文章基本信息

标题：Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model
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作者：Eduardo Guzman ; Beatriz Andres ; Raul Poler 等
期刊名称：Computers
电子版ISSN：2073-431X
出版年度：2022
卷号：11
期号：1
页码：1
DOI：10.3390/computers11010001
语种：English
出版社：MDPI Publishing
摘要：This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses.