文章基本信息

标题：Ant Colony Optimisation for Backward Production Scheduling
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作者：Leandro Pereira dos Santos ; Guilherme Ernani Vieira ; Higor Vinicius dos R. Leite 等
期刊名称：Advances in Artificial Intelligence
印刷版ISSN：1687-7470
电子版ISSN：1687-7489
出版年度：2012
卷号：2012
DOI：10.1155/2012/312132
出版社：Hindawi Publishing Corporation
摘要：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.