Increasing Energy Efficiency in Production Environments Through an Optimized, Hybrid Simulation-based Planning of Production and Its Periphery

Sobottka, Thomas; Kamhuber, Felix; Sihn, Wilfried

doi:10.1016/j.procir.2016.11.151

Cited by 17 publications

(6 citation statements)

References 9 publications

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“…Fig. 1) have been applied in academia to predict the energy demand of production systems on different levels such as process, process chain, and factory level [14,16,17].…”

Section: Product Life Cycle Simulationmentioning

confidence: 99%

Integrated computational product and production engineering for multi-material lightweight structures

Hürkamp

Dér

Gellrich

et al. 2020

Int J Adv Manuf Technol

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Within product development processes, computational models are used with increasing frequency. However, the use of those methods is often restricted to the area of focus, where product design, manufacturing process, and process chain simulations are regarded independently. In the use case of multi-material lightweight structures, the desired products have to meet several requirements regarding structural performance, weight, costs, and environment. Hence, manufacturing-related effects on the product as well as on costs and environment have to be considered in very early phases of the product development process in order to provide a computational concept that supports concurrent engineering. In this contribution, we present an integrated computational concept that includes product engineering and production engineering. In a multi-scale framework, it combines detailed finite element analyses of products and their related production process with process chain and factory simulations. Including surrogate models based on machine learning, a fast evaluation of production impacts and requirements can be realized. The proposed integrated computational product and production engineering concept is demonstrated in a use case study on the manufacturing of a multi-material structure. Within this study, a sheet metal forming process in combination with an injection molding process of short fiber reinforced plastics is investigated. Different sets of process parameters are evaluated virtually in terms of resulting structural properties, cycle times, and environmental impacts.

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“…Fig. 1) have been applied in academia to predict the energy demand of production systems on different levels such as process, process chain, and factory level [14,16,17].…”

Section: Product Life Cycle Simulationmentioning

confidence: 99%

Integrated computational product and production engineering for multi-material lightweight structures

Hürkamp

Dér

Gellrich

et al. 2020

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Comparison: For the discussed case study, an optimization based on a GA was also implemented as part of a research project, see [264]. A comparison shows a similar performance: While the optimization potential found is nearly the same, the GA typically requires significantly more evaluations, up to twice as many for a comparable scenario [261], due to its population-based nature.…”

Section: Multi-objective Production Planning Optimizationmentioning

confidence: 99%

“…On the other hand, the GA offers itself to easier parallelize the computations in order to decrease runtime, although there are also some parallelization strategies for VNS [73,74]. For a more detailed discussion on the GA, we refer to [261,264,263,252].…”

Section: Multi-objective Production Planning Optimizationmentioning

confidence: 99%

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Methods for Hybrid Modeling and Simulation-Based Optimization in Energy-Aware Production Planning

Heinzl¹

2020

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Production Planning (PPC) optimization in the operation of industrial production plants enables to achieve energy savings and improve energy efficiency. To provide decision support in production planning, simulation-based methods are particularly well-suited for complex production system to assess different planning scenarios and evaluate them with regard to their energy demand. To this end, a hybrid discrete/continuous modeling approach based on the Discrete-Event System Specification (DEVS) is investigated in this thesis. Hybrid modeling allows to accurately capture material flows as discrete entities on one hand and continuous energy flows on the other hand, in the production as well as for other domains, such as heating or cooling, up to the thermal building envelope, including their dynamic interactions. A meta-heuristic optimization procedure for operative production planning evaluates energy efficiency together with other production goals (delivery tardiness, storage costs, etc.) as part of a multi-objective optimization. The procedure follows a hybrid Variable Neighborhood Search (VNS) meta-heuristic and uses the hybrid simulation for the energetic evaluation of production programs. The VNS uses special energy-related operators, for example to optimize the setup times of heating ovens, in order to save energy. Furthermore, to support the modeling process of such DEVS-based simulations in practical applications, a domain-specific model abstraction is designed, which allows to describe hybrid simulation models in an intuitive way. The method is based on a Model-Driven Engineering (MDE) process and defines a component-based meta-model for production systems. Concrete implementations can be derived from the abstract specification by means of model transformations. Proof-of-concept implementations are realized to evaluate the individual contributions and case studies from industry demonstrate the practical applicability. The hybrid simulation approach was implemented in its entirety as a simulator prototype, together with reusable model components, which can be used by practitioners to design new application models with reduced efforts. Altogether, these methods contribute to the integration of energy efficiency aspects into modern PPC systems and to increase energy efficiency in production. i Kurzfassung Im operativen Betrieb von industriellen Anlagen lassen sich durch optimierte Produktionsplanung und-steuerung (PPS) gezielt Energieeinsparungen erreichen und die Energieeffizienz verbessern. Zur Entscheidungsunterstützung bieten sich gerade bei komplexen Produktionssystemen simulationsbasierte Methoden an, die es ermöglichen, unterschiedliche Planungsszenarien zu vergleichen und hinsichtlich ihres Energieeinsatzes zu bewerten. Dafür wird in der vorliegenden Arbeit ein hybrider diskret/kontinuierlicher Modellierungsansatz untersucht, der auf der Discrete-Event System Specification (DEVS) basiert. Die hybride Modellierung erlaubt es einerseits die Materialflüsse als diskrete Entitäten, andererseits auch die zei...

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“…Different metaheuristic algorithms, such as Evolutionary Computation, Tabu Search, Particle Swarm Optimization (PSO) or Simulated Annealing (SA) have been successfully applied to various logistics optimization problems [13]. In previous work, we have also investigated applying a Genetic Algorithm on a similar case study [14,15]. A set of tuning and customization measures was applied to significantly improve performance, including adapting operators for a guided search, and hybridization with Tabu Search and Pattern Search.…”

Section: Related Workmentioning

confidence: 99%

Metaheuristic Simulation-based Production Planning for Energy Efficiency: A Case Study

Heinzl¹,

Kästner²

2020

SNE

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Modern industrial production planning and control (PPC) systems are responsible for supporting planning decisions on how to optimally produce a given set of products while minimizing costs and retaining production constraints, such as delivery tardiness or offtimes. In recent years, more and more attention has also been paid on energy efficiency as part of production optimization, resulting in competing optimization targets. In order to solve such complex multi-objective scheduling problems in practice, metaheuristic methods are used because of their ability to deliver acceptable solutions in feasible time. In this paper, we demonstrate the application of a General Variable Neighborhood Search (GVNS) metaheuristic on a case study of flow shop scheduling in an industrial bakery in different scenarios and study the effect of different energy prices on the planning result. The case study features a simple production line with thermal processes for baking and freezing and also incorporates the energy supply system as well as a model of the thermal building hull. The metaheuristic is combined with a hybrid discrete/continuous simulation model to evaluate the energy efficiency of different production scenarios. The hybrid simulation enables to accurately capture material and energy flow within the production in an integrated and dynamic manner. Overall, this simulation-based optimization method is intended to support energy-aware production scheduling in practical applications.

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Increasing Energy Efficiency in Production Environments Through an Optimized, Hybrid Simulation-based Planning of Production and Its Periphery

Cited by 17 publications

References 9 publications

Integrated computational product and production engineering for multi-material lightweight structures

Integrated computational product and production engineering for multi-material lightweight structures

Methods for Hybrid Modeling and Simulation-Based Optimization in Energy-Aware Production Planning

Metaheuristic Simulation-based Production Planning for Energy Efficiency: A Case Study

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