On-line Scheduling Heuristics in Distributed Environments

Rad se bavi specifičnim tipom raspoređivanja na paralelnim strojevima. Bavi se raspoređivanjem poslova na izvršne čvorove kroz mrežu servera za raspoređivanje. Pri tome je cilj optimizacija vremena trajanja. U ovom problemu izvršni čvorovi predstavljaju strojeve sa ograničenjima pridruživanja poslova. Svaki posao je ograničen na samo jedan stroj, a početak izvođenja mu može ovisiti o nekom drugom zadatku. Rad opisuje tri faze rješavanja problema. U svakoj od faza je predstavljen dio sustava i neke rukom pisane heuristike koje su korištene u rješavanju problema. Rad isto predstavlja neke tehnike strojnog učenja poput genetskog programiranja i neuronskih mreža koje su korištene da bi se proizvele što bolje heuristike.This thesis tackles a specific type of multiple machine scheduling problem. It deals with scheduling tasks on executing nodes through a network of scheduling servers, where the goal is to optimize the makespan. In this problem executing nodes represent the machines with eligibility restrictions. Tasks are machine bound because every task can only be executed at a specific machine and they can also be precedence constrained to other tasks. The thesis describes the tackling of the problem in three phases. Each of those phases presents a part of the system and some hand written heuristics that were used in order to solve the problem. The thesis also presents some machine learning techniques, like genetic programming and neural networks, which were used in order to produce the best possible heuristics

Optimization efficiency based on fitness landscape

Ovaj rad se bavi ispitivanjem dali se mogu klasificirati optimizacijski problemi i onda odrediti dali je neki algoritam optimizacije dobar za optimiranje određene klase problema, sa ciljem smanjenja vremena potrebnog za određivanjem dobrog optimizacijskog algoritma na novom optimizacijskom problemu. Algoritmi se pokušavaju klasificirati pomoću krajolika dobrote tih problema. Testne funkcije korištene u radu grupirane su u klase pomoću K-means algoritma. U ovom radu korištena su 4 modela klasifikacije strojnim učenjem: stablo odluke generirano genetskim programiranjem, simbolička regresija generirana genetskim programiranjem, učenje neuronske mreže diferencijskom evolucijom i učenje neuronske mreže algoritmom selekcije klona. Ispitivanje se izvodi u 2 faze, u prvoj fazi se određuju najbolji parametri za svaki od modela učenja, dok se u drugoj fazi uspoređuju modeli i procjenjuje se njihova uspješnost klasifikacije.This paper is concerned with examining whether you can classify an optimization problem and then determine is some optimization algorithm good for optimizing that problem with goal of reducing time required for determining that. Algorithms where classified using fitness landscape values. Fitness landscape values were calculated from optimisation problems. Test functions where classified using k-means algorithm. In this paper 4 classification models were used: symbolic regression generated by genetic programming, decision three generated by genetic programming, neural network trained by differential evolution and finaly neural network trained by clonal selection algorithm. Testing was done in 2 faze. In first faze best parameters for all modes where determent, while in the second phase models were compared and evaluated by its performance in classification

Optimization efficiency based on fitness landscape

Ovaj rad se bavi ispitivanjem dali se mogu klasificirati optimizacijski problemi i onda odrediti dali je neki algoritam optimizacije dobar za optimiranje određene klase problema, sa ciljem smanjenja vremena potrebnog za određivanjem dobrog optimizacijskog algoritma na novom optimizacijskom problemu. Algoritmi se pokušavaju klasificirati pomoću krajolika dobrote tih problema. Testne funkcije korištene u radu grupirane su u klase pomoću K-means algoritma. U ovom radu korištena su 4 modela klasifikacije strojnim učenjem: stablo odluke generirano genetskim programiranjem, simbolička regresija generirana genetskim programiranjem, učenje neuronske mreže diferencijskom evolucijom i učenje neuronske mreže algoritmom selekcije klona. Ispitivanje se izvodi u 2 faze, u prvoj fazi se određuju najbolji parametri za svaki od modela učenja, dok se u drugoj fazi uspoređuju modeli i procjenjuje se njihova uspješnost klasifikacije.This paper is concerned with examining whether you can classify an optimization problem and then determine is some optimization algorithm good for optimizing that problem with goal of reducing time required for determining that. Algorithms where classified using fitness landscape values. Fitness landscape values were calculated from optimisation problems. Test functions where classified using k-means algorithm. In this paper 4 classification models were used: symbolic regression generated by genetic programming, decision three generated by genetic programming, neural network trained by differential evolution and finaly neural network trained by clonal selection algorithm. Testing was done in 2 faze. In first faze best parameters for all modes where determent, while in the second phase models were compared and evaluated by its performance in classification

On-line Scheduling Heuristics in Distributed Environments

Rad se bavi specifičnim tipom raspoređivanja na paralelnim strojevima. Bavi se raspoređivanjem poslova na izvršne čvorove kroz mrežu servera za raspoređivanje. Pri tome je cilj optimizacija vremena trajanja. U ovom problemu izvršni čvorovi predstavljaju strojeve sa ograničenjima pridruživanja poslova. Svaki posao je ograničen na samo jedan stroj, a početak izvođenja mu može ovisiti o nekom drugom zadatku. Rad opisuje tri faze rješavanja problema. U svakoj od faza je predstavljen dio sustava i neke rukom pisane heuristike koje su korištene u rješavanju problema. Rad isto predstavlja neke tehnike strojnog učenja poput genetskog programiranja i neuronskih mreža koje su korištene da bi se proizvele što bolje heuristike.This thesis tackles a specific type of multiple machine scheduling problem. It deals with scheduling tasks on executing nodes through a network of scheduling servers, where the goal is to optimize the makespan. In this problem executing nodes represent the machines with eligibility restrictions. Tasks are machine bound because every task can only be executed at a specific machine and they can also be precedence constrained to other tasks. The thesis describes the tackling of the problem in three phases. Each of those phases presents a part of the system and some hand written heuristics that were used in order to solve the problem. The thesis also presents some machine learning techniques, like genetic programming and neural networks, which were used in order to produce the best possible heuristics

Natural ventilation as a means of airborne tuberculosis infection control in minibus taxis

Airborne infection control measures are used extensively in health-care settings to curtail the spread of airborne infectious diseases. Few such measures are applied in public congregate spaces outside health facilities, such as those associated with public transport. In minibus taxis – a popular form of public transport in South Africa – poor ventilation creates conditions that allow for transmission of airborne diseases, particularly tuberculosis. In this study, we focused on developing quantitative ventilation profiles for the 16-seater Toyota Quantum Ses’fikile model commonly used in the Cape Town metropole. We studied the ventilation rates achievable in an occupied taxi under varying operational conditions, such as driving speed and open window configurations, which were based on observations made during preliminary taxi journeys. Two open-window configurations were found to provide ventilation rates close to or exceeding WHO recommended per-person requirements for high-risk clinical areas and are therefore likely to be effective in reducing the risk of tuberculosis transmission. Significance: The results obtained augment the limited data available on the role that natural ventilation can play in reducing TB transmission in minibus taxis. Ventilation rates were shown to depend on both the taxi speed and specific open window configuration, countering the notion that simply opening a random selection of windows provides adequate reduction in the transmission risk

Innovative Methods for the Benefit of Public Health Using Space Technologies for Disaster Response

Space applications have evolved to play a significant role in disaster relief by providing services including remote sensing imagery for mitigation and disaster damage assessments; satellite communication to provide access to medical services; positioning, navigation, and timing services; and data sharing. Common issues identified in past disaster response and relief efforts include lack of communication, delayed ordering of actions (eg, evacuations), and low levels of preparedness by authorities during and after disasters. We briefly summarize the Space for Health (S4H) Team Project, which was prepared during the Space Studies Program 2014 within the International Space University. The S4H Project aimed to improve the way space assets and experiences are used in support of public health during disaster relief efforts. We recommend an integrated solution based on nano-satellites or a balloon communication system, mobile self-contained relief units, portable medical scanning devices, and micro-unmanned vehicles that could revolutionize disaster relief and disrupt different markets. The recommended new system of coordination and communication using space assets to support public health during disaster relief efforts is feasible. Nevertheless, further actions should be taken by governments and organizations in collaboration with the private sector to design, test, and implement this system