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.
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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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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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.
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."
This paper presents a family of generative linear programming models that permit to compute the exact Wasserstein Barycenter of a large set of two-dimensional images. Wasserstein Barycenters were recently introduced t...
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ISBN:
(纸本)9783030192129;9783030192112
This paper presents a family of generative linear programming models that permit to compute the exact Wasserstein Barycenter of a large set of two-dimensional images. Wasserstein Barycenters were recently introduced to mathematically generalize the concept of averaging a set of points, to the concept of averaging a set of clouds of points, such as, for instance, two-dimensional images. In Machine Learning terms, the Wasserstein Barycenter problem is a generative constrained optimization problem, since the values of the decision variables of the optimal solution give a new image that represents the "average" of the input images. Unfortunately, in the recent literature, linear programming is repeatedly described as an inefficient method to compute Wasserstein Barycenters. In this paper, we aim at disproving such claim. Our family of linear programming models rely on different types of Kantorovich-Wasserstein distances used to compute a barycenter, and they are efficiently solved with a modern commercial linear programming solver. We numerically show the strength of the proposed models by computing and plotting the barycenters of all digits included in the classical MNIST dataset.
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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