An Efficient Heuristic Algorithm for Flexible Job-Shop Scheduling Problem with Due Windows

Constructing intelligent manufacturing systems and optimizing scheduling management is of economic significance. To apply the flexible job-shop scheduling problem to specific manufacturing fields, such as the food fermentation industry and aerospace industry, in which each product is required to be delivered within its due window, this paper establishes the mixed-integer nonlinear programming model for the flexible job-shop scheduling problem with due windows and develops a bi-level closed-loop heuristic algorithm to minimize the weighted earliness or tardiness. In the upper level, the genetic algorithm is used to optimize each job's processing path and sequence iteratively. In the lower level, the sequential quadratic programming determines the start time for each operation of each job by solving a nonlinear programming problem. The convergence of the algorithm is analyzed. Experimental results show that the algorithm can efficiently solve the optimal solution or approximate optimal solution of the problem. As a general idea of dividing integer and continuous solution space, the algorithm is robust to the field and details of the problem.