A Genetic Algorithm Approach to Parallel Machine Scheduling Problems Under Effects of Position-Dependent Learning and Linear Deterioration: Genetic Algorithm to Parallel Machine Scheduling Problems

This paper investigates parallel machine scheduling problems where the objectives are to minimize total completion times under effects of learning and deterioration. The investigated problem is in NP-hard class and solution time for finding optimal solution is extremely high. The authors suggested a genetic algorithm, a well-known and strong metaheuristic algorithm, for the problem and we generated some test problems with learning and deterioration effects. The proposed genetic algorithm is compared with another existing metaheuristic for the problem. Experimental results show that the proposed genetic algorithm yield good solutions in very short execution times and outperforms the existing metaheuristic for the problem.

Automated summary

This study investigates parallel machine scheduling problems with the objective of minimizing total completion times under the effects of learning and deterioration. The authors proposed a genetic algorithm as a solution, which proved to yield good solutions in short execution times and outperform existing metaheuristic algorithms for the problem.