Asynchronous multi-agent based differential evolution for assembly hybrid differentiation flowshop scheduling with variable sub-lot and limited buffer

Lot-streaming enables a discrete supply of components to multiple assembled products, while the limited buffer controls the fluency of the production flow. Thus, integrating differentiation processing with assembly, this work addresses an assembly hybrid differentiation flowshop scheduling with variable sub-lot and limited buffer (AHDFSP-VS-LB). Focusing on makespan minimization, a mixed-integer linear programming model is established and an asynchronous multi-agent based differential evolution (AMDE) is developed. Constrained by the limited buffer capacity, the AMDE incorporates a deadlock pre-detection strategy to quickly identify infeasible solutions during encoding and a dynamic adjustment strategy to ensure a high-quality feasible solution during decoding. Further, derived from processing-assembly coordination and buffer constraint, four problem-specific properties are extracted and embedded into the initialization and improvement strategy to optimize the performance of the algorithm. To achieve fast and sufficient convergence of the algorithm, an asynchronous multi-agent cooperative learning mechanism is designed to dynamically control the evolution power and direction for each individual by identifying the critical encoding layer and its suitable parameters at different search phases. Comprehensive experiments demonstrate that the designed operators excel in efficiency and coordination, and the proposed algorithm is superior to five state-of-the-art algorithms in solving this new problem.