Energy-efficient scheduling of flexible job shops with complex processes: A case study for the aerospace industry complex components in China

As an effective means to reduce enterprise energy consumption, job-shop scheduling has received extensive attention in the industry. This paper focuses on a real case energy-efficient scheduling problem for aerospace complex components in a flexible job-shop with complex processes. To handle the issues of low production efficiency, high energy consumption and processing cost in the job-shop, an aerospace complex components lot-splitting scheduling model is formulated with the optimization objectives of total energy consumption, makespan, and processing cost for the first time. A novel improved crossover artificial bee colony algorithm is presented to cope with the combinatorial optimization problem in the model. Specifically, a multi-objective process tree algorithm is introduced to address the poor quality of random initial solutions, and the crossover and mutation are designed in the global search of employed bees to improve the population diversity. Moreover, the parameter setting of the proposed algorithm is calibrated by a new assessment metrics ASQ and the DOE Taguchi method. In this way, the Pareto optimal solution set is obtained. Then, a comparative experiment of the proposed algorithm and other famous optimization algorithms including ABC, MOBA, NSGA-II, as well as heuristic scheduling methods including RANDOM+LPP and RANDOM+SPT is designed to verify the energy-saving effect of the proposed model and the feasibility of the algorithm. The results show that the proposed algorithm is a very competitive algorithm for this real-world instance. Finally, the Plant Simulation is used to verify the practicability of the proposed energy-efficient model and algorithm. The simulation results show that the model and algorithm in this work can effectively reduce energy consumption, shorten the production makespan and improve the utilization of various resources in the job shop. The research results of this paper established a quick response, high efficiency, and flexible job-shop scheduling scheme for the aerospace complex components manufacturer, and lay a foundation for other multi-variety, single-piece and small-batch manufacturers to solve similar problems in the future.