Evolutionary framework for two-stage stochastic resource allocation problems
This work addresses resource allocation under uncertainty for operations research, but it is incremental as it applies known metaheuristics to a specific problem generalization.
The paper tackles two-stage stochastic resource allocation problems by proposing an evolutionary framework that combines local search and a genetic metaheuristic to minimize total expected cost, demonstrating its effectiveness on large instances of the Steiner tree problem.
Resource allocation problems are a family of problems in which resources must be selected to satisfy given demands. This paper focuses on the two-stage stochastic generalization of resource allocation problems where future demands are expressed in a finite number of possible scenarios. The goal is to select cost effective resources to be acquired in the present time (first stage), and to implement a complete solution for each scenario (second stage), while minimizing the total expected cost of the choices in both stages. We propose an evolutionary framework for solving general two-stage stochastic resource allocation problems. In each iteration of our framework, a local search algorithm selects resources to be acquired in the first stage. A genetic metaheuristic then completes the solutions for each scenario and relevant information is passed onto the next iteration, thereby supporting the acquisition of promising resources in the following first stage. Experimentation on numerous instances of the two-stage stochastic Steiner tree problem suggests that our evolutionary framework is powerful enough to address large instances of a wide variety of two-stage stochastic resource allocation problems.