In this paper, we propose a new approximate algorithm for the model predictive control (MPC) problem with a time-varying reference of hybrid systems. The proposed algorithm consists of an offline computation and an on...
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In this paper, we propose a new approximate algorithm for the model predictive control (MPC) problem with a time-varying reference of hybrid systems. The proposed algorithm consists of an offline computation and an online computation. In the offline computation, candidates of mode sequences are derived. In the online computation, after the mode sequence is uniquely decided among candidates, the finite-time optimal control problem, i.e., the quadratic programming problem, is solved. So by applying the proposed algorithm, the computational amount of the online computation is decreased. First, the MPC problem with a time-varying reference is formulated. Next. the proposed algorithm is explained, and the accuracy of the obtained approximate solution is discussed. Finally, the effectiveness of the proposed method is shown by a numerical example.
This paper suggests an approximate algorithm, designed to solve nonlinear integer problems. This algorithm belongs to the class of component algorithms of feasible integer directions. The search for a feasible integer...
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This paper suggests an approximate algorithm, designed to solve nonlinear integer problems. This algorithm belongs to the class of component algorithms of feasible integer directions. The search for a feasible integer direction is done on the basis of a linear approximation of the objective function and the constraints at the integer points under consideration. Theoretical analysis is presented, as well as experimental investigation, using the algorithm for test examples taken from the literature.
Many business decision problems involve multiple objectives and can thus be described by multiple objective linear programming (MOLP) models. When a MOLP problem is being formulated, the parameters of objective functi...
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Many business decision problems involve multiple objectives and can thus be described by multiple objective linear programming (MOLP) models. When a MOLP problem is being formulated, the parameters of objective functions and constraints are normally assigned by experts. In most real situations, the possible values of these parameters are imprecisely or ambiguously known to the experts. Therefore, it would be more appropriate for these parameters to be represented as fuzzy numerical data that can be represented by fuzzy numbers. In this paper, a new approximate algorithm is developed for solving fuzzy multiple objective linear programming (FMOLP) problems involving fuzzy parameters in any form of membership functions in both objective functions and constraints. A detailed description and analysis of the algorithm are supplied. In addition, an example is given to illustrate the approximate algorithm. (c) 2005 Elsevier Inc. All rights reserved.
The objective of this article is to show the improvement reached by a ceramic logistics operator using an approximate algorithm for cargo of logistics of many different products with different weights and volumes. Thi...
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The objective of this article is to show the improvement reached by a ceramic logistics operator using an approximate algorithm for cargo of logistics of many different products with different weights and volumes. This algorithm, which has been used successfully for efficient assignment in logistics industry, where many different products in small but heavy items have to be distributed, can improve road transport efficiency for clients' orders in the minimum time and with the least possible costs. The paper describes how it could increase efficiency in logistics in a ceramic industry (from the initiation of activities and over several days to the end of the job cycle) and similar heavy and small items production when time and costs play the role in function criterion. The algorithm is based on several priority rules. Real life application of the algorithm developed here has been running on a time horizon of more than one week. Though the results of the first steps (initial solution) of algorithm are not as good as the results of already known algorithms for transportation assignments, the algorithm is improving the value of criterion function rapidly, during further iterations dealing with the sequences of daily assignments, which is a major improvement in applications for such types of algorithms, known up until now. The algorithm was a well accepted development and seen as very beneficial to the ceramics industry.
In the era of big data,correlation analysis is significant because it can quickly detect the correlation between *** then,it has been received much *** to the good properties of generality and equitability of the maxi...
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In the era of big data,correlation analysis is significant because it can quickly detect the correlation between *** then,it has been received much *** to the good properties of generality and equitability of the maximal information coefficient(MIC),MIC is a hotspot in the research of correlation ***,if the original approximate algorithm of MIC is directly applied into mining correlations in big data,the computation time is very *** the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n2.4 when parameters are *** the experiments show that the large number of candidate partitions of random relationships results in long computation *** analysis is a good preparation for the next step work of designing new fast algorithms.
Multiple conflicting objectives in many decision making problems can be well described by multiple objective linear programming (MOLP) models. This paper deals with the vague and imprecise information in a multiple ob...
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Multiple conflicting objectives in many decision making problems can be well described by multiple objective linear programming (MOLP) models. This paper deals with the vague and imprecise information in a multiple objective problem by fuzzy numbers to represent parameters of an MOLP model. This so-called fuzzy MOLP (or FMOLP) model will reflect some uncertainty in the problem solution process since most decision makers often have imprecise goals for their decision objectives. This study proposes an approximate algorithm based on a fuzzy goal optimization under the satisfactory degree alpha to handle both fuzzy and imprecise issues. The concept of a general fuzzy number is used in the proposed algorithm for an FMOLP problem with fuzzy parameters. As a result, this algorithm will allow decision makers to provide fuzzy goals in any form of membership functions.
To find a convex hull for n points in d-dimensional space, the optimal algorithm has time complexity O(n(right) (perpendicular d/2 left perpendicular)). When n and d are large, the execution time is very long. In this...
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To find a convex hull for n points in d-dimensional space, the optimal algorithm has time complexity O(n(right) (perpendicular d/2 left perpendicular)). When n and d are large, the execution time is very long. In this paper, we propose an approximate algorithm for computing multidimensional convex hulls. This algorithm finds quasi-two-side approximation to the hull to reduce the time for computing the exact hull boundary. To yield an epsilon-approximate convex hull, it has time complexity O(epsilon(-1/(d-1))n) and storage complexity O(epsilon(-1(d-1))). The approximate algorithm has several advantages: (1) it can easily be implemented, (2) it is suitable for parallel implementation, (3) it is much faster than the exact algorithm, (4) the user can choose to get more accurate results using longer computation time, and (5) it can be applied to solve many problems related to convex hull computation. (C) 1998 Elsevier Science Inc. All rights reserved.
Multiple constant multiplications (MCM) problem that is to obtain the minimum number of addition/subtraction operations required to implement the constant multiplications finds itself and its variants in many applicat...
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ISBN:
(纸本)9781605582313
Multiple constant multiplications (MCM) problem that is to obtain the minimum number of addition/subtraction operations required to implement the constant multiplications finds itself and its variants in many applications, such as finite impulse response (FIR) filters, linear signal transforms, and computer arithmetic. There have been a number of efficient algorithms proposed for the MCM problem. However, due to the NP-hardness of the problem, the proposed algorithms have been heuristics and cannot guarantee the minimum solution. In this paper, we introduce an approximate algorithm that can ensure the minimum solution on more instances than the previously proposed heuristics and can be extended to an exact algorithm using an exhaustive search. The approximate algorithm has been applied on a comprehensive set of instances including FIR filter and randomly generated hard instances, and compared with the previously proposed efficient heuristics. It is observed from the experimental results that the proposed approximate algorithm finds competitive and better results than the prominent heuristics.
The problem of rearranging manufacturing facilities over time is known as Dynamic Facility Layout Problem (DFLP). The objective is to minimize the sum of the material handling and the rearrangement costs. The problem ...
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ISBN:
(数字)9783642157660
ISBN:
(纸本)9783642157653
The problem of rearranging manufacturing facilities over time is known as Dynamic Facility Layout Problem (DFLP). The objective is to minimize the sum of the material handling and the rearrangement costs. The problem is NP-hard and has begun to receive attention very recently. In this paper, an approximate algorithm/ heuristic for solving DFLP is presented. The proposed heuristics has been applied to eight different data sets of a problem set (containing 48 data sets) given by Balakrishnan and Cheng [1], and it has been found that the proposed heuristic provide good solutions having about 12% of deviation from the best known solution available in the published literature. Further, as a future scope of research work, an improvement heuristic can be developed or some meta-heuristic approach such as simulated annealing, and tabu search can be further applied to improve the solution quality obtained from the proposed approximate algorithm.
Admission control is the problem of deciding for a given set of requests which of them to accept and which to reject, with the goal of maximizing the profit obtained from the accepted requests. The problem is consider...
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ISBN:
(纸本)9783642022692
Admission control is the problem of deciding for a given set of requests which of them to accept and which to reject, with the goal of maximizing the profit obtained from the accepted requests. The problem is considered in a scenario with advance reservations where multiple resources exist and users can specify several resource request alternatives. Each alternative is associated with a resource capacity requirement for a time interval on one of the multiple resources and a utility. We give a novel (1 + alpha)-approximation admission control algorithm with respect to the maximal utility and derive the approximation ratio for different request scenarios. We also design non-guaranteed greedy heuristics. We compare the performance of our approximation algorithm and greedy heuristics in aspect of utility optimality and timing in finding solutions. Simulation results show that on average our approximation algorithm appears to offer the best trade-off between quality of solution and computation cost. And our (1 + alpha)-approximation algorithm shows its intrinsic stability in performance for different utility functions.
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