Cracking the code for the perfect restaurant table layout

[April 2025, Reading, UK]: A breakthrough study by University of Bath graduate Jatin Yadav could change the way restaurants manage their table layouts, helping venues maximise efficiency and revenue. 

Jatin’s dissertation, part of his Master of Computing in Computer Science degree, focused on optimising table allocations in restaurants—an issue that many restaurant owners face daily. 

By analysing real booking patterns and applying advanced mathematical models, he has developed a ‘Reallocation Algorithm’ that significantly reduces table downtime and increases seating capacity, all without requiring more space. 

“The challenge for restaurants isn’t just about taking reservations; it’s about making sure every table is used in the best possible way,” explains Jatin. 

“My research has found that minimising the empty slots between bookings is a far more effective method of boosting restaurant profitability than simply increasing the number of diners.” 

Jatin’s work compared various table allocation methods, including First-Fit, Best-Fit, and Next-Fit algorithms. However, his new approach—integrating real-time adaptability and predictive adjustments—outperformed traditional methods. His research suggests that with better reservation reshuffling and table combinability, restaurants could see a near 100% table utilisation. 

For restaurant owners, this means greater efficiency, fewer wasted opportunities, and a stronger bottom line. With the industry still recovering from the financial impacts of COVID-19 and navigating an unpredictable economic climate, Jatin’s findings could provide a much-needed boost to profitability. 

Looking ahead, his research could be integrated into platforms like Favouritetable, enhancing automated booking systems and paving the way for AI-enhanced restaurant management. Machine learning could take optimisation even further, learning from past reservations to predict and allocate tables dynamically. 

With Jatin’s insights already sparking interest in the restaurant tech space, excitement is building about how his work could shape the future of dining reservations. One thing is clear—smarter bookings mean bigger profits. 
 

Cracking the table allocation challenge for restaurants 

Interview by Mark Ferguson, Favouritetable 

When it comes to running a successful restaurant, getting the most out of your tables is critical. But is there a smarter way to optimise seating and maximise revenue? 

Jatin Yadav, a Master of Computing graduate from the University of Bath, believes so. His dissertation research focused on restaurant table allocation, and the results could be game-changing for restaurant owners.  

We sat down with Jatin to discuss his findings, how they could be applied in real-world restaurant management, and the role AI might play in the future. 

Q: What inspired you to focus your dissertation on restaurant table allocation? 

Jatin Yadav: At its core, table allocation is a scheduling problem—one that, if solved efficiently, could be applied to many other industries.  

But I was particularly drawn to the restaurant sector because it’s an area where small operational changes can have a big financial impact. 

When I began researching the topic, I was surprised to find very little existing academic work on it, despite how crucial table management is for restaurant profitability. That made it an exciting and valuable problem to tackle. 

Q: Can you explain how your ‘Reallocation Algorithm’ works and why it performed better than traditional models? 

Jatin: The goal of my algorithm was to mimic how a restaurant manager might manually rearrange bookings to make better use of space. For every new reservation, the algorithm attempts to reshuffle existing bookings to free up capacity. 

It prioritises finding a table that meets size and availability requirements while also minimising empty time gaps before and after bookings. If it can’t find a better arrangement, it defaults to the original allocation—ensuring the worst-case scenario is still functional. 

This approach allowed my model to outperform traditional methods by reducing wasted tables and increasing the number of seated guests. 

Q: What were the biggest surprises or challenges in your research? 

Jatin: The biggest surprise was the lack of academic research on this topic. Given how crucial table management is for restaurant profitability, I expected to find more studies addressing it. 

Another challenge was ensuring that the algorithm didn’t just produce good solutions but did so in real time. A reservation system can’t afford to spend an hour figuring out where to seat a single booking! Balancing efficiency with speed was one of the most important aspects of my work. 

Q: How can restaurant owners practically apply your findings to improve table management and revenue? 

Jatin: Integrating this algorithm into a restaurant management platform would be a natural step. Instead of applying it only when a new booking comes in, restaurants could also run it periodically on fully booked days to see if adjustments could free up more tables. 

The worst case? Nothing changes. The best? A restaurant finds room for extra guests, leading to more revenue without increasing costs. 

Q: You mentioned table combinability—how much of a difference does it make in efficiency? 

Jatin: Combining tables adds complexity because it dramatically increases the number of possible seating arrangements the algorithm needs to consider. 

However, it’s absolutely worth exploring because, in real life, restaurants often push tables together for larger parties. If properly integrated into an optimisation model, table combinability could lead to even more efficient layouts. 

Q: Looking ahead, do you think AI or machine learning could further optimise restaurant layouts? 

Jatin: I’m actually working on this right now as part of my dissertation! AI has a lot of potential, but there are challenges. 

With supervised learning, the model relies on past data—so if previous table allocations were inefficient, the AI might learn bad habits. 

With reinforcement learning, the model improves over time through trial and error, but that can lead to unpredictable results. It’s a fascinating area, and I’m excited to see how it evolves. 

Q: Do you see your research influencing the future of restaurant management software? 

Jatin Yadav: Absolutely. The restaurant industry is becoming increasingly tech-driven, and smarter reservation systems will play a big role in that. 

Integrating AI and optimisation algorithms into platforms like Favouritetable could help restaurants increase efficiency, boost revenue, and improve customer satisfaction. 

Jatin’s research has the potential to reshape restaurant booking systems. With venues constantly looking for ways to maximise revenue without expanding their space, a smarter approach to table allocation might be just what the industry needs.