Alexander Lambrianou
BASc Year 3
How Can A Computational Model Guide the Optimisation of Interdisciplinary Team Design
AI
Future of Work
Technology

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Alexander Lambrianou
Summary
This study explores a new way of designing interdisciplinary teams to tackle complex real-world problems. Using Heathrow Airport’s ambitious sustainability goals as a pilot case, it asks not just who works together, but how the knowledge they bring fits together. Instead of focusing on job titles or social connections, the approach looks at the actual ideas and themes different disciplines contribute. By comparing these with strategies that have already proven successful, the model highlights which disciplines add the most value and how they can best complement one another.
The findings show that effective teams are not simply made up of the most aligned disciplines, but those whose strengths combine in ways that cover gaps and create balance.
The findings show that effective teams are not simply made up of the most aligned disciplines, but those whose strengths combine in ways that cover gaps and create balance.
Approach and Methodology
This study began with a pressing problem: Heathrow Airport’s challenge of reducing its substantial carbon emissions while moving toward ambitious sustainability goals. Rather than approaching this issue from a single disciplinary perspective, the research focused on how different forms of expertise could be combined to design more effective interdisciplinary teams.
The methodology followed an exploratory sequential mixed-methods design. The qualitative stage began with a thematic analysis of benchmark case studies from airports around the world, identifying key strategies that had been successfully used to cut emissions. Heathrow’s own sustainability report provided a starting point for creating a codebook of themes, which was then expanded through inductive coding of benchmark sources. This ensured the analysis was both contextually relevant and open to new insights.
The quantitative stage translated this qualitative data into thematic vectors that could be systematically compared. GPT-generated action plans representing distinct disciplines (e.g., Economics, Engineering, Environmental Science) were coded using the benchmark themes. The epistemic alignment of each discipline with successful strategies was measured using KL divergence, before constrained optimisation and the ASHA algorithm were applied to test how different disciplinary combinations performed under realistic constraints.
This process showed that while some disciplines aligned closely with benchmarks on their own, their greatest value emerged in strategic synthesis. The methodology not only revealed which perspectives mattered, but how they could complement one another to form robust, well-balanced teams capable of addressing complex challenges.
The methodology followed an exploratory sequential mixed-methods design. The qualitative stage began with a thematic analysis of benchmark case studies from airports around the world, identifying key strategies that had been successfully used to cut emissions. Heathrow’s own sustainability report provided a starting point for creating a codebook of themes, which was then expanded through inductive coding of benchmark sources. This ensured the analysis was both contextually relevant and open to new insights.
The quantitative stage translated this qualitative data into thematic vectors that could be systematically compared. GPT-generated action plans representing distinct disciplines (e.g., Economics, Engineering, Environmental Science) were coded using the benchmark themes. The epistemic alignment of each discipline with successful strategies was measured using KL divergence, before constrained optimisation and the ASHA algorithm were applied to test how different disciplinary combinations performed under realistic constraints.
This process showed that while some disciplines aligned closely with benchmarks on their own, their greatest value emerged in strategic synthesis. The methodology not only revealed which perspectives mattered, but how they could complement one another to form robust, well-balanced teams capable of addressing complex challenges.
Proposal/Outcome
The outcome of this project was the creation of a proof-of-concept tool for interdisciplinary team optimisation, demonstrated through both an academic paper and a professional pitch. The product combined rigorous research with a practical communication strategy: the academic paper set out the conceptual and methodological foundations, while the pitch presented a concise, accessible case to Heathrow’s Director of Carbon Strategy. Together, these outputs show how academic insight can be translated into a product with real-world applicability.
The framework produced not only identified optimal team compositions for Heathrow’s sustainability goals but also provided a clear narrative and strategic recommendation that could guide decision-making. This dual outcome—an academic contribution and a professional proposal—ensured that the research moved beyond theory into practical utility.
The project demonstrated that effective teams are not simply built from the strongest individual disciplines, but from complementary ones. It highlighted the limitations of assembling teams based on tradition or intuition alone and offered a new, data-driven approach to design. Ultimately, the conclusion is that complex challenges like decarbonisation demand methods that bridge research and practice, combining technical robustness with strategic insight to create meaningful organisational impact.
The framework produced not only identified optimal team compositions for Heathrow’s sustainability goals but also provided a clear narrative and strategic recommendation that could guide decision-making. This dual outcome—an academic contribution and a professional proposal—ensured that the research moved beyond theory into practical utility.
The project demonstrated that effective teams are not simply built from the strongest individual disciplines, but from complementary ones. It highlighted the limitations of assembling teams based on tradition or intuition alone and offered a new, data-driven approach to design. Ultimately, the conclusion is that complex challenges like decarbonisation demand methods that bridge research and practice, combining technical robustness with strategic insight to create meaningful organisational impact.
Beyond Outcomes
Beyond the immediate findings, this study laid the foundation for a consultancy offering a data-driven approach to interdisciplinary team design. The model developed has been translated into a professional service that helps organisations optimise collaboration for complex challenges, moving beyond intuition or job labels to evidence-based decision-making. Positioned at the intersection of research and practice, the consultancy demonstrates how academic insight can be scaled into a strategic capability—delivering measurable value for clients facing sustainability, innovation, and other multifaceted problems.
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Here is some student work from their formal assignments. Please note it may contain errors or unfinished elements. It is shared to offer insights into our programme and build a knowledge exchange community.



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How Can A Computational Model Guide the Optimisation of Interdisciplinary Team Design
- A Pilot Based on Heathrow's Sustainability Goals
AI
Future of Work
Technology