In this paper, a methodology to solve the optimal reactive power dispatch (ORPD) in electric power systems (EPS), considering discrete controllers, is proposed. Discrete controllers, such as the tap position of on-loa...
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In this paper, a methodology to solve the optimal reactive power dispatch (ORPD) in electric power systems (EPS), considering discrete controllers, is proposed. Discrete controllers, such as the tap position of on-load tap changing (OLTC) transformers and switchable reactive shunt compensation, are optimized by the proposed method. A semidefinite relaxation (SDR) of the ORPD problem and a branch-and-bound (B&B) algorithm have been fully deployed. A new formulation is presented for the OLTC transformers to obtain a connected structure of the semidefinite programming (SDP) matrices. The customized B&B algorithm deals with the discrete nature of the binary control variables. Moreover, in order to enhance the convexification, valid inequalities called lifted nonlinear cuts (NLC) are implemented in the SDR. Additionally, a chordal decomposition technique is used to improve the computational performance. Finally, the B&B algorithm is used to solve the mixed-integer semidefinite programming problem. Several benchmarks have been used to show the accuracy and scalability of the proposed method, and convergence analysis shows that near-global optimal solutions are generated with small relaxation gaps.
We examine a non-cyclic scheduling problem of a timed Petri net (TPN) with a branch and bound (B & B) algorithm. There have been many approaches and algorithms for conventional scheduling problems such as job shop...
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ISBN:
(纸本)9781467317139
We examine a non-cyclic scheduling problem of a timed Petri net (TPN) with a branch and bound (B & B) algorithm. There have been many approaches and algorithms for conventional scheduling problems such as job shops, resource-constrained project scheduling problems (RCPSPs), and robotized system scheduling problems. Most of these methods have focused on their effectiveness or efficiency in solving their own problems. However, they tend to ignore the issue of compatibility with other scheduling problems and the solution methods are ad hoc and hard to be used for other scheduling problems with even small changes. Petri nets have been widely used for modeling and analyzing complex discrete event dynamic systems, such as robotized manufacturing cells or other automated manufacturing systems. There are studies on scheduling cyclic Petri net models and some non-cyclic Petri net models for specific applications. In this paper, we examine a scheduling problem for non-cyclic TPNs, where there is the starting and end transitions, and the transitions do not repeat an identical firing cycle. We also allow multiple arc weights in TPNs so as to model batch processing of tasks at a resource and multiple units of a resource required for a task. We briefly explain how various scheduling constraints and objectives can be modeled by TPNs. Then, we develop an efficient B & B procedure that utilizes a dynamic branching strategy and a resource-based lower bound. We finally present examples of the B & B algorithm for an RCPSP and a single-armed cluster tool scheduling problem.
This paper proposes an efficient algorithm to deal with various scheduling requirements of diverse structure cluster *** tools not only have complex flow patterns such as parallel PMs,wafer residency time constraints ...
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This paper proposes an efficient algorithm to deal with various scheduling requirements of diverse structure cluster *** tools not only have complex flow patterns such as parallel PMs,wafer residency time constraints and dual-arm robot,but also have different structures such as the equipment front-end module which consists of an aligner,a signal-arm robot and two load *** solutions for different requirements may be *** solve this problem,the Petri net models of different configured cluster tool with diverse scheduling requirements is developed ***,based on the Petri net models the state search tree is presented,which can efficiently determine the local optimal scheduling *** experiments,the effectiveness of the branch and bound algorithm proposed in this paper is verified.
We examine a scheduling problem of a wet station with multiple job flows. The wet station performs cleaning processes for removing residual contaminants after wafer fabrication processes. It consists of several chemic...
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ISBN:
(纸本)9781467317139
We examine a scheduling problem of a wet station with multiple job flows. The wet station performs cleaning processes for removing residual contaminants after wafer fabrication processes. It consists of several chemical and rinse baths, and multiple transport robots. Most studies on scheduling robotized systems including a wet station assume identical jobs and deal with cyclic scheduling that repeats a predefined work cycle. However, jobs arrive dynamically and many different jobs are processed concurrently at a wet station. We therefore examine a non-cyclic scheduling problem of the wet station to minimize the makespan. We first develop a Petri net model and solve the problem using a branch and bound (B & B) algorithm. We also propose a dynamic branching method and evaluate a lower bound based on a bottleneck process. During searching the nodes, we analyze deadlocks and add places to the Petri net model for precedence relations among the robot tasks by applying the deadlock prevention conditions. We finally show that the proposed B & B algorithm is sufficient to solve practical problems.
Network header steganography is a specialized field of steganography that focuses on embedding hidden data within network headers rather than the payload of a communication. Traditional methods typically utilize a sin...
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Network header steganography is a specialized field of steganography that focuses on embedding hidden data within network headers rather than the payload of a communication. Traditional methods typically utilize a single header field as a covert channel to conceal secret messages. However, these approaches are susceptible to detection using statistical analysis techniques, such as the chi-square test, which can identify anomalies in the data distribution. In this paper, we present an enhanced algorithm to overcome the limitations of single-channel steganography. Our method manipulates the data with redundant bits generated by BCH code. By carefully adjusting these bits, the algorithm effectively evades detection by the chi-square test, making the concealed data virtually undetectable to standard statistical scrutiny. On the receiver side, we employ peterson-gorenstein-zierler decoder to accurately recover the adjusted bits, ensuring reliable communication without data loss. This innovative approach significantly improves the security of network header steganography. Experimental results demonstrate that our algorithm offers at least 80% higher security compared to previous techniques, marking a substantial advancement in the field. This improvement enhances the practicality and robustness of covert communication in network systems, addressing a critical challenge faced by earlier methods.
In this paper we consider two branch and bound algorithms for the maximum clique problem which demonstrate the best performance on DIMACS instances among the existing methods. These algorithms are MCS algorithm by Tom...
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In this paper we consider two branch and bound algorithms for the maximum clique problem which demonstrate the best performance on DIMACS instances among the existing methods. These algorithms are MCS algorithm by Tomita et al. (2010) and MAXSAT algorithm by Li and Quan (2010a, b). We suggest a general approach which allows us to speed up considerably these branch and bound algorithms on hard instances. The idea is to apply a powerful heuristic for obtaining an initial solution of high quality. This solution is then used to prune branches in the main branch and bound algorithm. For this purpose we apply ILS heuristic by Andrade et al. (J Heuristics 18(4):525-547, 2012). The best results are obtained for p_hat1000-3 instance and gen instances with up to 11,000 times speedup.
This paper analyzes the performances of parallel branch and bound algorithm with best-first search strategy by examining various anomalies on the expected speed up: detrimental, acceleration and detrimental accelerati...
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This paper analyzes the performances of parallel branch and bound algorithm with best-first search strategy by examining various anomalies on the expected speed up: detrimental, acceleration and detrimental acceleration. Since the best evaluation is not always sufficient to distinguish the best node to choose with best-first search strategy, we define tie breaking rules for cases when nodes have the same value: the fifo, the lifo and the consistent rules. The purpose of the paper is to convey, through bounds of the parallel execution for each tie breaking rule, an understanding of the nature of the anomalies, the range of their impact and a comparison of their efficiency to cope with these anomalies. Sufficient and necessary conditions are given regarding the predisposition for each of the three classes of anomalous behavior. For comparison, we introduce a propriety of proneness to anomaly. In particular, we show that the consistent rule on best-first search branch and bound algorithm may be the weaker solution to cope the detrimental acceleration anomaly. Finally, we prove that the fifo rule is theoretically and practically efficient.
The branch and bound algorithm is an optimal feature selection method that is well-known for its computational efficiency. However, when the dimensionality of the original feature space is large, the computational tim...
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The branch and bound algorithm is an optimal feature selection method that is well-known for its computational efficiency. However, when the dimensionality of the original feature space is large, the computational time of the branch and bound algorithm becomes very excessive. If the optimality of the solution is allowed to be compromised, one can further improve the search speed of the branch and bound algorithm;the look-ahead search strategy can be employed to eliminate many solutions deemed to be suboptimal early in the search. In this paper, a comparative study of the look-ahead scheme in terms of the computational cost and the solution quality on four major branch and bound algorithms is carried out on real data sets. We also explore the use of suboptimal branch and bound algorithms on a high-dimensional data set and compare its performance with other well-known suboptimal feature selection algorithms. (C) 2014 Elsevier B. V. All rights reserved.
A branch and bound algorithm (B&B) has been widely used in various discrete and combinatorial optimization fields. To obtain optimal solutions as soon as possible for scheduling problems, three tools, which are br...
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A branch and bound algorithm (B&B) has been widely used in various discrete and combinatorial optimization fields. To obtain optimal solutions as soon as possible for scheduling problems, three tools, which are branching, bounding and dominance rules, have been developed in the B&B algorithm. One of these tools, a branching is a method for generating subproblems and directly determines size of solution to be searched in the B&B algorithm. Therefore, it is very important to devise effective branching scheme for the problem. In this note, a survey of branching schemes is performed for parallel machines scheduling (PMS) problems with n independent jobs and m machines and new branching schemes that can be used for identical and unrelated PMS problems, respectively, are suggested. The suggested branching methods show that numbers of generated subproblems are much smaller than that of other methods developed earlier and therefore, it is expected that they help to reduce a lot of CPU time required to obtain optimal solutions in the B&B algorithm. (C) 2009 Elsevier Ltd. All rights reserved.
The branch and bound algorithm is widely known as an efficient approach for selecting optimal feature subsets. If the optimality of the solution is allowed to be compromised, it is possible to further improve the sear...
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The branch and bound algorithm is widely known as an efficient approach for selecting optimal feature subsets. If the optimality of the solution is allowed to be compromised, it is possible to further improve the search speed of the branch and bound algorithm. This paper studies the look-ahead search strategy which can eliminate many solutions deemed to be suboptimal early in the branch and bound search. We propose ways to incorporate the look-ahead search scheme into four major branch and bound algorithms, namely the basic branch and bound algorithm, the ordered branch and bound algorithm, the fast branch and bound algorithm, and the adaptive branch and bound algorithm. A comparative study of the look-ahead scheme in terms of the computational cost and the solution quality on these suboptimal branch and bound algorithms is carried out on real data sets. Furthermore, we test the feasible use of suboptimal branch and bound algorithms on a high-dimensional data set and compare its performance with other well-known suboptimal feature selection algorithms. (C) 2014 Elsevier Inc. All rights reserved.
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