Solving Flexible Job-Shop Insertion Problem by Improved Grey Wolf Optimization
In order to solve the order insertion problem in flexible job-shop,a rescheduling model with maximum completion time,total energy consumption,total delay time and equipment load as objective functions was established,and the four objective functions were normalized using the linear weighted sum method,and an improved grey wolf optimization(IGWO)was proposed as a global optimization algorithm.Firstly,high-quality initial population was generate using discrete integer encoding and mixed initialization rules.The nonlinear convergence factor was introduced to balance the global search and local exploitation of the algorithm.Simultaneously internal wolf pack individual evolution and external wolf pack invasion mechanisms was introduced to increase the search breadth of the algorithm and avoid the algorithm falling into"precocity".An event driven strategy was adopted to reschedule the unprocessed processes after the order insertion point.Finally,verified through production examples.The results indicate that IGWO is effective and stable in solving the flexible job-shop order insertion rescheduling problem.
job-shop schedulinggrey wolf optimizationreschedulenonlinear convergence factorindividual evolution of wolvesforeign competitive strategie