A Discrete Grey Wolf Optimizer for Solving Flexible Job Shop Scheduling Problem with Lot-streaming

A flexible job shop scheduling problem with lot-streaming (LSFJSP) is studied in this work. Lot-streaming means dividing all jobs in an order into several independent sublots and each sublot contains a certain number of jobs from the order. A discrete grey wolf optimizer (DGWO) is proposed for solving LSFJSP. In the algorithm, an encoding method with two strings including task string and sublot string is designed based on the characteristics of LSFJSP. And a positive decoding method combined with the rules of first come first served (FCFS) and shortest process time (SPT) is developed to reduce the solution space. Several strategies including population guide mechanism, critical task local search, and wolf random walk are adopted in the DGWO to make the algorithm suitable to solve LSFJSP. The results of computational experiments on 120 instances show that the DGWO is an competitive algorithm for LSFJSP.