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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Most industrial buildings have a very short lifespan due to frequently changing production processes. The load-bearing structure severely limits the flexibility of industrial buildings and is a major contributor to their costs, carbon footprint and waste. This paper presents a parametric optimization and decision support (POD) model framework that enables automated structural analysis and simultaneous calculation of life cycle cost (LCC), life cycle assessment (LCA), recycling potential and flexibility assessment. A method for integrating production planning into early structural design extends the framework to consider the impact of changing production processes on the footprint of building structures already at an early design stage. With the introduction of a novel grading system, design teams can quickly compare the performance of different building variants to improve decision making. The POD model framework is tested by means of a variant study on a pilot project from a food and hygiene production facility. The results demonstrate the effectiveness of the framework for identifying potential economic and environmental savings, specifying alternative building materials, and finding low-impact industrial structures and enclosure variants. When comparing the examined building variants, significant differences in the LCC (63%), global warming potential (62%) and flexibility (55%) of the structural designs were identified. In future research, a multi-objective optimization algorithm will be implemented to automate the design search and thus improve the decision-making process.

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.