The joint replenishment problem (JRP) presents a complex optimisation challenge where the objective is to determine coordinated ordering policies for multiple items, thereby minimising total costs ...

Right now, quantum computers are small and error-prone compared to where they’ll likely be in a few years. Even within those limitations, however, there have been regular claims that the hardware can ...

The travelling salesman problem (TSP) remains one of the most challenging NP‐hard problems in combinatorial optimisation, with significant implications for logistics, network design and route planning ...

Research teams from energy giant ExxonMobil and IBM have been working together to find quantum solutions to one of the most complex problems of our time: managing the tens of thousands of merchant ...

In 1994, a mathematician figured out how to make a quantum computer do something that no ordinary classical computer could. The work revealed that, in principle, a machine based on the rules of ...

Since the very first days of computer science — a field known for its methodical approach to problem-solving — randomness has played an important role. The first program to run on the world’s first ...

AlphaEvolve uses large language models to find new algorithms that outperform the best human-made solutions for data center management, chip design, and more. Google DeepMind has once again used large ...

The ultimate goal of every software product is to convert inputs(provided by end-users or automatically received from external systems) into valuable outputs ...