Mass Variety and Small Batch Scheduling in the Flexible Job Shop

Flexible job shop scheduling with the batch factor under consideration is a NP-hard problem. A multi-objective integrated genetic algorithm (MIGA) is proposed to solve both the assignment of machines to operations and the scheduling of operations on the assigned machines at the same time. MIGA uses random weights to convert the multi-objective problem to a single problem. An operation-extended coding method is presented. The elitist strategy and the niche technology are integrated into the roulette selection operation to speed up the convergence and to improve the diversity of the population respectively. A new scheduling-batch oriented active decoding method is designed. Finally, some benchmark problems are experimented. The results prove that MIGA can solve mass variety and small batch scheduling problem in the flexible job shop effectively and efficiently.