Optimal job scheduling to minimize total tardiness by dispatching rules and community evaluation chromosomes

In traditional scheduling, job processing times are assumed to be fixed. However, this assumption may not applicable in many realistic industrial processes. Using the time of real processes instead a fixed value converts deterministic model stochastic one. This study provides three approaches solving problem scheduling: linguistic, scenarios, and probabilistic. A combinatorial algorithm, dispatching rules community evaluation chromosomes (DRCEC) is developed generate an optimal sequence minimize tardiness performance measure scheduling problem. Thirty-five datasets problems generated tested with model. The DRCEC compared Genetic Algorithm (GA) terms total tardiness, tendency convergence, execution time, accuracy. has been discovered outperform GA. computational results show that approach gives response 63 per cent cases near-optimal solution remaining 37 cases. Finally, manufacturing company case demonstrates DRCEC's acceptable performance.