A Multi-Objective Discrete Artificial Bee Colony Scheduling Algorithm with Referential Search Strategy

With the popularization of industrial intelligence, Automated Guided Vehicles (AGVs) have gradually become an efficient means of transportation in manufacturing workshops. Previous studies on this issue mainly considered the transportation cost of AGVs, while ignoring the optimization of customer satisfaction. This paper studies the AGV scheduling problem with time and capacity constraints for material handling in an intelligent manufacturing workshop. To better reflect real production conditions and simultaneously minimize AGV carbon emissions while maximizing customer satisfaction, a Mixed-Integer Linear Programming (MILP) model is developed. A Multi-objective Discrete Artificial Bee Colony algorithm (MDABC) is proposed, which employs an adaptive selection strategy to ensure that different neighborhoods of solutions are fully explored. The reference search strategy is introduced to carry out in-depth search according to the effective information carried by high quality solutions. In addition, in order to avoid the algorithm falling into local optimality, a high-quality generation strategy is proposed. Comprehensive comparisons with state-of-the-art algorithms and statistical analyses demonstrate that the proposed MDABC achieves superior performance.