An alternative hybrid evolutionary technique focused on allocating machines and sequencing operations

• Main Authors: Mariano Frutos, Fernando Tohmé, Fernando Delbianco, Fabio Miguel
• Format: Article
• Language: English
• Published: Growing Science 2016-09-01
• Series: International Journal of Industrial Engineering Computations
• Subjects: Flexible job-shop scheduling problem; Optimization; Multi-objective hybrid Evolutionary algorithm; Production
• Online Access: http://www.growingscience.com/ijiec/Vol7/IJIEC_2016_12.pdf

We present here a hybrid algorithm for the Flexible Job-Shop Scheduling Problem (FJSSP). This problem involves the optimal use of resources in a flexible production environment in which each operation can be carried out by more than a single machine. Our algorithm allocates, in a first step, the machines to operations and in a second stage it sequences them by integrating a Multi-Objective Evolutionary Algorithm (MOEA) and a path-dependent search algorithm (Multi-Objective Simulated Annealing), which is enacted at the genetic phase of the procedure. The joint interaction of those two components yields a very efficient procedure for solving the FJSSP. An important step in the development of the algorithm was the selection of the right MOEA. Candidates were tested on problems of low, medium and high complexity. Further analyses showed the relevance of the search algorithm in the hybrid structure. Finally, comparisons with other algorithms in the literature indicate that the performance of our alternative is good.