Integrated scheduling of green production with flexible preventive maintenance and customer-side workers by a learning-based coevolutionary algorithm

Abstract

Previous production scheduling studies on integrating preventive maintenance (PM) plans have overlooked the impact of arranging customer-side workers on the coupling of spare part delivery times, potentially leading to inefficient solutions. To address this gap, this study expands an integrated scheduling model of green two-stage hybrid flowshop production with flexible PM mode and customer-side workers for spare part replacement services. The model arranges tasks for the customer-side workers based on the relationship between delivery timetables, worker selection, replacement sequences, and equipment due time windows, aiming to maximize the total customer satisfaction. Another objective is to minimize the total energy consumption during production, maintenance and idle processes. To solve the large-scale instances, a double deep Q-network-based coevolutionary algorithm (shorten to DDQCA) is proposed, incorporating a six-layer chromosome encoding scheme. In DDQCA, double deep Q-networks are trained online to guide the selection of crossover methods for coevolution. Additionally, the DDQCA incorporates a hybrid initialization operator, two objectives-oriented local search methods and a proposition-based PM strategy to enhance search performance. Finally, comprehensive experiments are conducted to validate the effectiveness of the algorithm. In addition, the results also demonstrate that the proposed integrated scheduling with flexible PM mode can improve energy efficiency but significantly customer satisfaction compared to the classical mode.