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Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 312132, 12 pages
http://dx.doi.org/10.1155/2012/312132
Research Article

Ant Colony Optimisation for Backward Production Scheduling

1Instituto Federal do Parana, Assis Chateaubriand, 80230-150 Curitiba, PR, Brazil
2Petrobras S.A., 41770-395 Salvador, BA, Brazil
3Doutor Jose Peroba 225, Apartment no. 1103, 41.770-235 Salvador, BA, Brazil
4Department of Industrial Engineering, Pontifical Catholic University of Parana, 80215-901 Curitiba, PR, Brazil
5Department of Management, Universidade Tecnológica Federal do Paraná, 80230-901 Curitiba, PR, Brazil

Received 7 May 2012; Accepted 31 July 2012

Academic Editor: Deacha Puangdownreong

Copyright © 2012 Leandro Pereira dos Santos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The main objective of a production scheduling system is to assign tasks (orders or jobs) to resources and sequence them as efficiently and economically (optimised) as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO) in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.