Challenges in job scheduling optimization lie in maximizing productivity and minimizing idle time across complex systems with varying constraints. This process requires adapting to dynamic workloads and unexpected changes while maintaining overall system efficiency. With GPU acceleration, cuOpt enables businesses to make data-driven job scheduling decisions, optimizing for factors like deadlines, priorities, and resource availability.

Asset allocation in finance involves distributing investment capital across securities to optimize portfolio performance. The challenge lies in balancing risk and return objectives while considering market volatility, economic indicators, and investor preferences. Complexity arises from numerous potential combinations and the need for swift adaptation to changing market conditions.

Fleets of trucks are dispatched from distribution centers to deliver orders to stores and end customer addresses. cuOpt reduces miles driven and accelerates delivery times with the same level of accuracy.

A service provider is dispatched to fulfill a list of different service requests. These are allocated a certain amount of time per service request and the time can vary according to request. For example, a telecommunications technician is assigned to a customer’s home to install a router and then to a different home to install a data cable. cuOpt enables technicians to be equipped with all required tools before departure and complete the tasks based on optimized routes.

For the inbound and outbound transportation of goods and vehicles with long-haul fleets, it’s critical to optimize scheduling and route planning. cuOpt allows users to factor in the number of available pilots, drivers, or ships to recommend the most optimized route and schedule.