Version 1
: Received: 14 August 2024 / Approved: 15 August 2024 / Online: 16 August 2024 (03:24:59 CEST)
How to cite:
Shao, Q. Q.; Miao, J. W.; Liao, P. H.; Liu, T. Dynamic Scheduling Optimization of Automatic Guide Vehicle for Terminal Delivery under Uncertain Conditions. Preprints2024, 2024081160. https://doi.org/10.20944/preprints202408.1160.v1
Shao, Q. Q.; Miao, J. W.; Liao, P. H.; Liu, T. Dynamic Scheduling Optimization of Automatic Guide Vehicle for Terminal Delivery under Uncertain Conditions. Preprints 2024, 2024081160. https://doi.org/10.20944/preprints202408.1160.v1
Shao, Q. Q.; Miao, J. W.; Liao, P. H.; Liu, T. Dynamic Scheduling Optimization of Automatic Guide Vehicle for Terminal Delivery under Uncertain Conditions. Preprints2024, 2024081160. https://doi.org/10.20944/preprints202408.1160.v1
APA Style
Shao, Q. Q., Miao, J. W., Liao, P. H., & Liu, T. (2024). Dynamic Scheduling Optimization of Automatic Guide Vehicle for Terminal Delivery under Uncertain Conditions. Preprints. https://doi.org/10.20944/preprints202408.1160.v1
Chicago/Turabian Style
Shao, Q. Q., Peng Hui Liao and Tao Liu. 2024 "Dynamic Scheduling Optimization of Automatic Guide Vehicle for Terminal Delivery under Uncertain Conditions" Preprints. https://doi.org/10.20944/preprints202408.1160.v1
Abstract
As an important part of urban terminal delivery, automated guided vehicle (AGV) has been widely used in the field of takeout delivery. Due to the real-time generation of takeout orders, the delivery system is required to be extremely dynamic, so the AGV needs to be dynamically scheduled. At the same time, the uncertainty in the delivery process (such as the meal preparation time) further increases the complexity and difficulty of AGV scheduling. Considering the influence of these two factors, the method of embedding stochastic programming model into rolling mechanism is adopted to optimize the AGV delivery routing. Specifically, To handle real-time orders under dynamic demand, an optimization mechanism based on rolling scheduling framework is proposed, which allows the AGV route to be continuously updated. Different from most VRP models, open chain structure is used to describe the dynamic delivery path of AGV. In order to deal with the impact of uncertain meal preparation time on route planning, a stochastic programming model is formulated with the purpose of minimizing the expectation of order timeout rate and the total customer waiting time. In addition, an effective path merging strategy and after-effects strategy are also considered in the model. In order to solve the proposed mathematical programming model, a multi-objective optimization algorithm based on NSGA-III framework is developed. Finally, a series of experimental results demonstrate the effectiveness and superiority of the proposed model and algorithm.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
