. "Modelling and optimization for analysis of big data"@en . . "5" . "Main target of the course is on providing precise and \r\npowerful tools strongly required in the study of optimization models. Namely, a solid \r\nintroduction to multidimensional calculus of variations and multidimensional control theory \r\nwill be given, coupled with elementary modern linear geometry, basics of functional analysis, \r\nbasics of discrete harmonic analysis and basics of discrete differential geometry needed in the \r\nmodern Big Data Analysis. As an application, many optimization problems are illustrated by \r\nexamples arising from Big Data science, like CCA, generalized CCA, Kernel and Nonlinear \r\nCCA.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Trajectory optimization"@en . . "4.5" . "Availabe: General Module (Space Flight Theory) Description\n•Trajectory computation and space flight analysis\r\n•Basic principles for designing and analysing a space mission\r\n\nOutcome: General Module (Space Flight Theory) Outcomes\nStudents have knowledge/responsibilities in:\r\n•Space mission analysis and design (tools)\r\n•Orbital and attitude dynamics\r\n•Modeling approaches of space environment\n•Satellite system modeling (thermal, sensors, actuators)\r\n•Definitions and technical terms of space applications and optimization\r\n•Mathematical models and problem statements relating to space applications\r\n•Using mathematical software\r\n•Numerical solution of mathematical problems" . . "Presential"@en . "TRUE" . . "Practical optimisation"@en . . "5" . "Aims\r\n\r\nThe aims of the course are to:\r\n\r\nTeach some of the basic optimisation methods used to tackle difficult, real-world optimisation problems.\r\nTeach means of assessing the tractability of nonlinear optimisation problems.\r\nDevelop an appreciation of practical issues associated with the implementation of optimisation methods.\r\nProvide experience in applying such methods on challenging problems and in assessing and comparing the performance of different algorithms.\n\nOutcome:\nAs specific objectives, by the end of the course students should be able to:\r\n\r\nUnderstand the basic mathematics underlying linear and convex optimisation.\r\nBe able to write and benchmark simple algorithms to solve a convex optimisation problem.\r\nUnderstand the technique of Markov-Chain Monte Carlo simulation, and apply it to solve a Travelling Salesman Problem.\r\nUnderstand the ways in which different heuristic and stochastic optimization methods work and the circumstances in which they are likely to perform well or badly.\r\nUnderstand the principles of multi-objective optimization and the benefits of such of approaching real-world optimization problems from a multi-objective perspective.\r\n* This module is shared with 4th Year undergraduates from the Department of Engineering." . . "Presential"@en . "FALSE" . . "Optimization methods to air transport"@en . . "4" . "General motivation: SESAR and NEXTGEN programmes. Static optimization; direct methods; gradient\r\nand Newton-type methods. Nonlinear programming; problem statement and optimality conditions. \r\nLinear programming; airport capacity estimation and optimization. Mixed integer linear \r\nprogramming. Conflict detection and resolution in air traffic management using direct optimization \r\nmethods. Linear dynamic models for air traffic flow management.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Optimization of geodetic networks ."@en . . "4" . "LO: knowledge and understanding of specific\nmethods of geodetic networks\n• knowledge of those processes and\nimplementation in practical work" . . "Presential"@en . "FALSE" . . "Optimization and data fitting"@en . . "5" . "An engineer is often faced with the problem of having to determine optimal values of the parameters in a mathematical model of a physical or technical problem. The problem is eg to find the parameters in a function so that the corresponding curve is a best fit to a given set of data points, or you may be given a mathematical formula that expresses the cost of producing a commodity or perform a transportation job. Here you have to choose values for the free parameters so that the cost is minimized.\r\nThe course deals with efficient methods for computing optimal values for the parameters in a mathematical model. The students will study and use available software libraries and learn how to construct their own programs." . . "Presential"@en . "FALSE" . . "Airline planning and optimisation"@en . . "4.00" . "Course Contents This course provides students with knowledge to analyse planning problems related to airline operations and to develop\nmodeling approaches to solve these problems. The focus is on the relationship between planning models and their operational\nimplications. It starts with a general overview of the airline demand analysis, followed by the study of the planning framework\nwhich airlines operate in. This planning framework includes strategic decisions, namely fleet planning and network\ndevelopment, tactical decisions, such as scheduling and maintenance planning.\nStudy Goals At the end of the course, the students should be able to:\nObj1: identify the main strategic, tactical, and operational problems of an airline;\nObj2: get familiarized with the development and implementation of some modeling techniques:\n(a) mix-integer linear programming,\n(b) multi-commodity flow networks,\n(c) time-space networks,\n(d) Markov decision programming.\nObj3: get familiarized with the development and implementation of some solution techniques:\n(a) branch-and-bound,\n(b) column generation,\n(c) dynamic programming.\nObj4: identify, formulate and solve airline strategic and tactical planning problems:\n(a) airline networks development,\n(b) fleet planning,\n(c) frequency planning,\n(d) aircraft assignment and routing planning,\n(e) crew scheduling, and\n(f) maintenance planning.\nObj5: identify an airline operations problem, analyse and solve it;\nObj6: explain the implications of airline planning decisions and report them in an academic manner" . . "Blended"@en . "TRUE" . . "Operations optimisation"@en . . "4.00" . "Course Contents The course aims at providing the students with knowledge and experience to set-up and analyze linear and nonlinear\noptimization problems.\nThe course covers the following topics:\n1. Introduction to Operations Research, examples of OR models for air transport.\n2. Linear programmming (LP) models and the simplex Method.\n3. Sensitivity analysis and Duality.\n4. Transportation and assignment problems.\n5. Network optimization and dynamic programming.\n6. Mixed Integer Linear programming.\n7. Nonlinear programming.\nStudy Goals At the end of the course, the students will be able to:\n1. Understand the theory behind basic linear and non-linear optimization problems.\n2. Model a problem as a linear program, a (mixed) integer program or a network optimization problem;\n3. Verify the model using self created test data set.\n4. Create and apply a sensitivity analysis." . . "Presential"@en . "TRUE" . . "Propagation and optimisation in astrodynamics"@en . . "5.00" . "no data" . . "Presential"@en . "FALSE" . . "Engineering optimisation: concepts and applications"@en . . "3.00" . "no data" . . "Presential"@en . "FALSE" . . "Numerical optimization of mechanical structures and processes"@en . . "5.00" . "Learning Outcomes\nThe implementation of numerical optimization in mechanical, manufacturing and process systems. Major emphasis is given in the optimization problem formulation using a single or multiple criteria using gradient based methods and non-gradient probabilistic methods.\nGeneral Competences\nApply knowledge in practice\nRetrieve, analyse and synthesise data and information, with the use of necessary technologies\nAdapt to new situations\nMake decisions\nWork autonomously\nWork in teams\nWork in an interdisciplinary team\nGenerate new research ideas\nAppreciate diversity and multiculturality\nDemonstrate social, professional and ethical commitment and sensitivity to gender issues\nBe critical and self-critical\nAdvance free, creative and causative thinking\nCourse Content (Syllabus)\nOptimization problem formulation\nDecision hierarchy, selection of criteria, decision variables (continuous, discrete), mathematical model formulation, constraints, parameters\nApplications (1st Assignment):\nManufacturing: Mechanical system model development\nEnergy: Thermal process model development.\nIndustrial management: Supply chain modellig.\n\nNumerical Optimization (gradient-based)\nUnconstrained and Constrained problems\nLinear and non-linear programming\nLinear and non-linear integer programming\nSolution of optimality conditions, Optimal solution sensitivity\nApplications (2nd Assignment) – Continuous decision variables (3rd Assignment) – Continuous and discrete decision variables\nManufacturing: Mechanical system optimization.\nEnergy: Heat exchanger network optimization.\nIndustrial management: Supply chain optimization.\n\nOptimization using probabilistic methods (non-gradient methods)\nSimulated annealing, genetic algorithms.\nApplications (4th Assignment) – Implementation of probabilistic optimization methods\nManufacturing: Mechanical system optimization.\nEnergy: Heat exchanger network optimization.\nIndustrial management: Supply chain optimization.\n\nMulti-objective optimization\nPareto front. Numerical optimization of multi-objective optimization problems.\nApplications (5th Assignment) – Implementation of multi-objective optimization methods\nManufacturing: Mechanical system optimization.\nEnergy: Heat exchanger network optimization.\nIndustrial management: Supply chain optimization.\n\nOptimization under uncertainty\nUncertainty characterization – Problem formulation and solution\nApplications (6th Assignment) – Implementation of optimization methods under uncertainty.\nManufacturing: Mechanical system optimization.\nEnergy: Heat exchanger network optimization.\nIndustrial management: Supply chain optimization.\n\nOptimization of dynamic problems\nTime discretization. Decision vector parameterization. Numerical solution (direct methods, sequential method, multiple shooting)\nApplications (4th Assignment) – Implementation of dynamic optimization methods.\nManufacturing: Mechanical system optimization.\nEnergy: Heat exchanger network optimization.\nIndustrial management: Supply chain optimization." . . "Presential"@en . "TRUE" . . "Optimization"@en . . "6.0" . "https://sigarra.up.pt/fcup/en/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=502765" . . "Presential"@en . "FALSE" . . "Mathematical optimization"@en . . "6.0" . "Optimization (or “Optimisation”) is the subject of finding the best or optimal solution to a problem from a set of potential or feasible solutions.\nOptimization problems are fundamental in all forms of decision-making, since one wishes to make the best decision in any context, and in the analysis of data, where one wishes to find the best model describing experimental data. This course treats two different areas of optimization: nonlinear optimization and combinatorial optimization. Nonlinear optimization deals with the situation that there is a\ncontinuum of available solutions. A best solution is then usually approximated with one of several available general-purpose algorithms, such as Brent’s method for one-dimensional problems, Newton, quasi-Newton and conjugate gradient methods for unconstrained problems, and Lagrangian methods, including active-set methods, sequential quadratic programming and interior-point methods for general constrained problems. Combinatorial optimization deals with situations that a best solution from a finite number of available solutions must be chosen. A variety of techniques, such as linear programming, branch and cut, Lagrange relaxation dynamic programming and approximation algorithms are employed to tackle this type of problems. Throughout the course, we aim to provide a coherent framework for the subject, with a focus on consideration of optimality conditions (notably the Karush-Kuhn-Tucker conditions), Lagrange multipliers and duality, relaxation and approximate problems, and on convergence rates and computational complexity.\nThe methods will be illustrated by in-class computer demonstrations, exercises illustrating the main concepts and algorithms, and modelling and computational work on case studies of practical interest, such as optimal control and network flow.\n\nPrerequisites\nDesired Prior Knowledge: Simplex algorithm. Calculus, Linear Algebra.\n\nRecommended reading\n1. Nonlinear Programming, Theory and Algorithms, by Bazaraa, Sherali, and Shetty (Wiley). 2. Combinatorial Optimization, Algorithm and Complexity, by Papadimitriou and Steiglitz (Dover Publications).\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/464091/mathematical-optimization" . . "Presential"@en . "FALSE" . . "Mathematical Optimisation"@en . . . . . . . . . . . . . .