More issues in construction management were found especially for decision making that related to the Arabian construction management office requirements. Operation research especially linear programming models conside...
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More issues in construction management were found especially for decision making that related to the Arabian construction management office requirements. Operation research especially linear programming models considered one of the most important tool used in optimization applications at many fields of production engineering and mass production, also linear programming applications was developed to construction engineering field. This paper presents a linear programming technique to spotlight decision making application for optimizing competitive bidding strategy to select best tender as shown in real case study. Therefore, project manager or decision maker can use this concept for getting the best project cost. This paper give linear programming concepts that are reviewed to describe recent linear programming component which had large focus on related time-cost and time problems for studied project. linear programming models are formulated to solve various cost and time problems by using LINDO software. The developed models had many limitations and restrictions for studied project. Construction managers can use it to explore more possible opportunities to predict influence of decision for construction to facilitate preferred different management objectives. linear programming implementation shows the practice of wide variety for construction problems especially cost with time issues and it is more applicable to generate a shortest computational effort and time with low cost. (C) 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.
Agriculture plays a significant role in the social and economic development of a country. To get optimum farm outputs;decisions such as crop allocation, crop combinations, operational activities performed for crop pro...
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The paper deals with the question, "What is the Vehicle Routing Problem, Which Is Minimized Idle Time, and How Its linear programming Model Is Written?" In this study, a linear programming (LP) model has bee...
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We consider the problem of learning discounted cost optimal control policies for unknown deterministic discrete time systems with continuous state and action spaces. We show that a policy evaluation step of the well-k...
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ISBN:
(纸本)9781728113982
We consider the problem of learning discounted cost optimal control policies for unknown deterministic discrete time systems with continuous state and action spaces. We show that a policy evaluation step of the well-known policy iteration (PI) algorithm can be characterized as a solution to an infinite dimensional linear program (LP). However, when approximating such an LP with a finite dimensional program, the PI algorithm loses its nominal properties. We propose a data-driven PI scheme that ensures a certain monotonic behavior and allows for incorporation of expert knowledge on the system. A numerical example illustrates effectiveness of the proposed algorithm.
Link prediction in complex networks has always been a hot topic in statistical physics, sociology and information science. Since most works focus on undirected networks, how to predict missing links in directed comple...
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Link prediction in complex networks has always been a hot topic in statistical physics, sociology and information science. Since most works focus on undirected networks, how to predict missing links in directed complex networks remains a valuable and challenging problem. Many existing methods fail to differentiate the information provided by links with different orientations, nor do they consider the unequal contributions of neighbors, leading to deficiency in prediction accuracy. In this paper, we propose a novel link prediction method in directed networks. It calculates the contributions of three types of neighbors by solving a simple linear programming problem. Empirical studies on eight real-world networks show that the proposed method performs better under two evaluation metrics in comparison with nine state-of-art benchmarks.
In this paper, we consider the problem of finding the global shape for placement of cells in a chip that results in minimum wirelength. Under certain assumptions, we theoretically prove that some shapes are better tha...
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ISBN:
(纸本)9781450360074
In this paper, we consider the problem of finding the global shape for placement of cells in a chip that results in minimum wirelength. Under certain assumptions, we theoretically prove that some shapes are better than others for purposes of minimizing wirelength, while ensuring that overlap-removal is a key constraint of the placer. We derive some conditions for the optimal shape and obtain a shape which is numerically close to the optimum. We also propose a linear-programming-based spreading algorithm with parameters to tune the resultant shape and derive a cost function that is better than total or maximum displacement objectives, that are traditionally used in many numerical global placers. Our new cost function also does not require explicit wirelength computation, and our spreading algorithm preserves to a large extent, the relative order among the cells placed after a numerical placer iteration. Our experimental results demonstrate that our shape-driven spreading algorithm improves wirelength, routing congestion and runtime compared to a bi-partitioning based spreading algorithm used in a state-of-the-art academic global placer for FPGAs.
Background and Objective: This study focuses on Multi-Channel Transcranial Electrical Stimulation, a non-invasive brain method for stimulating neuronal activity under the influence of low-intensity currents. We introd...
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Background and Objective: This study focuses on Multi-Channel Transcranial Electrical Stimulation, a non-invasive brain method for stimulating neuronal activity under the influence of low-intensity currents. We introduce a mathematical formulation for finding a current pattern that optimizes an L1-norm fit between a given focal target distribution and volumetric current density inside the brain. L1-norm is well-known to favor well-localized or sparse distributions compared to L2-norm (least-squares) fitted ***: We present a linear programming approach that performs L1-norm fitting and penalization of the current pattern (L1L1) to control the number of non-zero currents. The optimizer filters a large set of candidate solutions using a two-stage metaheuristic search from a pre-filtered set of ***: The numerical simulation results obtained with both 8-and 20-channel electrode montages sug-gest that our hypothesis on the benefits of L1-norm data fitting is valid. Compared to an L1-norm regular-ized L2-norm fitting (L1L2) via semidefinite programming and weighted Tikhonov least-squares method (TLS), the L1L1 results were overall preferable for maximizing the focused current density at the target position, and the ratio between focused and nuisance current ***: We propose the metaheuristic L1L1 optimization approach as a potential technique to obtain a well-localized stimulus with a controllable magnitude at a given target position. L1L1 finds a current pattern with a steep contrast between the anodal and cathodal electrodes while suppressing the nuisance currents in the brain, hence, providing a potential alternative to modulate the effects of the stimulation, e.g., the sensation experienced by the subject.(c) 2022 Published by Elsevier B.V.
This paper presents an approach to generate optimal configurations for the neutron source distribution in subcritical systems. These optimal configurations are modeled as linear programming optimization problems (LPOP...
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Under the Strong Exponential Time Hypothesis, an integer linear program with n Boolean-valued variables and m equations cannot be solved in c(n) time for any constant c < 2. If the domain of the variables is relaxe...
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ISBN:
(纸本)9781450367059
Under the Strong Exponential Time Hypothesis, an integer linear program with n Boolean-valued variables and m equations cannot be solved in c(n) time for any constant c < 2. If the domain of the variables is relaxed to [0, 1], the associated linear program can of course be solved in polynomial time. In this work, we give a natural algorithmic bridging between these extremes of 0-1 and linear programming. Specifically, for any subset (finite union of intervals) E subset of [0, 1] containing {0, 1}, we give a random-walk based algorithm with runtime O-E ((2-measure(E))(n) poly( n, m)) that finds a solution in E-n to any n-variable linear program with m constraints that is feasible over {0, 1}(n). Note that as E expands from {0, 1} to [0, 1], the runtime improves smoothly from 2(n) to polynomial. Taking E = [0, 1/k) boolean OR (1 - 1/k, 1] in our result yields as a corollary a randomized (2 - 2/k)(n) poly( n) time algorithm for k-SAT. While our approach has some high level resemblance to Schoning's beautiful algorithm, our general algorithm is based on a more sophisticated random walk that incorporates several new ingredients, such as a multiplicative potential to measure progress, a judicious choice of starting distribution, and a time varying distribution for the evolution of the random walk that is itself computed via an LP at each step (a solution to which is guaranteed based on the minimax theorem). Plugging the LP algorithm into our earlier polymorphic framework yields fast exponential algorithms for any CSP (like k-SAT, 1-in-3-SAT, NAE k-SAT) that admit so-called "threshold partial polymorphisms."
Value Engineering is an effective technique for reducing costs, increasing productivity and improving quality. Value engineering is a technique directed toward analyzing the functions of an item or process to determin...
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Value Engineering is an effective technique for reducing costs, increasing productivity and improving quality. Value engineering is a technique directed toward analyzing the functions of an item or process to determine best value or the best relationship between worth and cost functions. Building construction costs comprises 2 major components as materials cost and labor cost. linear programming (LP) is a quantitative technique by means of a mathematical modeling technique designs to solve allocation problem (resource allocation) to achieve the maximum profit or lowest cost. Therefore the authors bringing the advantages of both as value engineering and linear programming combined to work together in order to be able to effectively reduced building construction costs or create maximum value engineering that can be best used in the construction projects. Aim of this paper shows the reduction of building construction cost as to achieved the targets for reducing building construction cost or maximum value engineering as much as possible that leads to efficiency of cost reduction by used of principles of value engineering to apply with using linear programming for the reduction of building construction costs based on the material costs and labor costs to serve construction resource reallocation by consideration is made only 3 type construction materials as concrete, rebar and formwork because of 3 type are main component of building construction materials.
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