Demand for poultry meat and eggs is increasing at a faster pace due to its good quality, nutritive values, and reasonable price. With the growing demand for egg and poultry meat, the demand for poultry feed is also in...
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Demand for poultry meat and eggs is increasing at a faster pace due to its good quality, nutritive values, and reasonable price. With the growing demand for egg and poultry meat, the demand for poultry feed is also increasing. Most of the feed ingredients which are used in poultry feed are also used for human nutrition. So these major feed ingredients and cumulatively poultry feed are facing market competition with increased cost. This study proposed linear programming (LP) technique to minimize the feed cost for small scale poultry farms. It employs locally available feed ingredients to formulate the broiler starter and finisher feed mix. The dietary nutrient requirement for broiler starter and finisher stage were determined from the prescribed standard specifications by Indian standard institutes and National Research Centers, Indian Council of Agricultural Research (ICAR). Sixteen feed ingredients were selected to formulate the optimal feed mix to minimize the total cost of feed mix subject to the essential nutrient constraints. Microsoft excel solver was used for the formulation of liner programming model and optimal feed mix for broiler starter and finisher were obtained.
Many technical systems like manufacturing plants or software applications generate large event sequences. Knowing the temporal relationship between events is important for gaining insights into the status and behavior...
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Many technical systems like manufacturing plants or software applications generate large event sequences. Knowing the temporal relationship between events is important for gaining insights into the status and behavior of the system. This paper proposes a novel approach for identifying the time lag between different event types. This identification task is formulated as a binary integer optimization problem that can be solved efficiently and close to optimality by means of a linear programming approximation. The performance of the proposed approach is demonstrated on synthetic and real-world event sequences. (C) 2018 Elsevier Ltd. All rights reserved.
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 research describes a real-time optimization model for multi-agent demand response (DR) from a Load Serving Entity (LSE) perspective. Three major categories of customers and five types of energy resources are cons...
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This research describes a real-time optimization model for multi-agent demand response (DR) from a Load Serving Entity (LSE) perspective. Three major categories of customers and five types of energy resources are considered simultaneously to achieve efficient DR decision making in highly stochastic future energy markets. Two infinite horizon stochastic optimization models are formulated;specifically, an LSE model and a dynamic pricing customer model. The objective of these models is to minimize long-term cost and discomfort penalty of the LSE and dynamic pricing customers. Because preferences of these two agents are different, they are inseparable and difficult to solve. A deterministic finite horizon linear program is solved as an approximation of the suggested stochastic model, and computational experiments are provided.
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