Thesis skills: project management, writing skills and presentation (VON001F)

Introduction to the scientific method. Ethics of science and within the university community.
The role of the student, advisors and external examiner. Effective and honest communications.
Conducting a literature review, using bibliographic databases and reference handling. Thesis structure, formulating research questions, writing and argumentation. How scientific writing differs from general purpose writing. Writing a MS study plan and proposal. Practical skills for presenting tables and figures, layout, fonts and colors. Presentation skills. Project management for a thesis, how to divide a large project into smaller tasks, setting a work plan and following a timeline. Life after graduate school and being employable.

Final project (IÐN441L)

• The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
• The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
• The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
• Final project exam is divided into two parts: Oral examination and open lecture
• Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
• The student delivers a thesis and a project poster.
• According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
• All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
• According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.

Learning Outcomes:

Upon completion of an MS thesis, the student should be able to:

• Formulate engineering design project  / research questions
• Use an appropriate theoretical framework to shed light on his / her topic
• Analyze and solve engineering tasks in a specialized field.
• Perform a literature search and a thorough review of the literature.
• Demonstrate initiative and independent creative thinking.
• Use economic methodology to answer a specific research question
• Competently discuss the current knowledge within the field and contribute to it with own research
• Work with results, analyze uncertainties and limitations and interpret results.
• Assess the scope of a research project and plan the work accordingly
• Effectively display results and provide logical reasoning and relate results to the state of knowledge.

Operations Research 2 (IÐN508M)

This course will introduce the student to decision and optimization models in operations research. On completing the course the student will be able to formulate, analyze, and solve mathematical models, which represent real-world problems, and critically interpret their results. The course will cover integer programming and modeling with stochastic programming. The student will become familiar with building mathematical models using Python.

Design & Innovation of Production systems (IÐN509M)

The aim of the course is to prepare students for participation in product development and design of production systems. The product development process and its individual components will be covered, with special emphasis on productivity and design of production systems (flow, outsourcing, material flow and storage). Emphasis is placed on the use of process and system analyzes and calculation models in decision-making.

Logistics & Environmental Engineering (IÐN510M)

The course focuses on the principles of logistics and supply management and gives a broad introduction to the field. The course is divided into three topics primarily. It covers purchase operations of services and inventory management. This part is followed by looking into transportation and distribution management. Finally, the environmental impacts of logistics is studies and all the three parts put together into a view of sustainability. The course consists of lectures, exercises, game (the Beer Game) and a management simulation game to give hands on experience on logistics management,

Innovation and venture development (IÐN121F)

In this course, students work on developing startup projects from idea to product. The course emphasises hands-on work on real-world challenges, where students apply user-centred methods, work with business models, and conduct various analyses and planning activities. The course is largely workshop-based and encourages collaboration among students from different academic backgrounds. Emphasis is placed on the critical analysis of opportunities for innovation and entrepreneurship, creative problem solving, and the development of ideas based on user needs. Students gain competence in methods for testing and communicating ideas and solutions, and develop an understanding of the practical and societal impacts of innovation in a broad context. The course is suitable for students who wish to strengthen their entrepreneurial mindset and learn to apply innovation methods, whether within organisations, established companies, or as independent entrepreneurs.

Project in System thinking (IÐN127F)

The purpose of the course is to give students an oportunity to practice solveing projects in cooperation with industry

Project in Technology Innovation Management (IÐN132F)

The purpose of the course is to give students an oportunity to practice solveing projects in cooperation with industry

Project in Engineering Management (IÐN133F)

The purpose of the course is to give students an oportunity to practice solveing projects in cooperation with industry

Quality Management (IÐN101M)

Organization and management systems. The systems approach. Quality management, quality concepts. Historical development of quality management. Quality cost. Quality in manufacturing. x, R, p, c and cusum-chart. Statistical quality control. Tests of hypotheses. Acceptance sampling - OC curves. Inspection planning. Quality systems and quality assurance. Quality handbook and organizing for quality. ISO 9001. Total Quality Management, improvement step by step, motivations theories. Quality tools. Practical assignment: Designing a quality system for a company.

Computational Intelligence (IÐN102M)

In designing intelligent systems, there is a need for autonomous systems that can learn to make good decisions from their own experience. This course introduces students to reinforcement learning—a computational approach in which systems automatically improve their performance through interaction and reward, without explicit instruction. The course covers Markov decision processes, dynamic programming, Monte Carlo methods, policy optimization, planning, search methods, and deep neural networks as function approximators. The curriculum combines theoretical discussion and project work, giving students the opportunity to model, analyze, and solve mathematical models for decision-making problems. Emphasis is also placed on the iterative process and on critical reflection regarding what it means for algorithms to learn and the impact that autonomous computational intelligence might have on science and society.

Human Factors in Engineering Design (IÐN128F)

This course provides an overview of Human Factors Engineering (HFE).  The goal of HFE is to design a product, process, system, or environment that minimizes human error and increases productivity while considering the safety, health, and comfort of the human users and operators. To achieve this, HFE makes use of both psychological and physiological principles to determine human characteristics (capabilities, limitations, and behavior) in relation to the design. Topics to be covered include usability testing, psychophysical testing, psychoacoustics, anthropometric measurements, and Physical-, Cognitive and Organizational Ergonomics.

Spatial Sound Engineering (IÐN129F)

This course provides an overview of virtual acoustics (also known as 3D sound, 3D audio, binaural audio, or spatialized sound). The course pulls from many subdisciplines of acoustics including psychoacoustics, physical acoustics, signal processing, active acoustic control, architectural acoustics, audio engineering, and computational acoustics.

Topics to be covered include Head related transfer functions (HRTFs); elements of psychoacoustics for 3D sound; the "stereo dipole"; auralization (including reverberation effects); virtual acoustic systems; cross talk cancellation; ambisonics; wave field synthesis; multi-channel audio; 3D modelling and applications.

Sustainable Supply chain management (IÐN130F)

The course emphasizes on acquire knowledge in sustainable supply chain management. Different aspects of logistics will be covered from the perspective of sustainability, from the selection of suppliers, procurement, inventory management, transport and distribution as well as the impact of the circular economy on sustainability, etc. Emphasis is placed on the building deeper knowledge in a specific field of logistcs by choosing a project that is worked on and is determined by the students' interest, which is made possible to work on group projects related to the choosen field. The course is based on lectures, seminars and project work where work is done on realistic projects, often directly related to companies and their activities. Assessment is based on continuous assessment and assignment submission.

Supply chain strategies (IÐN131F)

The  aim of the course is to give participants comprehensive knowledge on the strategic management of operations and supply chains in different types of industrial  firms. In the course a real case is used to analyse the effects of different strategies on the logistics operations. Different tools are used to analyse the situation and future supply chain including the SCOR model,  SWOT analysis, value streams, process analysis, etc. This will be done a s group work and includes number of seminars and assignments.After passing the course successfully, the student shall be able to analyze industrial systems as well as design and manage supply chains. Assessment is based on continuous assessment, assignment submissions and seminar outcomes.

Design Optimization (VÉL113F)

Optimum design concepts. Fundamentals of linear and nonlinear programming, constrained and unconstrained optimum design problems. Simulated annealing and genetic algorithms. Project and applications to realistic engineering design problems.

Final project (IÐN441L)

• The topic of the Master's thesis must be chosen under the guidance of the supervisor and the Faculty Coordinator of the student. The thesis represents 30 credits. All Master's student have been assigned to a Faculty Coordinator from the beginning of their studies, who advises the student regarding the organization of the program. If a student does not have a supervisor for the final project, he / she must turn to the Faculty Coordinator for assistance.
• The choice of topic is primarily the responsibility of the student in collaboration with his or her project supervisor. The topic of the project should fall within the student's area of study, i.e. course of study and chosen specialisation.
• The master’s student writes a thesis according to the School’s template and defends it in a master’s defense.
• Final project exam is divided into two parts: Oral examination and open lecture
• Present at the oral exam is the student, supervisor, examiner and members of the Master's committee. The student presents a brief introduction on his / her project. It is important that the objectives and research question(s) are clearly stated, and that main findings and lessons to be drawn from the project are discussed.
• The student delivers a thesis and a project poster.
• According to the rules of the Master's program, all students who intend to graduate from the School of Engineering and Natural Sciences need to give a public lecture on their final project.
• All students graduating from the University of Iceland shall submit an electronic copy of their final Master's thesis to Skemman.is. Skemman is the digital repository for all Icelandic universities and is maintained by the National and University Library.
• According to regulations of University of Iceland all MS thesis should have open access after they have been submitted to Skemman.

Learning Outcomes:

Upon completion of an MS thesis, the student should be able to:

• Formulate engineering design project  / research questions
• Use an appropriate theoretical framework to shed light on his / her topic
• Analyze and solve engineering tasks in a specialized field.
• Perform a literature search and a thorough review of the literature.
• Demonstrate initiative and independent creative thinking.
• Use economic methodology to answer a specific research question
• Competently discuss the current knowledge within the field and contribute to it with own research
• Work with results, analyze uncertainties and limitations and interpret results.
• Assess the scope of a research project and plan the work accordingly
• Effectively display results and provide logical reasoning and relate results to the state of knowledge.

Planning (IÐN611M)

The purpose of this course is to provide students with an understanding of operations planning in both manufacturing and service contexts. Students will gain proficiency in the theoretical foundations and practical methods used in developing operational plans. The manufacturing component covers demand forecasting models, how forecasts drive product and component planning, the allocation of resources through scheduling and sequencing, and the use of operational plans to balance variability. The service component addresses service levels, the use of operational planning in service delivery, shift scheduling, and related topics.

Service System Design (IÐN612M)

The aim of this course is to prepare students to participate in the design of service systems. The course covers the service design process, defining the framework of service, how to serve people, how service delivery takes place, and how services are designed. Key components of these areas are explored, with particular emphasis on needs analysis and the design of processes and systems to meet customer needs. The course also addresses product–service systems, servitization, and the experience economy. Emphasis is placed on diverse methods for mapping needs (workshops, research), creative approaches, and the use of prototyping and presentations.

Project in Operation Research (IÐN231F)

The purpose of the course is to give students an oportunity to practice solveing projects in cooperation with industry

Project in Operation Management (IÐN232F)

The purpose of the course is to give students an oportunity to practice solveing projects in cooperation with industry

Technological innovation (IÐN202M)

The aim of the course is to train students to understand and predict the interaction between technological change and innovation. Special emphasis is placed on being able to identify and explain the fundamental aspects of technological innovation and to present a reasoned forecast of opportunities for innovation in an emerging technological field. The learning process takes place primarily with the assistance of artificial intelligence (large language models), where the AI’s outputs are systematically reviewed, among other things with the support of data and with input from the group, the instructor, and experts.

Field Course in Innovation and Entrepreneurship (II) (IÐN216F)

The course is a continuation of the course "Field Course in Innovation and Entrepreneurship (I)". This part of the course consists of detailed development of the business model related to a particular business opportunity. This work takes place in groups, where cross-disciplinary collaboration, between individuals with a background in business and individuals with a background in a particular technical or professional field related to the relevant opportunity, is emphasized. Projects can originate in an independent business idea or in collaboration with companies that partner with the course. In both cases, the emphasis will be on product or service development, built on technical or professional expertise, where the business case of the opportunity and its verification is in the foreground.

Innovation and start-up development (IÐN222F)

In this course, students work on developing startup projects from idea to product. The course emphasises hands-on work on real-world challenges, where students apply user-centred methods, work with business models, and conduct various analyses and planning activities. The course is largely workshop-based and encourages collaboration among students from different academic backgrounds. Emphasis is placed on the critical analysis of opportunities for innovation and entrepreneurship, creative problem solving, and the development of ideas based on user needs. Students gain competence in methods for testing and communicating ideas and solutions, and develop an understanding of the practical and societal impacts of innovation in a broad context. The course is suitable for students who wish to strengthen their entrepreneurial mindset and learn to apply innovation methods, whether within organisations, established companies, or as independent entrepreneurs.

Technology and innovation (IÐN227F)

The aim of the course is to give students an insight into research on the interaction between technology and innovation in modern societies. The course will cover key concepts, theories and unresolved questions within the field, recent academic articles will be reviewed, and contemporary issues will be discussed. Special emphasis will be on the use of simulation model when doing research within this field.

Performance Management (IÐN229F)

The course introduces the various aspects of performance management. There are two main poles in the course: performance management from a business perspective and performance measurement from an operational perspective. In the course the basic ideology of performance management will be covered along with: the implementation process, the main tools, the negative aspects of such systems will be covered and topics like: key metrics (KPIs), systems approach (PMS), productivity, performance measures and measurement design. Students become acquainted with the implementation of performance management in Icelandic companies and institutions and work on realistic projects.

Knowledge worker Productivity (IÐN230F)

The aim of the course is to provide students with insight into research on the Knowledge Worker Productivity. The main concepts, theories and issues in this field will be reviewed, recent academic articles will be reviewed and the issues that are in focus at any given time will be discussed. The productivity of a knowledge worker will be placed in the context of workers' productivity and modern trends of automation.

Simulation (IÐN403M)

Simulation techniques and system modelling find application in fields as diverse as physics, chemistry, biology, economics, medicine, computer science, and engineering. The purpose of this course is to introduce fundamental principles and concepts in the general area of systems modelling and simulation. Topics to be covered in this course are discrete event simulation, statistical modelling, and simulation modelling design, experimental design, model testing and interpretation of simulation results. The maximum likelihood estimation of probability distributions base on real data is presented. The course will also introduce the generation of random variates and testing. Fundamental programming of simulation models in C is covered and specialized simulation packages introduced. The students will complete a real world simulation project where the emphasis will be on manufacturing or service systems.

Lean Management (IÐN601M)

In this course the focus is on the methodologies of lean management where the goal is to maximize customer value. The methods used are continuous improvements (Kaizen) on the supply chain, standardized work and leveling out work load on people and machines (Heijunka). Students will learn how to use lean methodologies to maximize quality, minimize lead time while simultaneously lowering cost. Continuous flow (Just in time) can shorten the production and process time while problems in the process are identified immediately when they occur (Jidoka). Students will learn value stream mapping for both production and service process, root cause analysis along with other Lean tools such as, 5S, Kaizen Blitz, A3, visual management, Kanban and SMED. In the cours examples of companies that have gone through Lean transformation will be studied.

Business Intelligence (IÐN610M)

Business intelligence are the strategies and technologies companies use to collect, interpret and utilize data for decision support. This course goes beyond reports and dashboards and demonstrates how artificial intelligence can help us gain insights and recommend action. The course is comprised of five learning modules: 1.) regression and classification where data is segmented accoring to predetermined labels. 2.)  semi- and un-labelled data where items are grouped based on similarity measures. 3.) Process mining. 4.) Natural language processing, and 5.) Data ethics. Within each learning module students prepare for class and work in teams on a business problem, followed by an individual assessment.