Multi-Objective Integrated Energy-Efficient Scheduling of Distributed Flexible Job Shop and Vehicle Routing by Knowledge-and-Learning-Based Hyper-Heuristics

Currently, supply chain operations face enormous challenges due to complex manufacturing processes and distribution activities. This work proposes a multi-objective integrated energy-efficient scheduling and routing method for a distributed flexible job shop with multiple vehicles to minimize job completion time, total energy consumption, and workload of factories. Firstly, a mixed integer programming model is formulized. Secondly, a knowledge-and-learning-based hyper-heuristic algorithm is developed to solve the model. It innovatively incorporates a Q-learning method to choose a search method from a pool containing genetic algorithm, artificial bee colony optimizer, brain storm optimizer and Jaya algorithm. Furthermore, it embeds problem-specific knowledge into the devised method, aiming to further refine obtained solutions. Finally, the formulated model and proposed algorithm's performance are verified by exact solver CPLEX. The algorithm is further compared with three state-of-the-art optimization approaches. The results confirm its superiority over them in solving the studied problem.