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Lecture

Concept Selection and Tradespace Exploration

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ENG-421: Fundamentals in systems engineering

Introduction to systems engineering using the classical V-model. Topics include stakeholder analysis, requirements definition, concept selection, design definition and optimization, system integration and verification and validation. This class is part of ...

Description

This lecture covers decision analysis, issues in concept selection, and simple methods like the Pugh Matrix and Multi-Attribute Utility. It also delves into non-dominance, Pareto frontiers, multiobjective optimization, and the Preliminary Design Review (PDR). The instructor discusses the V-Model in systems engineering, system architecture design, and the role of the facilitator in the concept selection process. The lecture emphasizes the importance of structured evaluation procedures, teamwork, and the convergence and divergence phases in decision-making. It also explores the challenges, critique, and utility theory in concept selection, along with examples and exercises on dominance and non-dominance in design optimization.

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Instructor

Volker Gass

After completing his master’s degree in Microtechnology at the Federal Institute of Technology in Lausanne in 1989 he worked as a Project Manager at Mecanex SA, a Swiss High-Tech company active in the field of Aerospace Mechanisms. While working at Mecanex he completed a PhD in Science in the field of applied Micro-Systems Technologies at the University of Neuchâtel, Switzerland in 1994. In 1995, Volker Gass participated in the Management Buy Out of Mecanex SA. In 2000, Mecanex was acquired by the Swiss Defence and Technology group RUAG . He was appointed to lead Customer Relations and Business Development of the newly formed Systems & Space Division at RUAG Aerospace in 2004. In the same year he was appointed member of the Swiss Academy of Engineering Sciences for his contribution in the field of High-Technology Space applications. From November 2006 to June 2007, Volker Gass successfully graduated RUAG’s Top Leadership Course, and held the position of General Manager, Sales & Marketing in the Space Division of RUAG in Switzerland as well as Member of the Board of Directors (President) of Mecanex USA Inc., Berlin, CT, until December 2009.In 2008 he successfully led the acquisition of SAAB Space and its subsidiary Austrian Aerospace. From January to June 2009 Volker Gass led the business team in the successful acquisition of Oerlikon Space. From mid-2009 to September 2011 he was responsible for Special Projects in the Marketing & Sales Organization of RUAG Space Switzerland. Since October 2011, Dr. Volker Gass is nominated Director of the Swiss Space Center at the Ecole Politechnique Fédérale de Lausanne. In March 2012, he is awarded the title of Adjunct Professor at the School of Engineering (STI) of the EPFL. From January 2014 to December 2017, he was nominated member of ESA’s Human Spaceflight and Exploration Science Advisory Committee (HESAC) and from spring 2015 to December 2018, observer at ESA’s Earth Science Advisory Committee (ESAC).As of January 1, 2021, The Swiss Space Center is renamed "Space Innovation"

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Related concepts (32)

Multi-objective optimization

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

Decision analysis

Decision analysis (DA) is the discipline comprising the philosophy, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing important aspects of a decision; for prescribing a recommended course of action by applying the maximum expected-utility axiom to a well-formed representation of the decision; and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker, and other corporate and non-corporate stakeholders.

Multiple-criteria decision analysis

Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine). Conflicting criteria are typical in evaluating options: cost or price is usually one of the main criteria, and some measure of quality is typically another criterion, easily in conflict with the cost.

Multidisciplinary design optimization

Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO), and Multidisciplinary Design Analysis and Optimization (MDAO). MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines.

Pareto efficiency

Pareto efficiency or Pareto optimality is a situation where no action or allocation is available that makes one individual better off without making another worse off. The concept is named after Vilfredo Pareto (1848–1923), Italian civil engineer and economist, who used the concept in his studies of economic efficiency and income distribution. The following three concepts are closely related: Given an initial situation, a Pareto improvement is a new situation where some agents will gain, and no agents will lose.

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