A GRASP METAHEURISTIC FOR THE ORDERED CUTTING STOCK PROBLEM
This study presents a new mathematical model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic to solve the ordered cutting stock problem. The ordered cutting stock problem was recently introduced in literature. It is appropriate to minimize the raw material used by industries...
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| Autores principales: | , , |
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| Lenguaje: | English |
| Publicado: |
Universidad de Tarapacá.
2008
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052008000300005 |
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| Sumario: | This study presents a new mathematical model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic to solve the ordered cutting stock problem. The ordered cutting stock problem was recently introduced in literature. It is appropriate to minimize the raw material used by industries that deal with reduced product inventories, such as industries that use the just-in-time basis for their production. In such cases, classic models for solving the cutting stock problem are useless. Results obtained from computational experiments for a set of random instances demonstrate that the proposed method can be applied to large industries that process cuts on their production lines and do not stock their products. |
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