Joint Scheduling Optimization of Machines and Automatic Guided Vehicles in Assembly Job Shop with Hybrid Artificial Bee Colony Algorithm

Le résumé fourni par la source

To achieve the joint scheduling optimization of machines and automatic guided vehicles (AGV) in the assembly job shop, a nonlinear constrained multi-objective model is established on workshop layout and the principle of assembly task, AGV task assignment and AGV route selection. The problem is formulated with three objectives of minimizing completion time, delivery deviation and energy consumption. Some constraints are added to the multi-objective model to control the energy cost of the scheduling scheme and to ensure that the delivery time is reasonable to maintain customer satisfaction. In this paper, a novel hybrid artificial bee colony (HABC) algorithm is proposed to solve the model. A new optimal initialisation operator designed in HABC can improve the quality of initial nectar sources by combining the Hiking Optimization Algorithm (HOA), which shows more adaptability and better search efficiency than the original one in the leading bee stage. The attack behaviour of the blackwinged kite algorithm (BKA) is introduced in the observation bee stage, which greatly enhances the global search capability of the algorithm and can find the optimal solutions in the complex solution space well. A experiment was studied with the practical needs and the actual data and the convergent multi-objective scheduling scheme was obtained. The numerical results demonstrate the joint scheduling problem of machines and AGV can be solved effectively by HABC.

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