This study focuses on the habitats of cetaceans in the Mid-Atlantic Bight, a region characterized by bathymetric diversity and the presence of distinct water masses (i.e. the shelf water, slope water, and Gulf Stream). The combination of these features contributes to the hydrographic complexity of the area, which furthermore influences biological productivity and potential prey available for cetaceans. The collection of cetacean sighting data together with physical oceanographic data can be used to examine cetacean habitat associations. Cetacean habitat modeling is a mechanism for predicting cetacean distribution patterns based on environmental variables such as bathymetric and physical properties, and for exploring the potential ecological implications that contribute to cetacean spatial distributions. We can advance conservation efforts of cetacean populations by expanding our knowledge of their habitats and distribution. Generalized additive models (GAMs) were developed to predict the spatial distribution patterns of sperm whales (Physeter macrocephalus), pilot whales (Globicephala spp.), bottlenose dolphins (Tursiops truncatus), and Atlantic spotted dolphins (Stenella frontalis) based on significant physical parameters along the continental shelf-break region in the Mid-Atlantic Bight. Data implemented in the GAMs were collected in the summer of 2006 aboard the NOAA R/V Gordon Gunter. These included visual cetacean survey data collected along with physical data at depth via expendable bathythermograph (XBT), and conductivity-temperature-depth (CTD) instrumentation. Additionally, continual surface data were collected via the ship’s flow through sensor system. Interpolations of physical data were created from collected point data using the inverse distant weighted method (IDW) to estimate the spatial distribution of physical data within the area of interest. Interpolated physical data, as well as bathymetric (bottom depth and slope) data were extracted to overlaid cetacean sightings, so that each sighting had an associated value for nine potentially significant physical habitat parameters. A grid containing 5x5 km grid cells was created over the study area and cetacean sightings along with the values for each associated habitat parameter were summarized in each grid cell. Redundant parameters were reduced, resulting in a full model containing temperature at 50 m depth, mixed layer depth, bottom depth, slope, surface temperature, and surface salinity. GAMs were fit for each species based on these six potentially significant parameters. The resultant fit models for each species predicted the number of individuals per km2 based on a unique combination of environmental parameters. Spatial prediction grids were created based on the significant habitat parameters for each species to illustrate the GAM outputs and to indicate predicted regions of high density. Predictions were consistent with observed sightings. Sperm whale distribution was predicted by a combination of depth, sea surface temperature, and sea surface salinity. The model for pilot whales included bottom slope, and temperature at 50 m depth. It also indicated that mixed layer depth, bottom depth and surface salinity contributed to group size. Similarly, temperature at 50 m depth was significant for Atlantic spotted dolphins. Predicted bottlenose dolphin distribution was determined by a combination of bottom slope, surface salinity, and temperature at 50 m depth, with mixed layer depth contributing to group size. Distribution is most likely a sign of prey availability and ecological implications can be drawn from the habitat parameters associated with each species. For example, regions of high slope can indicate zones of upwelling, enhanced vertical mixing and prey availability throughout the water column. Furthermore, surface temperature and salinity can be indicative of patchy zones of productivity where potential prey aggregations occur. The benefits of these models is that collected point data can be used to expand our knowledge of potential cetacean “hotspots” based on associations with physical parameters. Data collection for abundance estimates, higher resolution studies, and future habitat surveys can be adjusted based on these model predictions. Furthermore, predictive habitat models can be used to establish Marine Protected Areas with boundaries that adapt to dynamic oceanographic features reflecting potential cetacean mobility. This can be valuable for the advancement of cetacean conservation efforts and to limit potential vessel and fisheries interactions with cetaceans, which may pose a threat to the sustainability of cetacean populations.

The objective of this project is to develop an operational scheduling model for Sasol Mining’s coal handling facility, Sasol Coal Supply (referred to as SCS), to optimise daily operations. In this document, the specific scheduling problem at SCS is presented and solved using Mixed Integer Non-Linear Programming (MINLP) continuous time representation techniques. The most recent MINLP scheduling techniques are presented and applied to an example problem. The assumption is made that the results from the example problem will display trends which will apply to the SCS scheduling problem as well. Based on this assumption, the unit-specific event based continuous time formulation is chosen to apply to the SCS scheduling problem. The detail mathematical formulation of the SCS scheduling problem, based on the chosen technique, is discussed and the necessary changes presented to customise the formulation for the SCS situation. The results presented show that the first phase model does not solve within 72 hours. A solution time of more than three days is not acceptable for an operational scheduling model in a dynamic system like SCS. Various improvement approaches are applied during the second phase of the model development. Special Ordered Sets of Type 1 (SOS1) variables are successfully applied in the model to reduce the amount of binary variables. The time and duration constraints are restructured to simplify the structure of the model. A specific linearization and solution technique is applied to the non-linear equations to ensure reduced model solution times and reliable results. The improved model for one period solves to optimality within two minutes. This dramatic improvement ensures that the model will be used operationally at SCS to optimise daily operations. The scheduling model is currently being implemented at SCS. Examples of the input variables and output results are presented. It is concluded that the unit-specific event based MINLP continuous time formulation method, as presented in the literature, is not robust enough to be applied to an operational industrial-sized scheduling problem such as the SCS problem. Customised modifications to the formulation are necessary to ensure that the model solves in a time acceptable for operational use. However, it is proved that Mixed Integer Non-linear Programming (MINLP) can successfully be applied to optimise the scheduling of an industrial-sized plant such as SCS. Although more research is required to derive robust formulation techniques, the principle of using mathematical methods to optimise operational scheduling in industry can dramatically impact the way plants are operated. The optimisation of daily schedules at SCS by applying the MINLP continuous time scheduling technique, has made a significant contribution to the coal handling industry. Finally, it can be concluded that the SCS scheduling problem was successfully modelled and the operational scheduling model will add significant value to the Sasol Group. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2006. / Industrial and Systems Engineering / unrestricted

This diploma thesis deals with optimization models with design of a new waste management infrastructure in the Czech Republic, such that combustible waste, which is not utilized by the material recovering, can be used by energy recovering. This task is handled by optimization models, including trac and mixing problems. First of all, the concepts of graph theory and optimization are presented in this paper. Subsequently, some of the GAMS functions are discussed, and later the VBA programming language used to handle the larger data quickly is presented. In the main part, three gradually expanding models are developed. At the end the data from the waste management information system are implemented into them.

The aim of this diploma thesis is to design and efficiently implement a framework to support optimization modelling. The emphasis is placed on two-stage stochastic optimization problems and performing calculations on large data. The computing core uses the GAMS system and with using its application interface and Python programming language, the user will be able to efficiently acquire and process input and output data. The separation of the data logic and the application logic then offers a wide range of options for testing and experimenting with a general model on dynamically changing input data. The thesis is also focused on an evaluation of the framework complexity. The framework performance was evaluated by measuring the time required to complete the required task for various use cases, on the increasing sample size of input data.

The diploma thesis deals with the modeling of selected risks in healthcare. Motivated by the current pandemic situation, it focuses on analysis of risks associated with the vaccination center in Brno. The theoretical part is mainly devoted to the issue of risk management with a focus on risks in healthcare, where the methods that are used in the practical part are defined. Furthermore, the thesis presents selected topics of mathematical programming. Especially, the newsvendor problem is introduced as inspiring case for further modelling. The brief description of the covid-19 pandemic situation later serves as one of the data sources. The practical part deals with the description and risk analysis of the vaccination process using the methods "What If?" and the FMEA method. Appropriate decisions are then proposed for selected risk situations using the GAMS optimization system. Based on the results of the calculations, specific recommendations are proposed.

The idea of this diploma thesis is an origin application of optimization models to solve a wage funds allocation problem on various institutes of each faculty. This diploma thesis includes an outline of linear programming models, nonlinear programming models, multiply programming models and parametric programming models. Studied questions are debating in wider context of distributing financial resources from the Budget of the Czech republic, through Ministry of education, youth and sports, universities, faculties after as much as various institutes. The accent is given on question of definition assessment scales of achievement criteria with general-purpose kvantification.

In the aftermath of the liberalization of European Energy Markets in the 2000s, Power Exchange platforms have constantly evolved towards more integrated and competitive designs, where quality forecasts and effective optimization strategies play decisive roles. This study presents the development of a hydropower scheduling optimization algorithm for the Day-Ahead spot market using Mixed Integer Linear Programming (MILP). This work was supported by the hydro asset management team of ENGIE Global Energy Markets (GEM) located in Brussels.  The model developed is focusing on the optimization of Coindre Hydraulic Power Plant (HPP), located in the highlands of Massif Central in France. With the combined water discharge of its two interconnected reservoirs, Grande-Rhue and Petite-Rhue, the powerhouse can reach up to 36 MW of power output capacity. The two reservoirs are located kilometres apart from each other and have different storage capacities and catchment areas. The reservoirs naturally exchange water due to the level difference along an interconnection pipe. Maximum power output is limited by water level differences in both reservoirs, which makes modelling complicated. These operational constraints are a limiting factor in terms of operability, as a result the scheduling process is a non-trivial task and is time-consuming.  A framing study of the power plant was conducted over a hydraulic year to identify the governing parameters of the model. The multi-reservoir nature of the optimization problem oriented the model development towards a Mixed Integer Linear Formulation. After experimenting with different solvers, Gurobi 28.1.0 was chosen for its performance in the Branch and Cut Algorithm for the power scheduling task.  The performance of the new model has been validated by re-running the model on past production plans, results show that reservoir volume errors are less than 5% of their respective capacities on a 5 days’ time-horizon. After backtesting it was found that the new optimization strategy results in higher revenue for the plant due to the optimized operation at higher average energy prices. The results also bring out the importance of proper valve actuation in the optimization strategy, as well as the need for future studies. / Till följd av liberaliseringen av de europeiska energimarknaderna under 2000-talet har energiföretagen och elbörserna ständigt utvecklats mot mer integrerade och konkurrenskraftiga lösningar, där kvalitetsprognoser och effektiva optimeringsstrategier spelar avgörande roller. Detta examensarbete presenterar utvecklingen av en algoritm för optimering av vattenkraftplaneringen på Day-Ahead elmarknaden med hjälp av en matematisk modell av typen Mixed Integer Linear Programming (MILP). Arbetet initierades av och utfördes hos ENGIE Global Energy Markets (GEM) i Bryssel.  Modellen som utvecklats är tänkt att optimera Coindre vattenkraftverk, som ligger på höglandet inom Massif Central i Frankrike. Med det kombinerade vattenutsläppet från dess två fördämningar, Grande-Rhue och Petite-Rhue, kan kraftverket leverera upp till 36 MW el netto till elnätet. Vattenreservoarerna ligger flertalet kilometer ifrån varandra och har mycket olika kapacitet och upptagningsområden. Båda reservoarerna är kopplade till varandra genom det gemensamma tilloppsröret till kraftverket, där en reglerventil finns endast vid Petite-Rhue. Vatten kan växlas naturligt mellan de två dammarna när ventilen är öppen på grund av skillnaden i varderas vattennivå. Den maximala effekten från kraftverket är begränsad av vattennivåerna i båda reservoarerna vilket gör optimeringsmodelleringen komplicerad. Dessa operationella begränsningar är mycket hindrande vad gäller valet av driftsregim, eftersom kalkylering av driftsplaneringen blir en svår och tidskrävande uppgift.  En ramstudie av vattenkraftverket genomfördes under ett typiskt hydrauliskt år för att identifiera modellens styrparametrar. Den möjliga vattenöverföringen mellan de två dammarna orienterade modellutvecklingen mot en Mixed Integer Linear Programming (MILP) formulering. Efter att ha experimenterat med olika kalkylverktyg valdes Gurobi 28.1.0 för sin bra prestation i lösningen av Branch and Cut-algoritmen.  Systemets hydraulik har validerats genom att injicera realiserade produktionsplaner som input till modellen. Resultaten visar att volymfelet är mindre än 5% av deras respektive kapacitet under en 5-dagars tidshorisont. Efter tvärstester mot historiska data konstaterades det att den nya optimeringsstrategin resulterar i bättre genomsnittliga elpriser på varje kWh inmatad till nätet och högre intäkter för kraftverket. Resultaten visar också på vikten av korrekt ventilmanövrering i optimeringsstrategin.  Modellen körs i rimliga beräkningstider och redan används i den dagliga optimeringen av Coindre kraftverket, vilket sparar mycket tid. Specifika exempel på den optimerade prestandan och framtida förbättringar hittas i slutet av denna rapport.

The diploma thesis deals with mathematical modeling of the resource allocation problem in an energy system with respect to technical parameters of the used resources. The model includes random input variables affecting the amount of demand and constraints related to associated risks. The thesis addresses control of the operation of various types of boilers and also extends the system with a heat storage tank examining its impact on the behavior of the system and achieved results. The optimization model is based on a multi-period two-stage scenario model of stochastic programming and works with simulated data, which combines real data, statistically determined estimates, and the use of logistic regression. The implementation utilizes GAMS software. When comparing the achieved results with the current state, it was found that the heat storage tank has a positive effect on the function of the system as it allows for extended usage of the cheaper unregulated sources by storing surplus heat, and thus helps to reduce the overall costs of the system.

The diploma thesis deals with the possibilities of using the optimization modelling software system GAMS in risk management. According to the assignment, emphasis is placed on a detailed approach to the program for those, who are interested in its use in the field of risk engineering applications. The first part of the thesis contains the knowledge to understand what the GAMS program is and what it is used for. The next part of the work provides instructions on how to download, install, activate the program and what the user interface of the program looks like. Thanks to mathematical programming, it will be explained on a project on the distribution of lung ventilators, what basic approaches may be used in risk modelling in the GAMS program on a deterministic model. The following are more complex wait-and-see models, which contains the probability parameters and here-and-now models, where we work with demand scenarios and verify whether if they meets the requirements of other scenarios or calculate costs for the highest demands. The two-stage model is also one of the here-and-now models, but it is significantly more complex in its size and range of input data, it includes additional price parameters for added or removed pieces of lung ventilators from the order.

The thesis is focused on the optimization of models of transportation and transshipment problem with random demand, additional edges, and dynamic pricing. The theoretical part of the thesis introduces mathematical models of transportation. The software GAMS, which is used for the solution, is all so described. The practical part is a split among chapters and implements the described models by using real data.