The greedy randomized adaptive search procedure method in formulating set covering model on cutting stock problem

Abstract

This study aims to apply the Greedy Randomized Adaptive Search Procedure method in formulating the Set Covering model. Selected cutting patterns in the Cutting Stock Problem are generated by the Greedy Randomized Adaptive Search Procedure method and then formulating them to the Set Covering model. This study used a single stock with four types of items. The Greedy Randomized Adaptive Search Procedure method can show the maximum number of cutting patterns with minimum trim loss. Based on data analysis, it can be concluded that the Greedy Randomized Adaptive Search Procedure method will give different patterns depends on the number of the upper limits of demand. The Set Covering model, which was solved by LINGO 13.0, showed the optimal cutting patterns with minimum trim loss, but still has the lack of the product in one of the items.