A Dual-Space Artificial Bee Colony Algorithm Integrating Configuration-Coupled Heterogeneous Disjunctive Graph for Scheduling Problem in Reconfigurable Manufacturing Systems

Haoran Li, Qihao Liu, Zipeng Yang, Liang Gao, Xinyu Li

Abstract: Reconfigurable manufacturing systems (RMS) offer high flexibility, enabling efficient adaptation to changing market demands. However, this reconfigurability significantly increases the complexity of production scheduling. This article addresses the rms scheduling problem (RMSSP) to minimize the makespan. A configuration-coupled heterogeneous disjunctive graph (CHDG) model is proposed to represent feasible solutions by incorporating machine-configuration arcs and reconfiguration nodes, capturing reconfiguration processes and operation statuses. Feasibility theorems for intramachine and intermachine movements are developed, and six solution-space clipping strategies are introduced to reduce invalid searches. Based on these, a dual-space artificial bee colony (DABC) algorithm is proposed, featuring a novel operation-configuration encoding scheme and configuration-associated active-decoding strategy to maximize the potential of encoding. A hierarchical crossover operator and CHDG-based neighborhood search operators collaboratively explore the encoding and disjunctive graph (DG) spaces for efficient optimization. Numerical experiment results on 60 benchmark instances show that integrating CHDG significantly improves DABC’s performance in solving RMSSP. In addition, the six clipping theorems reduce invalid intramachine neighborhood searches by 55.3%.

External IDs:dblp:journals/tsmc/LiLYGL25