An Effective Heuristic Algorithm for Flexible Flow Shop Scheduling Problems with Parallel Batch Processing

In this study, a firm's scheduling problem optimized using the genetic algorithm method and it aimed to reach the schedule that gives the smallest time in the production schedules. Considering the scheduling of the solenoid part produced by the company, a schedule with a shorter production time than the current production time of the part obtained and the production times of the company were improved. A genetic algorithm developed to solve the parallel batch processing problems. The developed genetic algorithm is an effective heuristic algorithm for the flexible flow type problem. Parameter optimization study carried out to improve the solution performance of genetic algorithms. Genetic operators examined in detail and compared with each other, and the most appropriate parameter set was determined because of research and experiments. The best parameters found for each problem with suggested algorithm. In order to reach the optimum solution of the part to produce in the scheduling problem, chromosomes created and sequence sizes randomly assigned. These assigned dimensions are in ascending order and converted to actual rows. Then, the total production times were determined by generating solutions sequentially from the generated chromosomes. © 2023 INFORMS Inst.for Operations Res.and the Management Sciences. All rights reserved.