Computer Engineering

The multimedia data objects scheduling problem for WWW applications is modeled using the twomachine #owshop problem of minimizing maximum lateness with separate setup times. We establish three dominance relations, and propose four heuristics. Also, we conduct computational experiments to compare the performance of the proposed heuristics and that of existing ones in the literature. The results of the computational experiments show that the proposed heuristics are quite e$cient.

In this paper, we address the two-stage assembly flowshop scheduling problem with a weighted sum of makespan and mean completion time criteria, known as bicriteria. Since the problem is NP-hard, we propose heuristics to solve the problem. Specifically, we propose three heuristics; simulated annealing (SA), ant colony optimization (ACO), and self-adaptive differential evolution (SDE). We have conducted computational experiments to compare the performance of the proposed heuristics. It is statistically shown that both SA and SDE perform better than ACO. Moreover, the experiments reveal that SA, in general, performs better than SDE, while SA consumes less CPU time than both SDE and ACO. Therefore, SA is shown to be the best heuristic for the problem.

In this paper, we address the three-machine flowshop scheduling problem. Setup times are considered separate from processing times, and the objective is to minimize total completion time. We show that the three-site distributed database scheduling problem can be modeled as a three-machine flowshop scheduling problem. A lower bound is developed and a dominance relation is established. Moreover, an upper bound is developed by using a three-phase hybrid heuristic algorithm. Furthermore, a branch-and-bound algorithm, incorporating the developed lower bound, dominance relation, and the upper bound is presented. Computational analysis on randomly generated problems is conducted to evaluate the lower and upper bounds, the dominance relation, and the branch-and-bound algorithm. The analysis shows the efficiency of the upper bound, and, hence, it can be used for larger size problems as a heuristic algorithm.

An optimal self-stabilizing algroithm is presented that ®nds the strongly connected components of a directed graph on an underlying network after OC rounds, where C is the length of the longest cycle in the graph. Because the algorithm is self-stabilizing, it is resilient to transient faults and does not require initialization. The proposed algorithm can withstand topology changes in the form of addition or removal of edges and vertices. A correctness proof of the algorithm is provided.

We address the problem of a finite horizon single item maintenance optimization structured as a combination of preventive and corrective maintenance in a nuclear power plant environment. We present Bayesian semiparametric models to estimate the failure time distribution and costs involved. The objective function for the optimization is the expected total cost of maintenance over the pre-defined finite time horizon. Typically, the mathematical modeling of failure times are based on parametric models. These models fail to capture the true underlying relationships in the data; indeed, under a parametric assumption, the hazard rates are treated as unimodal and skewed, which, as shown in this paper, is incorrect. Importantly, assuming an increasing failure rate, as is typically done, we show, is way off the mark in the present context. Since hazard and cost estimates feed into the optimization phase, from a risk management perspective, potentially gross errors, resulting from purely parametric models, can be obviated. We show the effectiveness of our approach using real data from the South Texas Project Nuclear Operating Company (STPNOC) located in Bay City, Texas.

In this paper we address the two-stage assembly flowshop scheduling problem with respect to the makespan criterion where setup times are considered as separate from processing times. We formulate the problem and obtain a dominance relation. Moreover, we propose two evolutionary heuristics: a Particle Swarm Optimization (PSO) and a Tabu search. We also propose a simple and yet efficient algorithm with negligible computational time. We have conducted extensive computational experiments to compare the two heuristics and the algorithm along with a random solution. The computational analysis indicates that both heuristics and the algorithm perform significantly well. The computational analysis also indicates that PSO is the best and that the difference between the average errors of PSO and the algorithm becomes small as the number of jobs increases, while the computational time of PSO becomes much larger. Moreover, the difference between the two errors becomes even smaller as the number of machines (at the first stage) and the ratio of setup times to processing times becomes smaller. Therefore, PSO is recommended for a number of jobs up to 50, whereas the algorithm is suggested for larger numbers of jobs and larger numbers of machines at the first stage.

The problem of scheduling a set of jobs with unit operation times in a job shop to minimize total completion time is addressed. It is shown that this problem can be modeled as finding the optimal coloring of a special mixed graph. Subgraph of such a mixed graph without edges represents union of paths, and subgraph without arcs represents union of cliques. Finding the optimal coloring of the mixed graph with the criterion of minimizing the sum of maximal colors (used for the paths) is shown to determine a schedule for minimizing total completion time for processing jobs in a job shop. Since the problem is NP-hard, we develop a branch and bound algorithm for obtaining the optimal coloring of such a mixed graph. We develop a color-based branching scheme, dominance of the vertices in the solution tree, and three lower bounds of the objective function. The computational experiments indicate that the branch and bound algorithm performs well for randomly generated mixed graphs of order up to 200, if all the paths in the directed subgraph (job routes) have the same length, and of order up to 750 otherwise. Since in the considered job shop problem, machine repetition and absence of some machines in the job route are allowed, the developed methodology can be used to minimize total completion time in the job shop with arbitrary integer operation durations. .kw (A. Allahverdi), andreev@newman.bas-net.by (G.V. Andreev).

The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.

We address the three-machine flowshop scheduling problem to minimise maximum lateness, where setup times are considered separate from processing times. We establish a dominance relation for the problem where it specifies which of the two adjacent jobs should precede in an optimal solution. Moreover, we propose three heuristics: Earliest Due Date (EDD), an Enhanced Earliest Due Date (EEDD) and a Polynomial Genetic-based Algorithm (PGA). We conduct computational analysis on randomly generated problems to evaluate the performance of the proposed heuristics. The analysis shows that the performance of EEDD is acceptable if the computational time is of the main concern and the number of jobs are large. The analysis also shows that PGA significantly outperforms EDD and EEDD.

The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.

This paper presents a stochastic model to evaluate the reliability and availability of a network service configuration consisting of a centralized database server and two distributed database server sets for a local/metropolitan area network. The centralized server monitors and controls the distributed database servers, which can be remotely separated from each other and from the center by almost any distance depending on the means of information transmission media between the center and the distributed servers. Using exponential time to failure and time to repair distributions for each of the database servers, exact numerical solutions are obtained for the steady state service reliability and availability measures. The results are then compared to approximate steady state numerical solutions for the general Weibull distribution case under different initial conditions. Finally, the model is extended to the case of three distributed database server sets. The reliability and availability study of such configurations has many applications in widely used internet services, mission-critical business fault tolerant systems, and automated manufacturing and service industries. A decision tool for the computer center manager to assess an upgrade to a three distributed database system from a two database system is also presented.

The two-machine flowshop problem of minimizing maximum lateness with separate setup times is used to model minimizing lateness of multimedia data object requests in Internet applications. We propose using a new genetic based algorithm (GA) to improve on existing methods in solving this problem. Also, we conduct computational experiments to compare the performance of the proposed heuristics and that of existing ones in the literature. The results of the computational experiments show that the proposed heuristics are quite efficient.

Abstract⎯The two-stage flowshop scheduling problem with separate setup times to minimize maximum lateness is addressed in this paper. It is well known that this problem is strongly NP-hard and that there exists at least one optimal solution which is a permutation schedule. A polynomial hybrid genetic based algorithm is proposed to find an approximate solution to this problem. The proposed algorithm is compared with the existing heuristics in the literature. Computational experiments show that the proposed hybrid algorithm significantly outperforms the existing ones. More specifically, the computational complexity of the proposed algorithm and the best existing heuristic is the same as O(n 3 ) while the average error of the best existing heuristic is 16 times that of proposed algorithm.

This paper presents two grammars for reading numbers of classical and modern Arabic language. The grammars make use of the structured Arabic counting system to present an accurate and compact grammar that can be easily implemented in different platforms. Automating the process of reading numbers from its numerical representation to its sentential form has many applications. Inquiring about your bank balance over the phone, automatically writing the amount of checks (from numerical form to letter form), and reading for the blind people are some of the fields that automated reading of numbers can be of service. The parsing problem of sentential representation of numbers in the Arabic language is also addressed. A grammar to convert from sentential representation to the numerical representation is also presented. Grammars presented can be used to translate from the sentential Arabic numbers to sentential English numbers, and vice versa, by using the common numerical representation as an intermediate code. Such methodology can be used to aid the automatic translation between the two natural languages. All grammars described in this paper have been implemented on a UNIX system. Examples of different number representations and the output of the implementation of the grammars are given as part of the paper.

The research literature provides strong evidence that characteristics of buildings and their indoor environments influence the prevalence of several adverse health effects. Kuwait is considered one of the countries with harshest weather conditions. It is estimated that Kuwaitis spend most of their times indoors. Indoor environments quality should be taken seriously since indoor allergens and irritants can play a significant role in determining the health of households. In this research we propose to profile synergistic interaction between morbidity differentials and air quality in Kuwait residential area. The objective of this project is to investigate the relation between indoors air quality and asthma symptoms. Data mining techniques are employed to discover the correlation between indoor air quality measures and asthma symptoms and trigger. The main trigger considered in this research is the concentration of nitrogen dioxide. Some other triggers investigated are dust mites, smoking and others.

The research literature provides strong evidence that characteristics of buildings and their indoor environments influence the prevalence of several adverse health effects. Kuwait is considered one of the countries with harshest weather conditions. It is estimated that Kuwaitis spend most of their times indoors. Indoor environments quality should be taken seriously since indoor allergens and irritants can play a significant role in determining the health of households. In this research we propose to profile synergistic interaction between morbidity differentials and air quality in Kuwait residential area. The objective of this project is to investigate the relation between indoors air quality and asthma symptoms. Data mining techniques are employed to discover the correlation between indoor air quality measures and asthma symptoms and trigger. The main trigger considered in this research is the concentration of nitrogen dioxide. Some other triggers investigated are dust mites, smoking and others.