09-04-2012, 03:33 PM
Industrial Engineering of Complex Systems
[attachment=19781]
Relevance of Industrial Engineering for Siemens
• Layout, equipment and engineering of industrial plants account
for 45% or 35 Billion € of the Siemens turnover
• The business type is a solution business, i.e., building upon a
large set of technologies, available components and IT assets a
unique solution is constructed in order to meet the customer’s
requirements to the point.
Automation and Support of Industrial Engineering by new Software Methods and Tools
• Modeling a parameterized reference
architecture by applying and
developing software methods
• Support of configuration of admissible
incarnations of the reference
architecture by appropriate software
tools
• Decision support for configuration of
admissible incarnation of the reference
architecture by implementing new
meta-heuristics for structural selforganization
• Validation of incarnation of the
reference architecture by implementing
new multi-criteria optimization
algorithms
Engineering of Industrial Plants
• Outset: Customer’s requirements and targets for an industrial plant are
the foundation for the evaluation of any proposed solution
• Transform these requirements and targets into a set of objective functions (e.g., using
KPIs) and / or constraints that characterize an admissible solution
• Develop a systematic approach to derive a reference architecture for a
solution
• Incorporate the constraints and rules arising from the provider’s business conduct
guidelines, from the engineering process applied, and the available technologies and
components (hardware, software, mechatronics)
• Formalize the applicability and interdependencies of available technologies and
components
• Develop and apply meta-heuristics (e.g., agent-like)
• to generate a possible solutions, i.e., a population of the reference architecture with
suitable technologies and components
• to optimize the solutions with respect to fulfilling the customer’s targets and
constraints
Department for Discrete Optimization
• Center of Excellence for industrial,
applied mathematics and IT
• Discrete Mathematics, Stochastics, Meta-
Heuristics
• Application example: Automatic and
cost optimized layout of RF
communication networks
• Objective: Minimize Capex for network
infrastructure
• Degrees of freedom:
• a set of possible locations for network nodes.
• a set of different parameterized equipment to
populate networks nodes
[attachment=19781]
Relevance of Industrial Engineering for Siemens
• Layout, equipment and engineering of industrial plants account
for 45% or 35 Billion € of the Siemens turnover
• The business type is a solution business, i.e., building upon a
large set of technologies, available components and IT assets a
unique solution is constructed in order to meet the customer’s
requirements to the point.
Automation and Support of Industrial Engineering by new Software Methods and Tools
• Modeling a parameterized reference
architecture by applying and
developing software methods
• Support of configuration of admissible
incarnations of the reference
architecture by appropriate software
tools
• Decision support for configuration of
admissible incarnation of the reference
architecture by implementing new
meta-heuristics for structural selforganization
• Validation of incarnation of the
reference architecture by implementing
new multi-criteria optimization
algorithms
Engineering of Industrial Plants
• Outset: Customer’s requirements and targets for an industrial plant are
the foundation for the evaluation of any proposed solution
• Transform these requirements and targets into a set of objective functions (e.g., using
KPIs) and / or constraints that characterize an admissible solution
• Develop a systematic approach to derive a reference architecture for a
solution
• Incorporate the constraints and rules arising from the provider’s business conduct
guidelines, from the engineering process applied, and the available technologies and
components (hardware, software, mechatronics)
• Formalize the applicability and interdependencies of available technologies and
components
• Develop and apply meta-heuristics (e.g., agent-like)
• to generate a possible solutions, i.e., a population of the reference architecture with
suitable technologies and components
• to optimize the solutions with respect to fulfilling the customer’s targets and
constraints
Department for Discrete Optimization
• Center of Excellence for industrial,
applied mathematics and IT
• Discrete Mathematics, Stochastics, Meta-
Heuristics
• Application example: Automatic and
cost optimized layout of RF
communication networks
• Objective: Minimize Capex for network
infrastructure
• Degrees of freedom:
• a set of possible locations for network nodes.
• a set of different parameterized equipment to
populate networks nodes