Solving the Workshop Production Planning Problem Using the Meta-Heuristic Algorithm

Workshop (cellular) production systems are one of the applications of group technology in industry, the purpose of which is to benefit from the physical or operational similarity of products in various aspects of manufacturing and design. Today, the use of workshop production systems and the use of its benefits as one of the ways to increase the speed of the organization's response to rapid market changes, has received much attention. In this paper, a meta-heuristic algorithm based on a composition of genetic and greedy algorithms is used to optimize and evaluate the performance indicators of workshop production planning systems. To improve the effectiveness of the genetic algorithm, the initial population is generated by a greedy algorithm and several elite operators are used to improve the solutions. The greedy approach to improving the create an initial population prioritizes the cells and tasks in each cell and produces quality solutions accordingly. In order to evaluate performance quality of the proposed method, the P-FJSP dataset and quality, scatter, distance and time indices in a multi-objective function have been used. The experimental results show better performance of the proposed approach compared to NRGA and NSGA-II algorithms.