Simulation of Job Sequencing for Stochastic Scheduling with a Genetic Algorithm

Abstract

Sequencing is done to determine the order in which the jobs are to be processed. Extensive research has been carried out with an aim to tackle real-world scheduling problems. In industries, experimentation is performed before an ultimate choice is made to know the optimal priority sequencing rule. Therefore, an extensive approach to selecting the correct choice is necessary for the management decisionmaking perspective. In this research, the genetic algorithm (GA) and working of a simulation environment are explained, in which a scheduling operator, under any given circumstances, can obtain the appropriate sequence for job scheduling in a shop. The paper also explains the stochastic based linguistic, scenarios and probabilistic approaches to solve sequencing problem. The simulation environment allows the operator to select the tardiness and non-tardiness related performance measures. The simulator takes input values such as number of jobs, processing time and due date and discovers a near-optimal sequence for scheduling of jobs that minimizes the performance measures selected by the operator as per requirement. The case study considered is solved using scenarios based stochastic scheduling approach and results are shown. The results are compared with the classical method used in the company and observed that the proposed approach gives a better result.

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

“…This starting population will affect the solutions and the time taken to obtain the optimal solution. The GA described by Bari et al (2022) in their research work is also employed to compare the proposed algorithm. This classical GA is improved by introducing dispatching rules related to processing time and due date in the initial population of the DRCEC algorithm.…”