A Feedback Learning-Based Memetic Algorithm for Energy-Aware Distributed Flexible Job-Shop Scheduling With Transportation Constraints

With increasing energy concerns and development of globalization, energy-aware scheduling and distributed scheduling have become significant topics in modern manufacturing. However, realistic manufacturing scenarios, such as collaborative scheduling of distributed shops and limited transportation resources, are rarely taken into account. To bridge the gap, this paper addresses the energy-aware distributed flexible job-shop with transportation constraints (EDFJSP-T) and proposes a feedback learning-based memetic algorithm (FLMA) to minimize makespan and total energy consumption simultaneously. First, a mathematical model is formulated to represent the relationship between different sub-problems. Additionally, an encoding and decoding method based on forward insertion is designed to reduce the search space and obtain high-quality schedules. Second, various problem-specific operators are designed to focus on different sub-problems and objectives to enrich search patterns. Third, memetic search with feedback learning is proposed via introducing observer indexes for both population state and individual state to adaptively match appropriate operators for individuals. Besides, local intensification search with multiple operators is incorporated for low-density regions to further improve exploitation ability. The parameter setting is investigated and experimental tests are carried out using different types of instances. The comparisons demonstrate the effectiveness of the feedback learning mechanism and the superiority of the FLMA over existing algorithms for solving the EDFJSP-T.