Ant Colony Optimisation for Backward Production Scheduling. (19th September 2012)

• Record Type: Journal Article
• Authors: Santos, Leandro Pereira dos; Vieira, Guilherme Ernani; Leite, Higor Vinicius dos R.; Arns Steiner, Maria Teresinha
• Other Names: Puangdownreong Deacha Academic Editor.
• Is Part Of: Advances in artificial intelligence. Volume 2012(2012)
• Publication Date: 2012-09-19
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1155/2012/312132
• Languages: English
• ISSNs: 1687-7470

Abstract

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.