reoptimace

Reoptimization is a process in operations research and computer science that involves improving the quality of a solution to a previously solved optimization problem, without having to start over from scratch. It is often used in applications where the constraints or objective function of the problem change over time, such as in production planning, logistics, or financial portfolio optimization.

Formally, reoptimization can be defined as the problem of finding a new solution that is at least as good as the current best known solution, subject to a modified set of constraints or objective function. The goal is to find a new solution that is better or at least as good as the current best known solution, without having to re-solve the entire problem from scratch.

Reoptimization techniques often rely on iterative improvement methods, which start from an initial solution and progressively improve it through a series of steps. These methods can take advantage of the existing solution to reduce the computational effort required to find a new solution. They can also be combined with other optimization techniques, such as local search or metaheuristics.

Reoptimization has applications in various fields, including logistics, finance, and energy trading. For example, in logistics, reoptimization can be used to adjust the delivery schedule of a fleet of trucks in response to changes in traffic congestion or other external factors. In finance, reoptimization can be used to adjust the composition of a portfolio in response to changes in market conditions or other external factors. The benefits of reoptimization include reduced computational effort, faster response times, and improved solution quality.