This book presents the necessary and essential backgrounds of fuzzy set theory and linear programming, particularly a broad range of common Fuzzy linear programming (FLP) models and related, convenient solution techni...
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ISBN:
(数字)9783030174217
ISBN:
(纸本)9783030174194
This book presents the necessary and essential backgrounds of fuzzy set theory and linear programming, particularly a broad range of common Fuzzy linear programming (FLP) models and related, convenient solution techniques. These models and methods belong to three common classes of fuzzy linear programming, namely: (i) FLP problems in which all coefficients are fuzzy numbers, (ii) FLP problems in which the right-hand-side vectors and the decision variables are fuzzy numbers, and (iii) FLP problems in which the cost coefficients, the right-hand-side vectors and the decision variables are fuzzy numbers. The book essentially generalizes the well-known solution algorithms used in linear programming to the fuzzy environment. Accordingly, it can be used not only as a textbook, teaching material or reference book for undergraduate and graduate students in courses on applied mathematics, computer science, management science, industrial engineering, artificial intelligence, fuzzy information processes, and operations research, but can also serve as a reference book for researchers in these fields, especially those engaged in optimization and soft computing. For textbook purposes, it also includes simple and illustrative examples to help readers who are new to the field.
In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and rep...
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ISBN:
(纸本)9781538674741
In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequency. In this paper, we define a linear programming (LP) model to calculate the set of partial replicas with the lowest overall memory capacity while evenly balancing the query load. Furthermore, we propose a scalable decomposition heuristic to calculate solutions for larger problem sizes. While guaranteeing the same performance as state-of-the-art heuristics, our decomposition approach calculates allocations with up to 23% lower memory footprint for the TPC-H benchmark.
This paper investigates the problem of event-triggered control for positive switched systems without/with input saturation. A 1-norm based event-triggered mechanism is established for positive switched systems. Using ...
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ISBN:
(纸本)9781728112985
This paper investigates the problem of event-triggered control for positive switched systems without/with input saturation. A 1-norm based event-triggered mechanism is established for positive switched systems. Using a gain matrix decomposition technique. an event-triggered controller is designed for the systems without input saturation. The saturation term in the saturation systems is transformed into an interval form under the event-triggered mechanism. Then, the upper and lower bound of the saturation systems matrix are obtained. An event-triggered controller is designed to guarantee the positivity of the lower bound closed-loop systems and the stability of the upper bound closed-loop systems in the saturation systems. Meanwhile, a cone attraction domain is constructed and a cone attraction domain gain matrix design approach is proposed. All presented conditions can be formulated into linear programming. Compared with existing results, the event-triggered control is more practical and efficient. Finally, a simulation example is provided to illustrate the validity of the design.
Parametric linear programming is central in polyhedral computations and in certain control applications. We propose a task-based scheme for parallelizing it, with quasi-linear speedup over large problems.
ISBN:
(纸本)9783030227500;9783030227494
Parametric linear programming is central in polyhedral computations and in certain control applications. We propose a task-based scheme for parallelizing it, with quasi-linear speedup over large problems.
An expatriate is a person who lives temporarily or resides outside her/his native country or a foreign who lives in a country. It is usually because of a state or professional duty. The activity of sending employees t...
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An expatriate is a person who lives temporarily or resides outside her/his native country or a foreign who lives in a country. It is usually because of a state or professional duty. The activity of sending employees to branch companies in other countries is called expatriation. Expatriation activities are high cost activities, so the need of sending employees to branch companies in other countries must be carefully calculated. Assignment problems occur in various decision-making situations which include assignment of work for machinery, agents for special tasks, sales personnel in the sales area and others. The distinguishing characteristic of an assignment problem is that one agent is assigned to one task. The research was conducted at PT. Asian Hybrid Seed Technologies Indonesia, where the company will open three new marketing areas and require expatriates to be assigned as project leaders. In this article, a set of assignments to optimize the objectives function, namely minimizing cost and time, was solved by using linear programming model.
This paper development of crisp and fuzzy portfolio models using linear programming. Using Lagrange multiplier method the solution of linear programming is carried out. As a input data past gains of assets and values ...
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ISBN:
(纸本)9783030041649
This paper development of crisp and fuzzy portfolio models using linear programming. Using Lagrange multiplier method the solution of linear programming is carried out. As a input data past gains of assets and values of expect gains are taken. The portfolio selection model, in the form of linear programming, based on the values of expected gains and the variance of securities is formulated. The gradient method is connected to discover weights values of assets. The a level method and interim number arithmetic is utilized to take care of fuzzy enhancement issue and locate the ideal fuzzy estimations of the securities.
In this paper we present models and optimization algorithms to rapidly compute the fuel-optimal energy management strategies of a hybrid electric powertrain for a given driving cycle. Specifically, we first identify a...
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In this paper we present models and optimization algorithms to rapidly compute the fuel-optimal energy management strategies of a hybrid electric powertrain for a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear problem and demonstrate its effectiveness by assessing the impact of different battery charge targets on the achievable fuel consumption. Numerical results show that the proposed algorithm can assess fuel-optimal control strategies in a few seconds, paving the way for extensive parameter studies and real-time implementations. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
In this paper, we propose an approach for learning sparse reject option classifiers using double ramp loss L-dr. We use DC programming to find the risk minimizer. The algorithm solves a sequence of linear programs to ...
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ISBN:
(纸本)9781577358091
In this paper, we propose an approach for learning sparse reject option classifiers using double ramp loss L-dr. We use DC programming to find the risk minimizer. The algorithm solves a sequence of linear programs to learn the reject option classifier. We show that the loss L-dr is Fisher consistent. We also show that the excess risk of loss L-d is upper bounded by excess risk of L-dr. We derive the generalization error bounds for the proposed approach. We show the effectiveness of the proposed approach by experimenting it on several real world datasets. The proposed approach not only performs comparable to the state of the art, it also successfully learns sparse classifiers.
The class of multiset combinatorial batch codes (MCBCs) was introduced by Zhang et al. (2018) as a generalization of combinatorial batch codes (CBCs). MCBCs allow multiple users to retrieve items in parallel in a dist...
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ISBN:
(纸本)9781538692912
The class of multiset combinatorial batch codes (MCBCs) was introduced by Zhang et al. (2018) as a generalization of combinatorial batch codes (CBCs). MCBCs allow multiple users to retrieve items in parallel in a distributed storage system and a fundamental objective in this study is to determine the minimum total storage given certain requirements. We formulate an integer linear programming problem so that its optimal solution provides a lower bound of the total storage of MCBCs. Borrowing techniques from linear programming, we improve known lower bounds in some cases and also, determine the exact values for some parameters.
A large data set is used to infer the fraction of immune cells using gene expression data. A model should be developed to infer the fraction of immune cells from a small set of genes used in cell surface marker experi...
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ISBN:
(纸本)9781728118673
A large data set is used to infer the fraction of immune cells using gene expression data. A model should be developed to infer the fraction of immune cells from a small set of genes used in cell surface marker experiments. We present a model created by linear programming (LP) by simplex method. To evaluate the accuracy of the algorithm, we created a simulated data set and evaluated its performance against the digital cell quantization (DCQ) algorithm. Finally, we applied our LP method to the systemic lupus erythematosus (SLE) patient dataset and examined the differences from healthy controls.
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