Since it has no product-form solution and natural decomposability due to complex nontree fork-cum-join architecture and dynamic dispatching rules, we propose a machine-based decomposition algorithm for the performance evaluation of the model. Each decomposed module consists of the processing machine and its dispatching station. Three how probabilities, derived from flow conservation analysis, relate the modules, which are updated iteratively until the parameters converge. The numerical results from a real-life AGV system application show that the algorithm is reasonably accurate.
Low-power design of VLSI circuits has been identified as a critical technological need in recent years due to the high demand for portable consumer electronics products or even in line operated devices in order to avo...
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ISBN:
(纸本)9788184244403
Low-power design of VLSI circuits has been identified as a critical technological need in recent years due to the high demand for portable consumer electronics products or even in line operated devices in order to avoid larger heat sinks. Adders and multipliers are the most important arithmetic units in a general microprocessor and the major source of power dissipation. Various architecture styles exist to implement these units, each having their own merits and demerits. The general objective of our work is to investigate the area and power-delay performances of 8 bit and 16 bit multiplier combining both Wallace tree[7] and decomposition[I] algorithm. This decomposition logic improves speed and reduces power consumption by reducing the spurious transitions on internal nodes. We have analyzed area and power-delay performances using different type of adder structures using different logic families CMOS[5], HYBRID[3] AND CPL[5]. With the help of these designs, it would be possible to design highly power efficient processor with less area, especially digital signal Processors.
A generalized decomposition technique is presented for determining optimal resource usage in segregated targeting problems with single quality index (e.g., concentration, temperature, etc.) through pinch analysis. The...
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A generalized decomposition technique is presented for determining optimal resource usage in segregated targeting problems with single quality index (e.g., concentration, temperature, etc.) through pinch analysis. The latter problems are concerned with determining minimal resource requirements of process networks characterized by the existence of multiple zones, each consisting of a set of demands and using a unique external resource. However, all the zones share a common set of internal sources. The decomposition algorithm allows the problem to he decomposed into a sequence of subproblems, each of which can in turn be solved using any established graphical or algebraic targeting methodology to determine the minimum requirement of respective resource. This article presents a rigorous mathematical proof of the decomposition algorithm, and then demonstrates its potential applications with case studies on carbon-constrained energy sector planning, interplant water integration, and emergy-based multisector fuel allocation. (C) 2009 American Institute of Chemical Engineers AIChE J, 56: 1235-1248, 2010
In this paper we propose an MILP model to address the optimization of the tactical planning for the Fast Moving Consumer Goods (FMCG) industry. This model is demonstrated for a case containing 10 Stock-Keeping Units (...
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In this paper we propose an MILP model to address the optimization of the tactical planning for the Fast Moving Consumer Goods (FMCG) industry. This model is demonstrated for a case containing 10 Stock-Keeping Units (SKUs), but is intractable for realistically sized problems. Therefore, we propose a decomposition based on single-SKU submodels. To account for the interaction between SKUs, slack variables are introduced into the capacity constraints. In an iterative procedure the cost of violating the capacity is slowly increased, and eventually a feasible solution is obtained. Even for the relatively small 10-SKU case, the required CPU time could be reduced from 1144 s to 175 s using the algorithm. Moreover, the algorithm was used to optimize cases of up to 1000 SKUs, whereas the full model is intractable for cases of 25 or more SKUs. The solutions obtained with the algorithm are typically within a few percent of the global optimum. (C) 2013 Elsevier Ltd. All rights reserved.
In compressed sensing, sparse signal reconstruction is a required stage. To find sparse solutions of reconstruction problems, many methods have been proposed. It is time-consuming for some methods when the regularizat...
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In compressed sensing, sparse signal reconstruction is a required stage. To find sparse solutions of reconstruction problems, many methods have been proposed. It is time-consuming for some methods when the regularization parameter takes a small value. This paper proposes a decomposition algorithm for sparse signal reconstruction, which is almost insensitive to the regularization parameter. In each iteration, a subproblem or a small quadratic programming problem is solved in our decomposition algorithm. If the extended solution in the current iteration satisfies optimality conditions, an optimal solution to the reconstruction problem is found. On the contrary, a new working set must be selected for constructing the next subproblem. The convergence of the decomposition algorithm is also shown in this paper. Experimental results show that the decomposition method is able to achieve a fast convergence when the regularization parameter takes small values. (C) 2013 Elsevier B.V. All rights reserved.
We use the generalized two-sided Chebyshev inequality to reformulate a certain nonlinear, chance constrained new product risk model. The problem has a linear cost objective and a constraint set featuring a probabilist...
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We use the generalized two-sided Chebyshev inequality to reformulate a certain nonlinear, chance constrained new product risk model. The problem has a linear cost objective and a constraint set featuring a probabilistic lower bound on an event which depends on a collection of mutually independent, uniform random parameters. Our reformulation permits a reduction of the problem to a sequence of second-order cone programs. We, therefore, identify a new family of non-convex programs whose members are amenable to convex programming solution techniques. (C) 2019 Elsevier B.V. All rights reserved.
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