VPI-FP: an integrative information system for factory planning

Büscher, Christian; Meisen, Tobias; Schilberg, Daniel; Jeschke, Sabina

doi:10.1080/00207543.2015.1057298

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…A sufficient factory information model needs three essential parts: a calculation model, a heterogeneous data integration and decision support tools (Bejjani et al 2018). Existing factory planning processes neither support a quantitative evaluation of the planning nor assist in holistic and systematic decision support during design (Büscher et al 2016). Regarding optimization and decision-support tools for industrial facilities, numerous research has been conducted about optimization on product level and manufacturing processes (Büscher et al 2016;Francalanza et al 2017;Kluczek 2017), on sustainable manufacturing (Deif 2011) or on manufacturing energy efficiency (Garwood et al 2018;Mousavi et al 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Design space exploration for flexibility assessment and decision making support in integrated industrial building design

2021

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Industrial buildings play a major role in sustainable development, producing and expending a significant amount of resources, energy and waste. Due to product individualization and accelerating technological advances in manufacturing, industrial buildings strive for highly flexible building structures to accommodate constantly evolving production processes. However, common sustainability assessment tools do not respect flexibility metrics and manufacturing and building design processes run sequentially, neglecting discipline-specific interaction, leading to inflexible solutions. In integrated industrial building design (IIBD), incorporating manufacturing and building disciplines simultaneously, design teams are faced with the choice of multiple conflicting criteria and complex design decisions, opening up a huge design space. To address these issues, this paper presents a parametric design process for efficient design space exploration in IIBD. A state-of-the-art survey and multiple case study are conducted to define four novel flexibility metrics and to develop a unified design space, respecting both building and manufacturing requirements. Based on these results, a parametric design process for automated structural optimization and quantitative flexibility assessment is developed, guiding the decision-making process towards increased sustainability. The proposed framework is tested on a pilot-project of a food and hygiene production, evaluating the design space representation and validating the flexibility metrics. Results confirmed the efficiency of the process that an evolutionary multi-objective optimization algorithm can be implemented in future research to enable multidisciplinary design optimization for flexible industrial building solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Design space exploration for flexibility assessment and decision making support in integrated industrial building design

2021

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“…Advanced modeling and simulation technologies, including BIM, parametric modeling, cloud-based simulation, and optimization algorithms have the potential for automated generation, evaluation, and optimization of multiple building design options [69]. There is a gap of digital solutions for quantitative planning success evaluation and systematic decision support models in factory planning [28]. Hence, the design of flexible industrial buildings requires a powerful computational model for multi-objective design optimization that integrates interdependent parameters and supports interdisciplinary decision-making.…”

Section: Discussionmentioning

confidence: 99%

“…Numerous research studies have been conducted regarding optimization and decision-support tools for manufacturing systems. Büscher et al [28] presented the concept of virtual production intelligence for an integrative information system, enabling planners to integrate, to aggregate and to analyze data gathered during planning projects. Francalanza et al [29] developed a knowledge-based decision-making approach for designing changeable manufacturing systems.…”

Section: Decision-making Support In Iibdmentioning

confidence: 99%

“…However, the models, data and processes of the disciplines involved in planning and operating of factories lack interoperability and are kept in discipline-specific silo thinking [27]. Regarding optimization and decision-support tools in factory planning, prior research has mostly been focusing on optimization on product-or manufacturing process level [28,29], energy efficiency in production [30,31] or sustainable manufacturing [32,33] and paid less attention to the integration of building structure or -services information [8]. Several authors proposed models concentrating on the industrial building, evaluating the environmental performance of building elements through lifecycle assessment [34][35][36] or optimizing the buildings' energy performance [37,38].…”

Section: Introductionmentioning

confidence: 99%

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Design Guideline for Flexible Industrial Buildings Integrating Industry 4.0 Parameters

2021

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The emergence of Industry 4.0 can contribute to sustainable development, but most concepts have not yet received much attention in industrial building design. Industry 4.0 aims to realize production in batch size of one and product individualization on demand. Constant reconfiguration and expansion of production systems demand highly flexible building structures to prolong service life and reduce economic and environmental impacts. However, most research and tools focus on either production system or building optimization. There is a lack of holistic approaches that combine these two aspects. This paper presents a systematic design guideline for flexible industrial buildings towards the requirements of Industry 4.0, integrating building and production planning. The methodology employs literature research and a multiple case study based on expert interviews. The design guideline is presented in the form of a categorized parameter catalogue that classifies the results, on the one hand, into the levels of (O) objectives, (T) technical parameters and (P) planning process, and on the other hand, into (S) success factors, (I) suggestions for improvement and (D) deficits. The findings identify flexibility, structural design parameters and an integrated computational design approach at early design stage as potential success factors for integrated industrial building design (IIBD). The results set the basis to develop a multi-objective optimization and decision-making support tool for IIBD in future research.

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