Collaborative scheduling of energy-saving spare parts manufacturing and equipment operation strategy using a self-adaptive two-stage memetic algorithm

Previous studies on production scheduling predominantly focus on developing optimal production plans in the context of deterministic customer requirements, achieving certain optimization goals related to the manufacturer. However, little research has been conducted on scheduling customized spare parts manufacturing on the supply side and equipment operation strategy on the demand side simultaneously. In real-world engineering projects, an efficient collaborative scheduling solution not only helps the manufacturer develop optimal production plans for critical spare parts, but also guides equipment users to achieve maximum output. Therefore, this paper studies a collaborative scheduling problem that both considers energy-saving spare parts manufacturing in a flexible job shop and equipment operation strategy of distributed users. To solve this problem effectively, a self-adaptive two-stage memetic algorithm (STMA) is proposed to minizine total energy consumption of the manufacturer and maximize total operation utility of equipment simultaneously. In detail, four heuristic rules are designed to generate a high-quality initial population. In the first stage, the crossover and mutation are utilized for global exploration, and a self-adaptive local search operator is presented to enhance the local development ability of the STMA. In the second stage, several energy-saving and utility-improving strategies are proposed to further optimize the Pareto front solutions generated in the previous period. Extensive experiments are conducted, and the numerical results indicate that STMA is the most promising compared to other well-known algorithms.