Search result: Catalogue data in Autumn Semester 2024
Spatial Development and Infrastructure Systems Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Master Studies (Programme Regulations 2009) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Major in Transport Systems and Behaviour | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-0445-00L | Production and Operations Management | W | 3 credits | 2G | T. Netland | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This core course provides insights into the basic theories, principles, concepts, and techniques used to design, analyze, and improve the operational capabilities of an organization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course provides students with a broad theoretical basis for understanding, designing, analyzing, and improving manufacturing operations. After completing this course: 1. Students can apply key concepts of POM to detail an operations strategy. 2. Students can do simple forecasting of demand and plan the needed capacity to meet it. 3. Students can conduct process mapping analysis, use it to design and improve processes and layouts, and elaborate on the limitations of the chosen method. 4. Students can choose IT, OT, and automation technology for manufacturing applications. 5. Students can design information flows, manage master data, and use it to plan and control a factory. 6. Students can design material flows in and beyond factories. 7. Students can design performance management systems. 8. Students can select and use problem-solving tools to improve quality and productivity. 9. Additional skills: Students acquire experience in teamwork. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course covers the most fundamental strategic and tactical concepts in production and operations management (POM). Production and Operations Management (POM) is at the heart of any business. It is concerned with the business processes that transform input into output and deliver products and services to customers. Factory management is an important part of POM, but it is much more than what takes place inside the production facilities of companies like ABB, Boeing, BMW, LEGO, Nestlé, Roche, TESLA, and Toyota. Did you know that the largest portion of assets and employees in most organizations are engaged in the operations function? Although this course focuses on manufacturing, all types of organizations depend on their operational capabilities. With the ongoing globalization and digitization of manufacturing, POM has won a deserved status for providing a competitive advantage. This course covers the following topics: Introduction to POM, Manufacturing strategy, Forecasting and capacity, Process design, Layout, Industry 4.0, Information flow, Material flow, Logistics/SCM Performance management, Performance improvement, Quality management, and Maintenance. This course is administered via Moodle. The course is designed around five elements: 1. Textbook. Baudin and Netland (2022) Introduction to Manufacturing: An Industrial Engineering and Management Perspective, 1st Ed. Routledge. 2. Video lectures. Short video lectures presenting basic POM concepts. 3. Class lectures. Deep-dives with case examples on select topics. 4. FactoryVR group assignment. FactoryVR allows students to visit factories virtually. 5. Quizzes. A few quizzes during the semester help students check their progress and prepare for the written exam. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Suggested literature is provided in the syllabus. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0491-00L | Agent Based Modeling in Transportation | W | 6 credits | 4G | M. Balac, G. O. Kagho | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides foundational programming, data science, and software engineering knowledge using the Python. Through a combination of lectures, hands-on exercises, and real-world case studies related to transportation data science, participants develop practical skills and knowledge for developing simple programs to analyze datasets, implement algorithms, conduct simulations, and more. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Acquire the ability to develop software with Python. Familiarity with software engineering techniques. Ability to conduct data science analyses with insights for civil engineering relevant topics, such as transportation and GIS. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course will combine lectures and hands-on exercises in a seminar room. Through multiple case studies, students will learn to apply programming and software engineering methods through various practical case studies. The students will also be given prepared tutorials, cheat sheets, code repository templates, and datasets. The data will be provided to the students within the course. Main topics covered: 1. Foundations of Programming with Python 2. Working with Tables to Process Data 3. Data Science with Python (e.g., regression models, machine learning) 4. Visualization of Data and Results with MatplotLib 5. Theory, Programming Concepts & Best Practices for Clean Coding 6. Applications in Transport 5. Hands-on case studies | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides and related material (software codes) will be made available in digital form (Moodle, Website & GitHub Repository). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Priority: no prior knowledge required Programming skills: no skills required | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 101-0469-00L | Road Safety | W | 6 credits | 4G | M. Deublein, P. Eberling | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The collection and the methods of statistical and geographical analysis of road accidents are important fundamentals of this course. Safety Aspects in design of urban roads are discussed and measures for improving the safety situation are presented. Procedures of infrastructure safety management for administrations and police are another topic. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Imparting knowledge base about road safety and the event of accident, presenting possibilities to increase road safety | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Accident origin, collection of road accidents, statistical (descriptive and multivariate, accident prediction models) and geographical analysis of road accidents, risk analysis and rehabilitation measures, road safety instruments for infrastructure with focus on road safety audit, Swiss and international transport policy | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Basic literature: message Via sicura; Directive 2008/96/EC on road infrastructure safety management; ELVIK, R.; VAA, T. (2004). The Handbook of Road Safety Measures. Oxford: ELSEVIER Ltd.; EU-Projekt RiPCORD-iSEREST (http://ripcord.bast.de/) Further literature: will be presented during the course | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0492-00L | Microscopic Modelling and Simulation of Traffic Operations | W | 3 credits | 2G | K. Riehl | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course introduces basics of microscopic modelling and simulation of traffic operations, including model design and development, calibration, validation, data analysis, identification of strategies for improving traffic flow performance, and evaluation of such strategies. The aim is to provide the fundamentals for building a realistic traffic-engineering project from beginning to end. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of this course is to conduct a realistic traffic engineering project from beginning to end. The students will first familiarize themselves with microscopic traffic models. Students will work in groups on a project that includes a base scenario on a real traffic network. Throughout the semester, along with theoretical concepts, the students will build the base scenario (design, calibration and validation) and will develop alternative scenarios regarding modification on the infrastructure, simulation of in-vehicle technologies and vehicle-to-everything (V2X) communication. Simulations will be implemented in Aimsun software. The students will be asked to understand, analyze, interpret and present traffic properties. Evaluation of alternative scenarios over the same network will be performed. Finally, students will be asked to design, implement, analyze and present a novel proposal, which will be compared with the base scenario. Upon completion of the course, the students will: • Understand the basic models used in microsimulation software (car-following, lane changing, gap acceptance, give ways, on/off-ramps, etc.). • Design a road transport network inside the simulation software. • Understand the basics behind modeling traffic demand and supply, vehicle dynamics, performance indicators for evaluation and network design for a realistic road transport network. • Understand how to design a complete study, implement and validate it for planning purposes, e.g. creating a new road infrastructure. • Make valid and concrete engineering proposals based on the simulation model and alternative scenarios. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | In this course, the students will first learn some microscopic modelling and simulation concepts, and then complete a traffic-engineering project with microscopic traffic simulator Aimsun. Microscopic modelling and simulation concepts will include: 1) Car following models 2) Lane change models 3) Calibration and validation methodology Specific tasks for the project will include: 1) Building a model with the simulator Aimsun in order to replicate and analyze the traffic conditions measured/observed. 2) Calibrating and validating the simulation model. 3) Redesigning/extending the model to improve the traffic performance through Aimsun and with/without programming in Python or C++. The course will be based on a project that each group of students will build (design, calibrate, analyze and presentation) across the semester. A mid-term and final presentation of the work will be asked from each group of students. It consists of weekly 2-hour lectures. The students work in pairs on a group project that completes in the end of the semester. The modelling software used is Aimsun and lectures (theory and hands on experience) are taking place in a computer room. The course Road Transport Systems (Verkehr III), or simultaneously taking the course Traffic Engineering is encouraged. Previous experience with Aimsun/Python/C++ is helpful but not mandatory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The lecture notes and additional handouts will be provided before the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Additional literature recommendations will be provided at the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students need to know some basic road transport concepts. The course Road Transport Systems (Verkehr III), or simultaneously taking the course Traffic Engineering is encouraged. Previous experience with Aimsun is helpful but not mandatory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 401-0647-00L | Introduction to Mathematical Optimization | W | 5 credits | 2V + 1U | D. Adjiashvili | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Information about relevant literature will be given in the lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This course is meant for students who did not already attend the course "Linear & Combinatorial Optimization", which is a more advance lecture covering similar topics. Compared to "Linear & Combinatorial Optimization", this course has a stronger focus on modeling and applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0491-10L | Basics of Java and Best Practices for Scientific Computing | W | 1 credit | 1U | M. Balac | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides an introduction to programming in Java, version control, and cloud computing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of the course, the students should ● Have acquired object-oriented programming skills with a focus on Java. ● Have an understanding of version control using git ● Have learned to deploy java applications on servers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course provides an introduction to object-oriented programming with Java. Four topics are covered: ● Basics of Java (objects, classes, interfaces, abstract classes, static classes, static methods,...) ● Injection (traditional vs. Guice) ● Code versioning ● Java application deployment on servers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Intro to Java Programming, Comprehensive Version (10th Edition) by Y. Daniel Liang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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