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A new knowledge-guided multi-objective optimisation for the multi-AGV dispatching problem in dynamic production environments

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journal contribution
posted on 23.09.2022, 07:20 authored by Lei Liu, Ting Qu, Matthias Thürer, Lin Ma, Zhongfei Zhang, Mingze Yuan

The efficiency of material supply for workstations using Automatic Guided Vehicles (AGVs) is largely determined by the performance of the AGV dispatching scheme. This paper proposes a new solution approach for the AGV dispatching problem (AGVDP) for material replenishment in a general manufacturing workshop where workstations are in a matrix layout, and where uncertainty in replenishment time of workstations and stochastic unloading efficiencies of AGVs are dynamic contextual factors. We first extend the literature proposing a mixed integer optimisation model with a delivery satisfaction soft constraint of material orders and two objectives: transportation costs and delivery time deviation. We then develop a new knowledge-guided estimation of distribution algorithm with delivery satisfaction evaluation for solving the model. Our algorithm fuses three knowledge-guided strategies to enhance optimisation capabilities at its respective execution stages. Comprehensive numerical experiments with instances built from a real-world scenario validate the proposed model and algorithm. Results demonstrate that the new algorithm outperforms three popular multi-objective evolutionary algorithms, a discrete version of a recent multi-objective particle swarm optimisation, and a multi-objective estimation of distribution algorithm. Findings of this work provide major implications for workshop management and algorithm design.


This work was supported by National Natural Science Foundation of China: [Grant Number 51875251,71872072,72150610504]; National Key Research and Development Program of China: [Grant Number 2021YFB3301701]; 2019 Guangdong Special Support Talent Program Innovation and Entrepreneurship Leading Team: [Grant Number 2019BT02S593].