COURSE CONTENT

The course content focuses on optimization as an integrated methodology for mechanical engineering design and decision-making, covering all levels of design, from the product level to complex production systems.

• Introduction to optimization as a design approach and the transition from feasibility analysis to optimal decision-making. Formulation of optimization problems, including the definition of objectives, constraints, and design variables, introduction to parametric design as well as the concepts of design space and feasible solutions.
• Performance criteria and evaluation indicators (KPIs) at different levels of design (technical, economic, and operational), multi-criteria optimization, analysis of conflicting objectives and trade-offs, and the concept of Pareto-optimal solutions.
• Application of optimization across different levels of mechanical engineering design, including product-level, process-level, and machine-level optimization, as well as the design and optimization of complex systems (material flow, synchronization, bottlenecks, ergonomics, safety, flexibility, and human factors)
• Optimization methods and tools, including numerical and evolutionary algorithmic approaches, simulation-based optimization, what-if scenario analysis, sensitivity analysis and interpretation of results, as well as principles of robust design under uncertainty.

Integrated case study combining all design levels (product → process → system), leading to knowledge synthesis and informed decision-making.

LEARNING OUTCOMES

The course is elective and is offered to all sectors during the 9th semester of studies.

Objectives of the course:

1. Recognition of the importance of optimization as a fundamental methodology in mechanical engineering design and decision-making
2. Familiarization with the formulation of optimization problems (objectives, constraints, design variables) – Parametric design principles
3. Understanding of performance criteria and evaluation indicators (KPIs) at different levels of design
4. Understanding of multi-criteria problems and development of the ability to analyze trade-offs
5. Understanding of the hierarchical nature of design (product → process → machine → complex systems)
6. Familiarization with optimization methods and simulation tools for supporting design decisions
7. Acquisition of practical experience through the application of the above in an integrated case study

 Upon successful completion of the course the student will be:

• Understand the role and importance of optimization in modern mechanical engineering practice
  • Be able to formulate and analyze optimization problems at different levels of design
  • Be familiar with evaluation criteria and decision-support methods
  • Be able to analyze and manage conflicting objectives and design trade-offs
  • Be able to apply optimization methods to products, processes, and systems
  • Be able to utilize simulation tools and interpret results for decision-making
  • Be capable of justifying design decisions under conditions of uncertainty