Refine
Year of publication
- 2016 (57) (remove)
Document Type
- Book chapter (57) (remove)
Is part of the Bibliography
- yes (57)
Institute
- Informatik (23)
- ESB Business School (15)
- Technik (11)
- Life Sciences (6)
- Texoversum (1)
Publisher
- Springer (16)
- Springer Gabler (12)
- Gesellschaft für Informatik (11)
- Wiley (6)
- Routledge (3)
- Elsevier (2)
- De Gruyter Oldenbourg (1)
- Economia (1)
- Nova Publishers (1)
- Public Verlagsgesellschaft (1)
Despite 30 years of Electronic Design Automation, analog IC layouts are still handcrafted in a laborious fashion today due to the complex challenge of considering all relevant design constraints. This paper presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), a novel approach addressing the problem with a multi-agent system: autonomous layout modules interact with each other to evoke the emergence of overall compact arrangements that fit within a given layout zone. SWARM´s unique advantage over conventional optimization-based and procedural approaches is its ability to consider crucial design constraints both explicitly and implicitly. Several given examples show that by inducing a synergistic flow of self-organization, remarkable layout results can emerge from SWARM’s decentralized decision-making model.
Instead of waiting for and constantly adapting to details of political interventions, utilities need to focus on their environment from a holistic perspective. The unique position of the company - be it a local utility, a bigger player, or an international utility specializing in specitic segments - has to be the basis of goals and strategies. But without consistent translation of these goals and strategies into processes, structures, and company culture, a strategy remains pure theory. Companies need to engage in a continuing learning process. This means being willing to pass on strategies, to slow down or speed up, to work from a different angle etc.
Verlängerte Werkbank, Global Sourcing, Low-Cost-Country-Potenziale, Outsourcing: Seit Jahren herrscht eine inflationäre Verwendung dieser Schlagworte in den Vorstandsetagen. Der Markt für professionelle Dienstleistungen ist schon lange nicht mehr auf die Strategieberatungsbranche beschränkt. Mittlerweile gibt es fast keinen Unternehmensprozess in einem produzierenden Unternehmen mehr, der nicht hinsichtlich seiner Auslagerbarkeit an Berater oder Dienstleister geprüft wurde. Die Autorin beschreibt, warum professionelle Dienstleister zunehmend beauftragt werden und zeigt am Beispiel einer Service-Performance-Studie die Erfolgsfaktoren für die Zusammenarbeit auf. Anhand einer Collaboration Check List legt sie dar, was im täglichen Doing wichtig ist. Abschließend werden die Risiken und Chancen der Inanspruchnahme von Dienstleistungen aus Kunden- und Lieferantenperspektive beleuchtet.
Motivation
(2016)
Since human beings started to work consciously with their environment, they have tried to improve the world they were living in. Early use of tools, increasing quality of these tools, use of new materials, fabrication of clay pots, and heat treatment of metals: all these were early steps of optimization. But even on lower levels of life than human beings or human society, we find optimization processes. The organization of a herd of buffalos to face their enemies, the coordinated strategies of these enemies to isolate some of the herd’s members, and the organization of bird swarms on their long flights to their winter quarters: all these social interactions are optimized strategies of long learning processes, most of them the result of a kind of collective intelligence acquired during long selection periods.
In this chapter we introduce methods to improve mechanical designs by bionic methods. In most cases we assume that a general idea of the part or system is given by a set of data or parameters. Our task is to modify these free parameters so that a given goal or objective is optimized without violation of any of the existing restrictions.
We have seen that bionic optimization can be a powerful tool when applied to problems with non-trivial landscapes of goals and restrictions. This, in turn, led us to a discussion of useful methodologies for applying this optimization to real problems. On the other hand, it must be stated that each optimization is a time consuming process as soon as the problem expands beyond a small number of free parameters related to simple parabolic responses. Bionic optimization is not a quick approach to solving complex questions within short times. In some cases it has the potential to fail entirely, either by sticking to local maxima or by random exploration of the parameter space without finding any promising solutions. The following sections present some remarks on the efficiency and limitations users must be aware of. They aim to increase the knowledge base of using and encountering bionic optimization. But they should not discourage potential users from this promising field of powerful strategies to find good or even the best possible designs.
Application to CAE systems
(2016)
Due to the broad acceptance of CAD-systems based on 3D solids, the geometric data of all common CAE (Computer-Aided Engineering) software, at least in mechanical engineering, are based on these solids. We use solid models, where the space filled by material is defined in a simple and easily useable way. Solid models allow for the development of automated meshers that transform solid volumes into finite elements. Even after some unacceptable initial trials, users are able to generate meshes of non-trivial geometries within minutes to hours, instead of days or weeks. Once meshing had no longer been the cost limiting factor of finite element studies, numerical simulation became a tool for smaller industries as well.
Due to the broad acceptance of CAD-systems based on 3D solids , the geometric data of all common CAE (Computer-Aided Engineering) software, at least in mechanical engineering, are based on these solids. We use solid models , where the space filled by material is defined in a simple and easily useable way. Solid models allow for the development of automated meshers that transform solid volumes into finite elements. Even after some unacceptable initial trials, users are able to generate meshes of non-trivial geometries within minutes to hours, instead of days or weeks. Once meshing had no longer been the cost limiting factor of finite element studies, numerical simulation became a tool for smaller industries as well.
In the early days of automated meshing development, there were discussions over the use of tetragonal (Fig. 4.1) or hexagonal based meshes. But, after a short period of time, it became evident, that there were and will always be many problems using automated meshers to generate hexagonal elements . So today nearly all automated 3D-meshing systems use tetragonal elements .
To illustrate the power and the pitfalls of Bionic Optimization, we will show some examples spanning classes of applications and employing various strategies. These applications cover a broad range of engineering tasks. Nevertheless, there is no guarantee that our experiences and our examples will be sufficient to deal with all questions and issues in a comprehensive way. As general rule it might be stated, that for each class of problems, novices should begin with a learning phase. So, in this introductory phase, we use simple and quick examples, e.g., using small FE-models, linear load cases, short time intervals and simple material models. Here beginners within the Bionic Optimization community can learn which parameter combinations to use. In Sect. 3.3 we discuss strategies for optimization study acceleration. Making use of these parameters as starting points is one way to set the specific ranges, e.g., number of parents and kids, crossing, mutation radii and, numbers of generations. On the other hand, these trial runs will doubtless indicate that Bionic Optimization needs large numbers of individual designs, and considerable time and computing power. We recommend investing enough time preparing each task in order to avoid the frustration should large jobs fail after long calculation times.
Die Entwicklung dynamischer Balanced Scorecards in enger Zusammenarbeit mit Kunden zählt zum Beratungsgebiet der PA Consulting Group. Das Anwendungsbeispiel beschreibt eine dynamische Balanced Scorecard eines europäischen Automobilherstellers. Dieser verfolgt von jeher das Ziel, internationale Standards bei Technologie, Stil, Design und Leistung zu setzen. Das Unternehmen sah sich allerdings einem zunehmenden asiatischen Wettbeweb ausgesetzt, dem es mit neu entwickelten Fahrzeugen begegnen wollte. Um diese vor der Konkurrenz in den Markt einzuführen, sollten die Entwicklungsprozesse beträchtlich gestrafft werden. Zugleich sollten die Fahrzeuge zu attraktiven Preisen mit wettbewerbsfähiger und qualitativ hochwertier Ausstattung angeboten und die unternehmensweiten Profitabilitätsziele erreicht werden.
The troubles began when Tom, the business analyst, asked the customer what he wants. The customer came up with good ideas for software features. Tom created a brilliant roadmap and defined the requirements for a new software product. Mary, the development team leader, was already eager to start developing and happy when she got the requirements. She and her team went ahead and created the software right away. Afterwards, Paul tested the software against the requirements. As soon as the software fulfilled the requirements, Linda, the product manager, deployed it to the customer. The customer did not like the software and ignored it. Ringo, the head of software development, was fired. How come? Nowadays, we have tremendous capabilities for creating nearly all kinds of software to fulfill the needs of customers. We can apply agile practices for reacting flexibly to changing requirements, we can use distributed development, open source, or other means for creating software at low cost, we can use cloud technologies for deploying software rapidly, and we can get enormous amounts of data showing us how customers actually use software products. However, the sad reality is that around 90% of products fail, and more than 60% of the features of a typical software product are rarely or never used. But there is a silver lining – an insight regarding successful features: Around 60% of the successes stem from a significant change of an initial idea. This gives us a hint on how to build the right software for users and customers.