The problem of scheduling a parallel program presented by a weighted directed acyclic graph (DAG) to the set of homogeneous processors for minimizing the completion time of the program has been extensively studied as academic optimization problem which occurs in optimizing the execution time of parallel algorithm with parallel computer.In this paper, we propose an application of the Ant Colony Optimization (ACO) to a multiprocessor scheduling problem (MPSP). In the MPSP, no preemption is allowed and each operation demands a setup time on the machines. The problem seeks to compose a schedule that minimizes the total completion time.We therefore rely on heuristics to find solutions since solution methods are not feasible for most problems as such. This novel heuristic searching approach to the multiprocessor based on the ACO algorithm a collection of agents cooperate to effectively explore the search space.A computational experiment is conducted on a suit of benchmark application. By comparing our algorithm result obtained to that of previous heuristic algorithm, it is evince that the ACO algorithm exhibits competitive performance with small error ratio.

This thesis presents an application of the Ant Colony algorithm for optimizing filter placement and sizing on a radial distribution system to reduce power losses and keep the effective harmonic voltage values and the total harmonic distortion (THD) within prescribed limits. First, a harmonic load flow (HLF) algorithm is performed to demonstrate the effect of harmonic sources on total power loss. Then the Ant Colony algorithm is used in conjunction with HLF to place a selection of filter sizes available at each possible location so that both power loss and THD are minimized. As a result the optimal adjustment of location and size of the filter are determined. Results of computational experiments on standard test systems are presented to demonstrate improvement and effectiveness of using the filters at the optimal location. The methodology used can be easily extended to different distribution network configurations. / Master Thesis

abstract: This thesis presents a successful application of operations research techniques in nonprofit distribution system to improve the distribution efficiency and increase customer service quality. It focuses on truck routing problems faced by St. Mary’s Food Bank Distribution Center. This problem is modeled as a capacitated vehicle routing problem to improve the distribution efficiency and is extended to capacitated vehicle routing problem with time windows to increase customer service quality. Several heuristics are applied to solve these vehicle routing problems and tested in well-known benchmark problems. Algorithms are tested by comparing the results with the plan currently used by St. Mary’s Food Bank Distribution Center. The results suggest heuristics are quite completive: average 17% less trucks and 28.52% less travel time are used in heuristics’ solution. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2015

The subject of the thesis is the optimization of one the key processes in a book company, namely warehouse logistics. This problem consist of two parts; first part focuses on route optimization of completion of customers' orders on daily basis, second part focuses on assigning of storage subsystems and their interconnection. The proposed solution uses a traveller salesman problem implemented into intranet application. Second problem was designed as quadratic assignment problem with use of ex post data analysis. On the basis of achieved results, we presented effective procedures for solving both of aforementioned problems and suggested their implementation into the company's enterprise resource planning system. Keywords:

First part of the thesis is about literature research of optimization algorithms. Three of the algorithms were implemented and tested, concretely the ant colony algorithm, tabu search and simulated annealing. All three algorithms were implemented to solve the traveling salesman problem. In second part of the thesis the algorithms were tested and compared. In last part the influence of the ant colony parameters was evaluated.

<div>The innovations in Magnetic Resonance Imaging (MRI) in the recent decades have given rise to large open-source datasets. MRI affords researchers the ability to look at both structure and function of the human brain. This dissertation will make use of one of these large open-source datasets, the Human Connectome Project (HCP), to study the structural and functional connectivity in the brain.</div><div>Communication processes within the human brain at different cognitive states are neither well understood nor completely characterized. We assess communication processes in the human connectome using ant colony-inspired cooperative learning algorithm, starting from a source with no <i>a priori</i> information about the network topology, and cooperatively searching for the target through a pheromone-inspired model. This framework relies on two parameters, namely <i>pheromone</i> and <i>edge perception</i>, to define the cognizance and subsequent behaviour of the ants on the network and the communication processes happening between source and target. Simulations with different configurations allow the identification of path-ensembles that are involved in the communication between node pairs. In order to assess the different communication regimes displayed on the simulations and their associations with functional connectivity, we introduce two network measurements, effective path-length and arrival rate. These measurements are tested as individual and combined descriptors of functional connectivity during different tasks. Finally, different communication regimes are found in different specialized functional networks. This framework may be used as a test-bed for different communication regimes on top of an underlying topology.</div><div>The assessment of brain <i>fingerprints</i> has emerged in the recent years as an important tool to study individual differences. Studies so far have mainly focused on connectivity fingerprints between different brain scans of the same individual. We extend the concept of brain connectivity fingerprints beyond test/retest and assess <i>fingerprint gradients</i> in young adults by developing an extension of the differential identifiability framework. To do so, we look at the similarity between not only the multiple scans of an individual (<i>subject fingerprint</i>), but also between the scans of monozygotic and dizygotic twins (<i>twin fingerprint</i>). We have carried out this analysis on the 8 fMRI conditions present in the Human Connectome Project -- Young Adult dataset, which we processed into functional connectomes (FCs) and time series parcellated according to the Schaefer Atlas scheme, which has multiple levels of resolution. Our differential identifiability results show that the fingerprint gradients based on genetic and environmental similarities are indeed present when comparing FCs for all parcellations and fMRI conditions. Importantly, only when assessing optimally reconstructed FCs, we fully uncover fingerprints present in higher resolution atlases. We also study the effect of scanning length on subject fingerprint of resting-state FCs to analyze the effect of scanning length and parcellation. In the pursuit of open science, we have also made available the processed and parcellated FCs and time series for all conditions for ~1200 subjects part of the HCP-YA dataset to the scientific community.</div><div>Lastly, we have estimated the effect of genetics and environment on the original and optimally reconstructed FC with an ACE model.</div>

The Master Thesis deals with logistic module innovation of information system ERP. The principle of innovation is based on implementation of heuristic algorithms which solve Travel Salesman Problems (TSP). The software MATLAB is used for analysis and tests of these algorithms. The goal of Master Thesis is the comparison of selections algorithm, which are suitable for economic purposes (accuracy of solution, speed of calculation and memory demands).