The flexible job shop scheduling problem (FJSP) with transportation resources such as automated guided vehicles (AGVs) is prevalent in manufacturing enterprises. Multi-type AGVs are widely adopted to transfer jobs and realize the collaboration of different machines, but are often ignored in current research. Therefore, this paper addresses the FJSP with multi-type AGVs (FJSP-MTA). Considering the difficulties caused by the introduction of transportation and the NP-hard nature, the artificial bee colony (ABC) algorithm is adopted as a fundamental solution approach. Accordingly, a Q-learning hybrid multi-objective ABC (Q-HMOABC) algorithm is proposed to deal with the FJSP-MTA. First, to minimize both the makespan and total energy consumption (TEC), this paper proposes a novel mixed-integer linear programming (MILP) model. In Q-HMOABC, a three-layer encoding strategy based on operation sequence, machine assignment, and AGV dispatching with type selection is used. Moreover, during the employed bee phase, Q-learning is employed to update all individuals; during the onlooker bee phase, variable neighborhood search (VNS) is used to update nondominated solutions; and during the scout bee phase, a restart strategy is adopted. Experimental results demonstrate the effectiveness and superiority of Q-HMOABC.

This paper addresses an integrated production–transportation scheduling problem in assembly workshops, encompassing the processes of part machining, material handling via handling resources, and final synchronous assembly. The finite buffer capacities of production resources can cause blocking, thereby reducing efficiency. Material handling resources are subject to different service area restrictions, and some share safety zones with production resources, preventing simultaneous processing. To address this, a mixed-integer programming model is formulated with makespan and total empty travel time as bi-objective optimization targets. Since the mixed-integer linear programming (MILP) model faces difficulties in solving medium- and large-scale instances, an improved memetic NSGA-II algorithm (IMNSGA-II) is proposed. The algorithm adopts a three-segment chromosome encoding and incorporates a VNS-SA local search mechanism within the global evolutionary framework of NSGA-II. Small-scale computational experiments using Gurobi are first used to verify the correctness of the model. Decoupling experiments further demonstrate the necessity of integrated optimization: compared with phased baseline methods, IMNSGA-II reduces makespan and empty travel time by approximately 10.16% and 12.33%, respectively. In ablation and comparative experiments, results based on hypervolume (HV) and inverted generational distance (IGD) show that the proposed method achieves better convergence, diversity, and overall Pareto front quality than multiple baseline algorithms. These experiments confirm the effectiveness of the proposed model and algorithm.