Solving an one-dimensional cutting stock problem by simulated annealing and tabu search

Abstract

A cutting stock problem is one of the main and classical problems in operations research that is modeled as Lp < /div><br /> problem. Because of its NP-hard nature, finding an optimal solution in reasonable time is extremely difficult and at<br /> least non-economical. In this paper, two meta-heuristic algorithms, namely simulated annealing (SA) and tabu<br /> search (TS), are proposed and developed for this type of the complex and large-sized problem. To evaluate the<br /> efficiency of these proposed approaches, several problems are solved using SA and TS, and then the related results<br /> are compared. The results show that the proposed SA gives good results in terms of objective function values<br /> rather than TS.