Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model

Abstract

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.

Modified Coral Reef Optimization Methods for Job Shop Scheduling Problems

“…Modern metaheuristic algorithms also incorporate various mathematical models and analytical operational techniques to deliver greater performance. Guzman et al propose a metaheuristic algorithm that combines GA with a disjunctive mathematical model and employs the open-source solution Coin-OR Branch and Cut to optimize the JSSP [12]. By combining an open-source solver with genetic algorithm, the metaheuristic approach enables the development of efficient solutions and reduces computation time.…”

“…Metaheuristic algorithms employ innovative search strategies to explore the solution space and avoid getting stuck in local optima by steering the feasible solution with a bias, enabling the rapid generation of high-quality solutions. Contemporary metaheuristic algorithms also combine with different mathematical models [12] and analytical operating procedures [13] to enhance performance.…”