A Novel Metaheuristic Hybrid Parthenogenetic Algorithm for Job Shop Scheduling Problems: Applying an Optimization Model

Metaheuristics are primarily developed to explore optimization techniques in many practice areas. Metaheuristics refer to computational procedures leading to finding optimal solutions to optimization problems. Due to the increasing number of optimization problems with large-scale data, there is an ongoing demand for metaheuristic algorithms and the development of new algorithms with more efficiencies and improved convergence speed implemented by a mathematical model. One of the most popular optimization problems is job shop scheduling problems. This paper develops a novel metaheuristic hybrid Parthenogenetic Algorithm (NMHPGA) to optimize flexible job shop scheduling problems for single-machine and multi-machine job shops and a furnace model. This method is based on the principles of genetic algorithm (GA), underlying the combinations of different types of selections, proposed ethnic GA, and hybrid parthenogenetic algorithm. In this paper, a parthenogenetic algorithm (PGA) combined with ethnic selection GA is tested; the parthenogenetic algorithm version includes parthenogenetic operators: swap, reverse, and insert. The ethnic selection uses different selection operators such as stochastic, roulette, sexual, and aging; then, top individuals are selected from each procedure and combined to generate an ethnic population. The ethnic selection procedure is tested with the PGA types on a furnace model, single-machine job shops, and multi-machines with tardiness, earliness, and due date penalties. A comparison of obtained results of the established algorithm with other selection procedures indicated that the NMHPGA is achieving better objective functions with faster convergence speed.