Triangle enumeration is an important element in graph analysis, and because of this it is a topic that has been studied extensively. Although the formulation is simple, for large networks the computation becomes challenging as we have to deal with memory limitation and efficiency. Many algorithms have been proposed to overcome these problems. Some use distributed computing, where the computation is distributed among many machines in a cluster. However, this approach has a high cost in terms of hardware resources and energy. In this thesis we studied triangle counting/listing algorithms for both directed and undirected graphs, and searched for methods to do the computation on a single machine. Through detailed analysis, we found some ways to improve the efficiency of the computation. Programs that implement the algorithms were built and tested on large networks with up to almost a billion nodes. The results were then analysed and discussed. / Graduate

In this paper we propose an improved algorithm to search optimal solutions to the single machine total weighted tardiness scheduling problem. We propose using longest common sequence to combine with the random key method. Numerical simulation shows that the scheme we proposed could improve the search efficiency of genetic algorithm in this problem for some cases.

Nous considérons un problème d'ordonnancement à une machine avec dates de fin impératives et nous cherchons caractériser l'ensemble des solutions optimales, sans les énumérer. Nous supposons que les travaux sont numérotés selon la règle EDD et que cette séquence est réalisable. La méthode consiste à utiliser le treillis des permutations et d'associer à la permutation maximale du treillis la séquence EDD. Afin de caractériser beaucoup de solutions, nous cherchons une séquence réalisable aussi loin que possible de cette séquence. La distance utilisée est le niveau de la séquence dans le treillis, qui doit être minimum (le plus bas possible). Cette nouvelle fonction objectif est étudiée. Quelques cas particuliers polynomiaux sont identifiés, mais la complexité du problème général reste ouverte. Quelques méthodes de résolution, polynomiales et exponentielles, sont proposées et évaluées. Le niveau de la séquence étant en rapport avec la position des travaux dans la séquence, de nouvelles fonctions objectifs en rapport avec les positions des travaux sont identifiées et étudiées. Le problème de la minimisation de la somme pondérée des positions des travaux est prouvé fortement NP-difficile. Quelques cas particuliers sont étudiés et des méthodes de résolution proposées et évaluées. / We consider a single machine scheduling problem with deadlines and we want to characterise the set of optimal solutions, without enumerating them. We assume that jobs are numbered in EDD order and that this sequence is feasible. The key idea is to use the lattice of permutations and to associate to the supremum permutation the EDD sequence. In order to characterize a lot of solutions, we search for a feasible sequence, as far as possible to the supremum. The distance is the level of the sequence in the lattice, which has to be minimum. This new objective function is investigated. Some polynomially particular cases are identified, but the complexity of the general case problem remains open. Some resolution methods, polynomial and exponential, are proposed and evaluated. The level of the sequence being related to the positions of jobs in the sequence, new objective functions related to the jobs positions are identified and studied. The problem of minimizing the total weighted positions of jobs is proved to be strongly NP-hard. Some particular cases are investigated, resolution methods are also proposed and evaluated.

<p>This thesis contributes to theoretical and quantitative aspects of machine scheduling. In fact, it is dedicated to the issue of scheduling n jobs on one single machine. The scope is limited to deterministic problems - i.e. those with all data available and known with certainty in advance - with tardiness involved objectives; hence, the common denominator of all problems addressed are jobs with a predetermined due date assigned to. A job is finished on time as long as it is completed before its due date, otherwise it is said to be tardy. Since the single machine utilized is assumed to be restricted to process at most one job at a time, the aim is to find a proper sequence - a schedule - of how to process the jobs in order to best fulfill a certain objective. The contribution of this thesis aims at giving a state of the art survey and detailed review of research effort considering the objectives "minimizing the number of tardy jobs" and "minimizing the weighted number of tardy jobs". Further, the objectives of "minimizing the total tardiness", "minimizing the total weighted tardiness" and "minimizing the maximum tardiness" are adumbrated but reduced to a rough overview of research effort made.</p>

This thesis contributes to theoretical and quantitative aspects of machine scheduling. In fact, it is dedicated to the issue of scheduling n jobs on one single machine. The scope is limited to deterministic problems - i.e. those with all data available and known with certainty in advance - with tardiness involved objectives; hence, the common denominator of all problems addressed are jobs with a predetermined due date assigned to. A job is finished on time as long as it is completed before its due date, otherwise it is said to be tardy. Since the single machine utilized is assumed to be restricted to process at most one job at a time, the aim is to find a proper sequence - a schedule - of how to process the jobs in order to best fulfill a certain objective. The contribution of this thesis aims at giving a state of the art survey and detailed review of research effort considering the objectives "minimizing the number of tardy jobs" and "minimizing the weighted number of tardy jobs". Further, the objectives of "minimizing the total tardiness", "minimizing the total weighted tardiness" and "minimizing the maximum tardiness" are adumbrated but reduced to a rough overview of research effort made.

The accuracy, ease of use, and execution time of the finite element method based software Maxwell coupled to the system simulation software Simplorer was evaluated for simulation of hydropower generator - power grid interaction. A generator test rig were modelled in Maxwell and coupled to Simplorer with a strong circuit coupling as a single machine infinite bus system. The accuracy of the model was measured by comparing the simulated output power oscillation frequency and damping characteristics to the measured ones after a torque step. The result shows that the difference in output power oscillation frequency between the model and the generator test rig was small, and that the difference in damping characteristics was significant. The usability of the software package was found to be fair, as were the execution times.

ABSTRACT A HEURISTIC APPROACH FOR THE SINGLE MACHINE SCHEDULING TARDINESS PROBLEMS &Ouml / zbakir, Saffet Ilker M.Sc., Department of Industrial Engineering Supervisor : Prof. Dr. &Ouml / mer Kirca September 2011, 102 pages In this thesis, we study the single machine scheduling problem. Our general aim is to schedule a set of jobs to the machine with a goal to minimize tardiness value. The problem is studied for two objectives: minimizing total tardiness value and minimizing total weighted tardiness value. Solving optimally this problem is difficult, because both of the total tardiness problem and total weighted tardiness problem are NP-hard problems. Therefore, we construct a heuristic procedure for this problem. Our heuristic procedure is divided to two parts: construction part and improvement part. The construction heuristic is based on grouping the jobs, solving these groups and then fixing some particular number of jobs. Moreover, we used three type improvement heuristics. These are sliding forward method, sliding backward method and pairwise interchange method. Computational results are reported for problem size = 20, 40, 50 and 100 at total tardiness problem and for problem size = 20 and 40 at total weighted tardiness problem. Experiments are designed in order to investigate the effect of three factors which are problem size, tardiness factor and relative range of due dates on computational difficulties of the problems. Computational results show that the heuristic proposed in this thesis is robust to changes at these factors.

Hundreds of steel products need to be scheduled on a single or parallel machine in a steel plant every week. A good feasible schedule may save the company millions of dollars compared to a bad one. Single and parallel machine scheduling are also encountered often in many other industries, making it a crucial research topic for both the process system engineering and operations research communities. Single or parallel machine scheduling can be a challenging combinatorial optimization problem when a large number of jobs are to be scheduled. Each job has unique job characteristics, resulting in different setup times/costs depending on the processing sequence. They also have specific release dates to follow and due dates to meet. This work presents both an exact method using mixed-integer quadratic programming, and an approximate method with metaheuristics to solve real-world large-scale single/parallel machine scheduling problems faced in a steel plant. More than 1000 or 350 jobs are to be scheduled within a one-hour time limit in the single or parallel machine problem, respectively. The objective of the single machine scheduling is to minimize a combined total changeover, total earliness, and total tardiness cost, whereas the objective of the parallel machine scheduling is to minimize an objective function comprising the gaps between jobs before a critical time in a schedule, the total changeover cost, and the total tardiness cost. The exact method is developed to benchmark computation time for a small-scale single machine problem, but is not practical for solving the actual large-scale problem. A metaheuristic algorithm centered on variable neighborhood descent is developed to address the large-scale single machine scheduling with a sliding-window decomposition strategy. The algorithm is extended and modified to solve the large-scale parallel machine problem. Statistical tests, including Student's t-test and ANOVA, are conducted to determine efficient solution strategies and good parameters to be used in the metaheuristics. / Thesis / Master of Applied Science (MASc)

O presente trabalho aborda o problema de determinação de datas de entrega e o sequenciamento de tarefas com tempos de processamento estocásticos. O ambiente considerado constitui em uma máquina simples e tarefas com custos individuais e distintos de adiantamento e atraso de entrega (earliness e tardiness ou simplesmente E/T). O objetivo é determinar a sequência e as datas de entrega ótimas que simultaneamente minimizam o custo total esperado de E/T. Para a determinação de sequências candidatas, são apresentadas diversas heurísticas construtivas com tempo de execução polinomial baseadas em um método de inserção de tarefas. Considerando tarefas com distribuição normal, experimentos computacionais comprovam a eficácia dos algoritmos para problemas de menor porte, os quais fornecem soluções ótimas em 99,85% dos casos avaliados. Quando aplicadas a um conjunto com uma maior quantidade de tarefas, as heurísticas apresentaram resultados melhores do que o algoritmo disponível na literatura em mais de 80% dos casos. Consideradas tarefas com distribuição lognormal, obteve-se um percentual de otimalidade entre 93,87% e 96,45%, a depender da heurística aplicada. Demonstra-se ainda para o caso com distribuição normal que os métodos propostos são assintoticamente ótimos e, portanto, são indicados para a resolução de problemas de grande porte. / This work addresses the problem of concurrent due-date assignment and sequencing of a set of jobs on a stochastic single-machine environment with distinct job earliness and tardiness penalty costs. It is assumed that the jobs processing times are statistically independent and follow a normal distribution whose mean and variance are provided and they are not necessarily integer values. The objective is to determine the job sequence and the integer due dates which minimize the expected total earliness and tardiness costs. Several efficient insertion-based construction heuristics are proposed in order to find candidates for the optimal sequence with polynomial time complexity. For the normal distribution problem, numerical experiments show that the proposed heuristic methods are able to find the optimal solution in 99,85% when applied to problems with a smaller size. When applied to problems with a bigger size, the solutions found by the proposed heuristics had better costs than the solutions described in the literature in more than 80% of cases. For the lognormal distribution problem, the proposed heuristic methods provided solutions with a percentage of optimality between 93,87% and 96,45%. Furthermore, for the normal distribuition case, it was proven that the heuristics are asymptotically optimal, i.e., it can be used for problems of any size.

Majority of the studies in the scheduling literature is devoted to time based performance measures. In this thesis, we develop a model that considers monetary issues in single machine scheduling environments. We assume all the jobs should be completed by a common due date. An early revenue is earned if the completion time is before or on the due date, and a tardy revenue is gained if the job is completed after the due date. We consider restricted and unrestricted due date versions of the problem. Our objective is the maximization of the net present value of all revenues. We first investigate some special cases of the problem, and present polynomial time algorithms to solve them. Then, we develop branch and bound algorithms with lower and upper bounding mechanisms. Computational experiments have shown that the branch and bound algorithms can solve large-sized problems in reasonable times.