Authors:
Ramiro Varela
1
;
César Muñoz
1
;
María Sierra
1
and
Inés González-Rodríguez
2
Affiliations:
1
University of Oviedo, Spain
;
2
University of Cantabria, Spain
Keyword(s):
Multi-objective optimization, Genetic Algorithms, Cutting Stock, Meta-heuristics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Enterprise Software Technologies
;
Intelligent Problem Solving
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Software Engineering
;
Symbolic Systems
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 multi-objective 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.