Solving binary cutting stock with matheuristics using particle swarm optimization and simulated annealing

摘要

In last decade, researchers have focused on improving existing methodologies through hybrid algorithms; these are a combination of algorithms between a metaheuristic with other metaheuristic and an exact method, to solve combinatorial optimization problems in the best possible way. This work presents a benchmark of different methodologies to solve the binary cutting stock problem using a column generation framework, this framework is divided into master and subproblem, master problem is solved using a classical integer linear programming, and the subproblem is solved using metaheuristic algorithms (genetic algorithm, simulated annealing and particle swarm optimization). This benchmark analysis is aimed to compare hybrid metaheuristics results with an exact methodology.