Multi-Objective Scheduling Simulation of Adaptive Job Shop based on Modified SOMA Algorithm

In this paper, an effective modified Self-Organization Migrating Algorithm (SOMA) is proposed to solve multi-objective adaptive job shop scheduling with the criterion to minimize the processing cost, minimize makespan, minimize the total machine loads. The modified SOMA stresses the balance between global exploration and local exploitation by introduce adaptive step, and improved the population diversity in the process of individual migration by introduce quadratic interpolation, which effectively avoided the premature and improved the convergence of the SOMA. Finally, through the 4×6 job shop scheduling problem verify the performance of the modified SOMA algorithm, the computational results show that the proposed modified SOMA efficiently solves adaptive job shop scheduling.