Applying a hybrid artificial immune systems to the job shop scheduling problem

Abstract

In today’s economy, manufacturing sectors are challenged by high costs, low revenues. As part of the managerial activities, scheduling plays an important role in optimizing cost, revenue, profit, time, and efficiency by optimization of available resources. The objective of this research is to evaluate the existing artificial immune system (AIS) principles, models, and applications, and to develop an algorithm applicable to job shop scheduling problems. The developed algorithm was based on the theories of the positive selection algorithm and the clonal selection principle. To test the algorithm, ten job shop scheduling problems were evaluated using the new AIS model. To validate the results, the same job scheduling problems were evaluated using a genetic algorithm (GA) model. The results of the two evaluations were compared against each other using the dimensions of optimality and robustness. The testing revealed that the AIS model was slightly less competitive than the GA model in the optimality test but beat the GA in robustness. Another key finding was that the robustness of the model increased as the best solutions produced by the model were closer to the known optimal.