A General Model for Job Shop Problems using Imune-Genetic Algorithm and Multiobjective Optimization Techniques

We define a global model to simulate the characteristics of three kinds of the manufacturing systems with transport resources. Based on this model, we use an immune-based genetic algorithm to solve the associated scheduling problems. We take the makespan and minimum storage as the two objectives and use modified Pareto ranking method to solve this problem. We show how to choose the best solutions for the studied systems. Though not all the constraints of the real systems are considered until now, the computational results show that our proposed model and algorithm have efficiencies in solving scheduling problems.