A Multi-Objective Genetic Algorithm for Cutting-Stock in Plastic Rolls Industry

Abstract

In this paper, we confront a variant of the cutting-stock problem with multiple objectives. It is an actual problem of an industry that manufactures plastic rolls under customers' demands. The starting point is a solution calculated by a heuristic algorithm, termed SHRP that aims mainly at optimizing the two main objectives, i.e. the number of cuts and the number of different patterns; then the proposed multi-objective genetic algorithm tries to optimize other secondary objectives such as changeovers, completion times of orders weighted by priorities and open stacks. We report experimental results showing that the multiobjective genetic algorithm is able to improve the solutions obtained by SHRP on the secondary objectives and also that it offers a number of non dominated solutions, so that the expert can chose one of them according to his preferences at the time of cutting the orders of a set of customers.