Zapata, BMW and MIT’s The Center for Quantum Engineering Publish Research On Optimizing Vehicle Production Planning Using Quantum-Inspired Generative AI Techniques

Simulations Show Improved Efficiency of Production Lifecycle

Zapata Computing, the software company building solutions to enterprises’ most computationally complex problems, announced the publication of a joint paper detailing work done with BMW as part of their membership in MIT’s The Center for Quantum Engineering (CQE). The collaboration uncovered new methods to optimize vehicle production using quantum-inspired generative AI techniques. Zapata and BMW’s membership in the CQE Consortium is in part to support the work of MIT students and researchers. For this project, the parties have developed and tested simulations that show how Zapata’s Generator-Enhanced Optimization (GEO) technique can quickly and efficiently optimize BMW’s complex vehicle production schedule across multiple plants. In many cases, GEO outperformed state-of-the-art solvers in minimizing assembly line idle time while maintaining monthly vehicle production targets. This work was done on Zapata’s Orquestra^® software platform.

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“That’s precisely why we created our Quantum Science and Engineering Consortium (QSEC) – connecting the best and brightest from the academic landscape with industry partners to solve real-world problems.”

“The problem that BMW presented to our team is an excellent quantum computing use case that addresses an incredibly complex, real-world challenge of commercial interest,” said Dr. William D. Oliver, Professor of Electrical Engineering and Computer Science and of Physics at MIT, and Director of The Center for Quantum Engineering. “That’s precisely why we created our Quantum Science and Engineering Consortium (QSEC) – connecting the best and brightest from the academic landscape with industry partners to solve real-world problems.”

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“At BMW, we’re always looking for new, innovative ways to drive operational efficiency at our manufacturing plants,” said Marcin Ziolkowski, Emerging Technologies Manager at BMW Group. “As you might imagine, optimizing our production schedule is an incredibly complex and unique challenge. There are a wide range of possible configurations and a high number of constraints, including varying production rates between shops, a discrete set of shift schedules, and the need to prevent overflows and shortages in the buffers between steps in the manufacturing process. This initiative aligns perfectly with MIT’s broader research and educational mission. Working with Zapata and CQE, we were able to prove that GEO outperforms other techniques in production planning.”

“We ran roughly a million optimization runs cycling through dozens of various algorithms, problem configurations and optimizer solutions to benchmark their performance against each other,” said Yudong Cao, CTO and co-founder at Zapata Computing. “GEO uses quantum or quantum-inspired generative machine learning models to learn from and improve upon the results generated by classical solvers. As we worked on new ways to address this challenge, we kept MIT’s mission front-of-mind – ensuring that we were helping to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the 21st century.”

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