![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
ABSTRACT
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.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data instances that support the findings of this study are openly available in science data bank at https://www.scidb.cnen/s/7VNFVf, and data 10.11922/sciencedb.01412.
Additional information
Funding
Notes on contributors
Lei Liu
Lei Liu is currently a Ph.D. Student in Management Science and Engineering at the School of Management, Jinan University (Guangzhou, PR China). He received a Master degree in industrial engineering from Chongqing University of Post and Telecommunications (Chongqing, PR China) in 2019 and a Bachelor degree in Manufacturing and Automation from Northwest A&F University (Yangling, PR China) in 2014. Lei Liu worked as a big data R&D engineer from 2019 to 2020 at GREE Electric Appliances, a domestic appliance manufacturer and the world’s largest air conditioner producer. His research interests include data and knowledge-driven operation decision making and optimisation for complex industrial systems, intelligent algorithms, and intelligent manufacturing for production-logistics synchronisation, production workload control, modular manufacturing systems and digital twins.
Ting Qu
Ting Qu is a full professor at the School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai, PR China). He received his BEng and MPhil degrees from the School of Mechanical Engineering of Xi'an Jiaotong University (China) and his Ph.D. degree from the Department of Industrial and Manufacturing Systems Engineering of The University of Hong Kong. His research interests include IoT-based smart manufacturing systems, logistics and supply chain management, and industrial product/production service systems. He has undertaken over twenty research projects funded by government and industry and has published nearly 200 technical papers in these areas, half of which have appeared in reputable journals. He serves as director or board member for several academic associations in industrial engineering and smart manufacturing.
Matthias Thürer
Matthias Thürer is Distinguished Professor in Management Science and Engineering at Jinan University (Zhuhai, PR China). Before getting involved in academia, Matthias worked in several companies, did an apprenticeship and became a master craftsman (‘Meister’). He contributed to the improvement, simplification and integration of material flow control systems, and their integration with Industry 4.0. Apart from Operations Management, Matthias is also interested in social and philosophical issues including system theory, cybernetics, causality and the philosophy of science.
Lin Ma
Lin Ma is a Ph.D. Student in Management Science and Engineering at the School of Management, Jinan University (Guangzhou, PR China). He holds a Master from Xi'an University of Science and Technology (Xi'an, PR China). Production Bottleneck Management for high variety make-to-order shops is one of his main research interests. He is also interested in intelligent manufacturing, including digital twins and production-logistics synchronisation.
Zhongfei Zhang
Zhongfei Zhang is a Ph.D. candidate at the School of Management, Jinan University (Guangzhou, PR China). He received his BEng degrees and MPhil degrees in Mechanical Engineering from the School of Electromechanical Engineering of Zhengzhou University of Aeronautics (China) and Zhengzhou University of Light Industry (China) in 2014 and 2018, respectively. His research interests include cloud manufacturing systems, blockchain-enabled production logistics and intelligent synchronisation decision-making.
Mingze Yuan
Mingze Yuan is a Master Student in Management Science and Engineering at the School of Management, Jinan University (Guangzhou, PR China). Her main research interests include Workload Control, order release mechanisms for high-variety make-to-order shops and Industry 4.0.