A job assignment scheduling algorithm with variable sublots for lot-streaming flexible job shop problem based on NSGAII

The flexible job shop scheduling problem has gained lots of attention from scholars because of its high flexibility. The lot-streaming scheduling strategy with its advantages of high resource utilization and reduced response time has gradually become an important solution to the flexible job shop scheduling problem at this stage, but it assumes that the number of available processing equipment is not limited. However, the number of equipment that can be processed in parallel for a single process in the actual production of small and medium-sized enterprises is limited. To address the issue, a multi-objective optimal scheduling method for variable sublots flexible shops involving switching time and switching operator restrictions is proposed in this paper, which can be described as based on the principle of job balancing the rational selection of the number of machines and jobs allocation (JBJA) to maximize the machine utilization while minimizing the total processing time. In this paper, we propose adaptive job scheduling NSGAII (AJS-NSGAII) which contains a hidden type encoding, a decoding method using JBJA strategy, and a hybrid rule to initialize the population and solve 15 orders. The experimental findings verify that this algorithm is capable of ensuring the actual production requirements of the enterprise, its multi-machine selection rules are effective and its overall performance is superior.