All posts by Jalamdhara

Towards a Design Methodology for Self-optimizing Systems

Towards a Design Methodology for Self-Optimizing Systems

Author: Jurgen Gausemeier, Ursula Frank, Andreas Schmidt, and Daniel Steffen

Abstract: Self-optimizing systems will be able to react autonomously and flexibly to changing environments. They will learn and optimize their performance during their product life cycle. The key for the design of self-optimizing systems is to utilize reconfigurable system elements, communication structures and experienced knowledge. The concept of active principles of Self-Optimization is an important starting point.

5.2 Self-optimizing Systems

In terms of software engineering, this involves distributed systems of interacting agents:

“An agent is an autonomous, proactive, cooperative and extremely adaptive function module. The term “autonomous” implies an independent control system, which it proactively initiates actions.  Agents are regarded as function modules, which work in cooperation or competition with one another. “Adaptive” refers to a generic behavior at run time, which may also, for example, include learning capabilities. A function module is taken to be a heterogeneous subsystem with electronic, mechanical and IT-related components.”

Combining the paradigm of intelligent agents with mechatronic structures makes it possible to construct self-optimizing mechanical engineering systems.

“Self-optimization of a technical system refers to the endogenous modification of the target vector due to changing environmental conditions and the resulting target-compliant, autonomous adaptation of the structure, the behavior and the parameters of this system. Self-optimization, therefore, far exceeds known control and adaptation strategies. Self-optimization enables empowered systems with inherent “intelligence,” which are able to react autonomously and flexibly to changing environmental solutions”

The examination of self-optimizing systems is based on four aspects:

  1. the target system (e.g. a hierarchical system of targets or a target vector)
  2. the structure (i.e. topology of mechanical components, sensors and actuators),
  3. the behavior and
  4. the parameters.

The following principles determine Self-optimization:

  • Reconfiguring system elements
    • An adaptation to different environmental situations presupposes the presence of system elements which can be reconfigured or which can interact with other system elements in different combinations. In a chassis, for example, redundant actors (mechanical feather/spring, pneumatic spring, hydraulic cylinder) are used. They are used together in different ways (parallel/in series) to absorb different stimuli.
  • Communication
    • System elements behave like software agents. They pursue their targets according to the target system of the overall system. They achieve these targets by negotiations and co-operation with other system elements. For adjustment processes and negotiation principles, generic patterns are defined. Examples for communication relations are the chassis reconfiguration or an arrangement about the right of way between two vehicles.
  • Experienced knowledge
    • In order to ensure the optimal behavior in unknown operating situations or in situations that are not described in models, experienced knowledge embodied as cases is stored and used again in similar situations. It is shared with other systems, as well. So-called active principles of Self-optimization describe generic patterns of behavior, which can be used in many situations. Especially the use of active principles of Self-optimization creates greater opportunities and enables absolutely new functionalities.

 Active Principles of Self-optimization

Active principles of Self-optimization are meant to be a combination of a technical system and the influences on the technical system (the environment, the user, or other system elements) and adaptation components. The technical system consists of a structure model, in terms of the topology of mechanical components or the hierarchy of multi-agent systems, a behaviour model, such as differential equations or planning and learning systems, and the parameterization of the models. A target system prescribes the current goals which the technical system tries to achieve. In this way the active principle of Self-optimization allows for the endogenous
modification of the technical system according to changing influences, as well as for target-compliant, autonomous adaptation of parameters, behaviour and structure. Adaptation strategies and adaptation tactics define the kind and process of modifications for long-term and medium- to short-term adaptation to application scenarios. Adaptation costs represent the effort of adaptation in terms of energy consumption, time-delays, monetary payments and the like.



Self Organization in Design

Self Organization in Design

Author: Bart R. Meijer

Abstract: Principles of self organization are discussed as a frame of reference and a source of ideas for new design processes that can deal with more complexity in less time. It is demonstrated that set-based concurrent engineering makes effective use of these principles. Taking this idea one step further, an evolutionary organization for design processes is proposed.

Most academic institutions still teach structured design to their students Not because it is the best method, guaranteed to lead to good designs, but merely because it addresses all the relevant areas of design processes in a comprehensible way to students unaware of their own early design experiences.

Axiomatic design is not fundamentally different from structured design. The design phases are roughly identical. Axiomatic design is characterized by the design matrices, which represent an efficient data representation. They show where design decisions are complicated (coupled) and where they are not. The problem of developing a set of uncoupled or decoupled design matrices spanning the design space for our problem is as complex as solving the design problem by using structured design.

Following the principle of structured or axiomatic design, one could easily see a phased plan, perfectly fit for a work breakdown structure and presumably fit for effective and efficient development process. Industrial practice shows that this approach often results either in risk-adverse incremental development of a known concept, or in cyclic hard to finalize development processes in case a new concept was pursued. It is very hard to predict up-front what dependencies in which concepts are vital to a successful design. As a consequence, the product of architectures of cars and aircraft have not changed significantly over decades of their existence. Despite claims that technology developments are speeding up, the impact of new technology or new materials is often limited to redesign of subsystems. The problem of introducing new and unknown relations are avoided as much as possible. The opportunities of new business models are the scope of implementing new technologies into existing product platforms.

 4.2 Concepts of Self Organization

In the area of systems control and cybernetics, self organization refers to systems that are capable of changing their structure and their functionality on order to adapt to new environments.  Another perspective on self organization originates from a systems perspective on understanding nature, life and organizations. This perspective, called autopoiesis (self-production), does not take adaptation as a response to changes in the environment as axiom, but it claims that living structures influence or adapt to their environment as a means to self-maintain and improve their chance of reproduction.


An autopoietic machine is a machine organized (defined as a unity) as a network of processes of production (transformation and destruction) of components that produce components which:

  1. through their actions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and
  2. constitute it (the machine) as a concrete unity in the space in which they (the components) exist by specifying the topological domain of its realization as such a network.

Evolutionary Problem Solving

Evolutionary problem solving is based on the structure of genetic algorithms. The basic structure of the genetic algorithm, originally developed by Holland is as follows:

  1. Initialize a starting population of physically feasible solutions.
  2. Create a new generation through genetic operands such as mutation, crossover and reproduction
  3. Rank this population using the fitness function.
  4. Select the top of this population and randomly select a couple of others to create a new starting population
  5. Repeat steps 2-4 until top-member of a generation has sufficient fitness score to be acceptable as a solution.

The success of this nature-inspired algorithm can be attributed to two properties that make it distinct from linear optimization techniques. The first property is redundancy and diversity. Rather than developing one solution, genetic algorithms develop and maintain multiple solutions concurrently. The resulting diversity is needed to maximize the probability to have solutions available at all times that can comply with all requirements and constraints. The second property is the non-linearity of the selection process. With linear optimization the fitness landscape is set from the start by the starting solution and the fitness function. Finding the optimum in this landscape could mean an exhaustive search through the entire landscape. Although the fitness landscape is set from the start, a genetic algorithm employs multiple starting points for the search, and the generation and selection steps cause the effective fitness landscape to be reshaped at the start of each generation.

Evolutionary Problem Solving and Self Organization

The genetic algorithm (GA) is a system model for the self-reproduction principles of the autopoiesis theorem. The solution patterns a GA may generate are predominantly the result of the initial set of solutions that were present at the start. The fitness function is the context within which structure changes may occur as long as survival as a unity or species is not at stake. Changing the fitness function will cause serious changes and may also cause death in case the present elements can not generate a sufficient fit (survival) to the new fitness function. in case of survival, biologists may recognize evolution, but they may also claim that the new organism is a different unity that is capable of a different set of interactions, fit for the new context. Thus the old species is declared extinct since it evolved into a new distinguishable organism.

4.3 Set-based Concurrent Engineering

Ward and his co-authors argue that in concurrent engineering there are two fundamentally different approaches to be recognized: point-based and set-based. In case of point-based design, a single solution is synthesized first, then analyzed and changed accordingly.  Even though the phases of the design process may be executed concurrently, all designers and specialists are investing their efforts in the pursuit of only one concept that is to be developed into a solution.

In set-based concurrent engineering, designers explicitly communicate and think about sets of design alternatives at both conceptual and parametric levels. The efficiency of set-based versus point-based design is that in communicating sets, implicitly or explicitly, all designers become more focused on relations and constraints between different aspects of the design than they would be when focusing at a point solution. All designers communicate their range of options rather than one preferred option. Sometimes to maintain focus, constraints for these sets can be set tighter than they would be in case of a point based design.

Set-based Concurrent Engineering: Toyota and Nippondenso

The set-based engineering process of Toyota have the following characteristics:

  1. The team defines a set of solutions at the system level rather than a single solution
  2. It defines sets of possible solutions for various subsystems.
  3. It explores these possible solutions in parallel, using analysis, design rules and experiments to characterize a set of possible solutions.
  4. It uses analysis to gradually narrow the sets of solutions. In particular the team uses analysis of the set of possibilities for subsystems to determine appropriate specifications to impost on those subsystems
  5. Once the team establishes a single solution for any part of the design, it does not change it unless absolutely necessary


Nippondenso. . . .also applies a process that has characteristics of set-based concurrent engineering and extends this even to pre-design R&D. In this process, the degree of parallelism and redundancy is much higher than it typically is with Toyota.

As an automotive supplier, the demand for diversity is higher and their competitiveness is much affected by new technologies and new materials. In order to push the limits and to stay ahead of the competition, Nippondenso tests as many ideas as they can to create a platform (set) of solutions that is competitive and can be easily adapted to the specific interfacing requirements of different car makes. What may be a surprise is that the start of Nippondenso’s development processes may be 3-5 years ahead of the start of the car development processes that adopt the new designs. Rather than pursuing rapid development once the outline of specification from their customers is clear, Nippondenso pursues radical breakthrough designs that are ready before their customers ask for them. When they start working with their customers, the focus is on interfacing and not on the core technology, which enables them to avoid the major part of development risk.

4.4 Evolutionary Organization of Design Process

The process can be as follows:

  1. Divide the staff into n independent teams that are all capable of executing the entire project, and give all these teams an identical assignment and a deadline.
  2. The teams will develop their concepts and solutions following set-based concurrent engineering practices, and they will record their achievements and findings in lessons learned books.
  3. At regular intervals, a fair is organized where all teams present their progress and give insight int their lessons learned books.
  4. At these fairs, team members look around for promising partial solutions with their “competitors”
  5. After the fair, teams continue their own development, including ideas inspired by the last fair.
  6. If a design with sufficient fitness has been achieved, stop; else, repeat steps 3-5.

The processes within the teams could also have the characteristics of a genetic algorithm if they apply brainstorming for finding and selecting ideas. However, the fair is really the place where crossovers and mutations occur. At the fair, everyone is looking for clever ideas that could fit to their own concept (crossings), and some ideas may also trigger new thoughts (mutations). Although a fitness function that could be used for ranking exists, the organizational form of a GA has the advantage that the ranking of partial ideas is fuzzy and not explicit. This means that ideas that may not be very successful in one context could be a perfect fit in another context. In case of explicit ranking, these ideas could have been lost. The implicit ranking also solves a social problem of working with a large engineering group force, where a dozen socially dominant engineers will monopolize the decision making at centralized meetings to a degree where a significant portion of the engineering staff effectively has no influence. Because the central meeting is now a fair where implicit recognition is the mechanism for the survival of ideas, good ideas, regardless of their source, stand a good chance of being inherited into the final concept. The process can be made more efficient if overlaid with a structured design process where the progress at the exchange moments (fairs) becomes synchronized.

‘What-if’ Design as an Integrative Method in Product Design

‘What-if’ Design as an Integrative Method in Product Design

Author: Fred van Houten, and Eric Lutters

Abstract: In product development, many different aspects simultaneously influence the advancement of the process. Many specialists contribute to the specification of products, whilst in the meantime the consistency and mutual dependencies have to be preserved. Consequently, much effort is spent on mere routine tasks, which primarily distract members of the development team of their main tasks of creating the best solution for the design problem at hand. Many of these routine tasks can be translated into problems with a more or less tangible structure; often they are in fact an attempt to assess the consequences of a certain design decision on the rest of the product definition. Therefore, such questions can be formulated as: “what happens if. . . . .”. The question is subsequently translated into a need for evolution of the information content determining the product definition. Based on this need for information, immediate workflow management processes can be triggered. This results in a ‘train’ of design and engineering processes that are carried out, leading to a viable answer to the question. As the structure of a ‘what-if’ question is independent of the domain under consideration, the ‘what-if’ questions can relate to any aspect in the information content at any level of aggregation. Consequently ‘what-if’ questions can range from anything between ‘What if another machine tool is used’ to ‘What does this product look like if it is made from sheet metal’. Such a way of looking at products under development obviously strongly binds different domains and downstream processes under consideration, thus enabling a more integrated approach of the design process.

Two approaches can be applied simultaneously:

  • A generic top-down approach, focusing on the methods of answering structured ‘what-if’ questions, whilst disregarding any specific domain information, and avoiding any bias of solution routines
  • A bottom-up approach, contributing to understanding the application of a ‘what-if’ system and support systems in general.

‘what-if’ design can be described as the information-based and workflow-driven, structured approach to the chart the consequences of design decisions or changes in a design.


Directions of Next Generation Product Development

Directions of Next Generation Product Development

Author: Tetsuo Tomiyama and Bart R. Meijer

 Abstract: For the last 20 years, the focus has been on product development processes and developing tools to support them, addressing not only technological but also managerial issues. While these tools have been successfully supporting product development processes in a general sense, consensus on the direction of future development seems to be lacking. IN the paper, it is argued that horizontal seamless integration of product life cycle knowledge is the key towards the next generation product development. Knowledge fusion, rather than just knowledge integration, is considered crucial. In this paper, we will try to outline the directions of the next generation product development its tools, and necessary research efforts.

 . . . we pointed out that “horizontal” “seamless integration of knowledge” about product’s life cycle is the key to arriving at product development for better, more innovative, quicker, and still greener products.

A knowledge system that represents a mono-discipline required for developing a simple, mono-disciplinary product.


We may then need a set of closely related knowledge systems for multidisciplinary product development. Integrating these closely related knowledge systems requires defining at least interfaces. Such multidisciplinary integration is a key for innovative product development.

knowledge2However, to be more innovative, we may need to go one step further; knowledge fusion.

knowledge3Knowledge fusion is to create a new knowledge system that can be operated as a whole to develop truly multidisciplinary products. Knowledge integration is still a collection of independent knowledge systems with clearly defined interfaces and describes common concepts among those integrated knowledge systems, while knowledge fusion is a situation in which these systems have been totally fused to create a new knowledge system.

. .. the directions of the next generation product development and necessary research efforts. Three key issues were identified. The first issue is “more horizontal integration” to include a wider range of engineering activities. The second is “seamless integration of activities” beyond data and knowledge level integration. The activities within product development can include such tasks as design, computation, procurement, prototyping, and testing, and might even be extended to SCM and VCM based on PLM. The third is product development still pursues” better quality, lower costs, more innovation, higher speed, and yet greener performance.”

For these three issues, knowledge integration plays a crucial role. However since knowledge integration only arrives at a set of knowledge collection of which interfaces are clearly defined, we may need another step; knowledge fusion.

Economic Growth, Business Innovation and Engineering Design

Economic Growth, Business Innovation and Engineering Design

Author: Gunnar Sohlenius, Leif Clausson, and Ann Kjellberg

Abstract: Scientific knowledge of engineering within innovative industrial decisions process has a great potential to improve quality and productivity in industrial operations and hence improve profitability. This is a precondition for economic growth, which in turn is necessary to improve welfare. Innovative processes have to combine creativity with quality and productivity in order to achieve profitability and growth. The most important ways to improve profitability in industrial production are through an improved ability to meet more advanced requirements in new products and processes by using new knowledge and inventions and higher productivity through investments in more advanced and automatic tools. This is the fundamental mechanism behind industrial production seen as an engine of welfare. Besides the real world of products and production processes, the mechanisms for this development can be classified into three worlds. These are the decision world, the human world and the model world. In striving to obtain increased welfare through industrial production, fundamental knowledge about these worlds and about their relations to products and processes has to be developed. This paper is a contribution to this understanding, which is necessary in order to combine Total Quality Management, (TQM) and Total Productivity Management (TPM) into Total Effective Management (TEM) by understanding Means.




  • Axiom 1: A design maintaining the independence of functions is superior to coupled designs.
  • Axiom 2: A design with higher probability to meet the functional requirements within specified tolerances is superior
  • Axiom 3: A design requiring less energy to be realized is superior
  • Axiom 4: A design requiring less time to be a realized is superior

Competence Management Process


Industrial Company as a Business System